prompt stringlengths 40 6.02k | answer stringlengths 0 3.5k | metadata stringlengths 241 19.8k | task stringclasses 26 values | level int64 0 2 | mode stringclasses 1 value |
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List: ['January 14, 2020', 'January 08, 2020', 'January 04, 2020', 'January 07, 2020', 'January 07, 2020', 'January 07, 2020', 'January 16, 2020', 'January 06, 2020', 'January 12, 2020', 'January 19, 2020']
How many times does 'January 07, 2020' appear? Only return the number. | 3 | {"elements": ["January 14, 2020", "January 08, 2020", "January 04, 2020", "January 07, 2020", "January 07, 2020", "January 07, 2020", "January 16, 2020", "January 06, 2020", "January 12, 2020", "January 19, 2020"], "target": "January 07, 2020", "_time": 0.0003197193145751953, "_task": "count_elements", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "max_count": 3, "list_size": 10, "domain_size": 20}, "_prompt_tokens": 112, "_cot_tokens": 1} | count_elements | 0 | instruct |
List: ['n', 'b', 'h', 'j', 'm', 'e', 's', 'i', 'p', 'f']
How many times does 'e' appear? Only return the number. | 1 | {"elements": ["n", "b", "h", "j", "m", "e", "s", "i", "p", "f"], "target": "e", "_time": 0.00022935867309570312, "_task": "count_elements", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "max_count": 3, "list_size": 10, "domain_size": 20}, "_prompt_tokens": 46, "_cot_tokens": 1} | count_elements | 0 | instruct |
List: ['2020-01-04', '2020-01-08', '2020-01-09', '2020-01-16', '2020-01-13', '2020-01-17', '2020-01-09', '2020-01-05', '2020-01-02', '2020-01-20']
How many times does '2020-01-02' appear? Only return the number. | 1 | {"elements": ["2020-01-04", "2020-01-08", "2020-01-09", "2020-01-16", "2020-01-13", "2020-01-17", "2020-01-09", "2020-01-05", "2020-01-02", "2020-01-20"], "target": "2020-01-02", "_time": 0.00024080276489257812, "_task": "count_elements", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "max_count": 3, "list_size": 10, "domain_size": 20}, "_prompt_tokens": 101, "_cot_tokens": 1} | count_elements | 0 | instruct |
List: ['d', 'k', 'o', 'm', 'd', 'o', 't', 'r', 'o', 'r']
How many times does 'e' appear? Only return the number. | 0 | {"elements": ["d", "k", "o", "m", "d", "o", "t", "r", "o", "r"], "target": "e", "_time": 0.00021719932556152344, "_task": "count_elements", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "max_count": 3, "list_size": 10, "domain_size": 20}, "_prompt_tokens": 46, "_cot_tokens": 1} | count_elements | 0 | instruct |
List: ['r', 'j', 'h', 'h', 'g', 'c', 'j', 'n', 'a', 'r']
How many times does 'o' appear? Only return the number. | 0 | {"elements": ["r", "j", "h", "h", "g", "c", "j", "n", "a", "r"], "target": "o", "_time": 0.00021338462829589844, "_task": "count_elements", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "max_count": 3, "list_size": 10, "domain_size": 20}, "_prompt_tokens": 46, "_cot_tokens": 1} | count_elements | 0 | instruct |
List: ['January 04, 2020', 'January 20, 2020', 'January 01, 2020', 'January 08, 2020', 'January 12, 2020', 'January 06, 2020', 'January 09, 2020', 'January 16, 2020', 'January 13, 2020', 'January 07, 2020']
How many times does 'January 03, 2020' appear? Only return the number. | 0 | {"elements": ["January 04, 2020", "January 20, 2020", "January 01, 2020", "January 08, 2020", "January 12, 2020", "January 06, 2020", "January 09, 2020", "January 16, 2020", "January 13, 2020", "January 07, 2020"], "target": "January 03, 2020", "_time": 0.00024962425231933594, "_task": "count_elements", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "max_count": 3, "list_size": 10, "domain_size": 20}, "_prompt_tokens": 112, "_cot_tokens": 1} | count_elements | 0 | instruct |
List: ['one', 'nineteen', 'eighteen', 'fifteen', 'sixteen', 'eighteen', 'twelve', 'fourteen', 'seven', 'eighteen']
How many times does 'eighteen' appear? Only return the number. | 3 | {"elements": ["one", "nineteen", "eighteen", "fifteen", "sixteen", "eighteen", "twelve", "fourteen", "seven", "eighteen"], "target": "eighteen", "_time": 0.0002589225769042969, "_task": "count_elements", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "max_count": 3, "list_size": 10, "domain_size": 20}, "_prompt_tokens": 61, "_cot_tokens": 1} | count_elements | 0 | instruct |
List: ['g', 'o', 'b', 'b', 'l', 'm', 's', 'r', 'p', 'q']
How many times does 'd' appear? Only return the number. | 0 | {"elements": ["g", "o", "b", "b", "l", "m", "s", "r", "p", "q"], "target": "d", "_time": 0.00021529197692871094, "_task": "count_elements", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "max_count": 3, "list_size": 10, "domain_size": 20}, "_prompt_tokens": 46, "_cot_tokens": 1} | count_elements | 0 | instruct |
List: ['sixteen', 'one', 'nineteen', 'four', 'eighteen', 'five', 'nineteen', 'three', 'thirteen', 'two']
How many times does 'four' appear? Only return the number. | 1 | {"elements": ["sixteen", "one", "nineteen", "four", "eighteen", "five", "nineteen", "three", "thirteen", "two"], "target": "four", "_time": 0.00023174285888671875, "_task": "count_elements", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "max_count": 3, "list_size": 10, "domain_size": 20}, "_prompt_tokens": 54, "_cot_tokens": 1} | count_elements | 0 | instruct |
List: ['2020-01-04', '2020-01-04', '2020-01-05', '2020-01-18', '2020-01-14', '2020-01-11', '2020-01-01', '2020-01-19', '2020-01-16', '2020-01-10']
How many times does '2020-01-19' appear? Only return the number. | 1 | {"elements": ["2020-01-04", "2020-01-04", "2020-01-05", "2020-01-18", "2020-01-14", "2020-01-11", "2020-01-01", "2020-01-19", "2020-01-16", "2020-01-10"], "target": "2020-01-19", "_time": 0.00023555755615234375, "_task": "count_elements", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "max_count": 3, "list_size": 10, "domain_size": 20}, "_prompt_tokens": 101, "_cot_tokens": 1} | count_elements | 0 | instruct |
List: ['2020-01-12', '2020-01-09', '2020-01-01', '2020-01-18', '2020-01-15', '2020-01-16', '2020-01-12', '2020-01-04', '2020-01-17', '2020-01-05']
How many times does '2020-01-03' appear? Only return the number. | 0 | {"elements": ["2020-01-12", "2020-01-09", "2020-01-01", "2020-01-18", "2020-01-15", "2020-01-16", "2020-01-12", "2020-01-04", "2020-01-17", "2020-01-05"], "target": "2020-01-03", "_time": 0.0002346038818359375, "_task": "count_elements", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "max_count": 3, "list_size": 10, "domain_size": 20}, "_prompt_tokens": 101, "_cot_tokens": 1} | count_elements | 0 | instruct |
List: ['January 07, 2020', 'January 14, 2020', 'January 12, 2020', 'January 15, 2020', 'January 18, 2020', 'January 08, 2020', 'January 12, 2020', 'January 03, 2020', 'January 01, 2020', 'January 08, 2020']
How many times does 'January 20, 2020' appear? Only return the number. | 0 | {"elements": ["January 07, 2020", "January 14, 2020", "January 12, 2020", "January 15, 2020", "January 18, 2020", "January 08, 2020", "January 12, 2020", "January 03, 2020", "January 01, 2020", "January 08, 2020"], "target": "January 20, 2020", "_time": 0.0002503395080566406, "_task": "count_elements", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "max_count": 3, "list_size": 10, "domain_size": 20}, "_prompt_tokens": 112, "_cot_tokens": 1} | count_elements | 0 | instruct |
List: [1, 14, 2, 8, 20, 17, 5, 14, 2, 2]
How many times does 14 appear? Only return the number. | 2 | {"elements": [1, 14, 2, 8, 20, 17, 5, 14, 2, 2], "target": 14, "_time": 0.00021505355834960938, "_task": "count_elements", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "max_count": 3, "list_size": 10, "domain_size": 20}, "_prompt_tokens": 45, "_cot_tokens": 1} | count_elements | 0 | instruct |
List: ['t', 'c', 't', 'm', 'g', 'p', 'c', 'c', 'j', 't']
How many times does 't' appear? Only return the number. | 3 | {"elements": ["t", "c", "t", "m", "g", "p", "c", "c", "j", "t"], "target": "t", "_time": 0.0002148151397705078, "_task": "count_elements", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "max_count": 3, "list_size": 10, "domain_size": 20}, "_prompt_tokens": 46, "_cot_tokens": 1} | count_elements | 0 | instruct |
List: [8, 17, 12, 12, 4, 5, 11, 14, 12, 2]
How many times does 11 appear? Only return the number. | 1 | {"elements": [8, 17, 12, 12, 4, 5, 11, 14, 12, 2], "target": 11, "_time": 0.00021529197692871094, "_task": "count_elements", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "max_count": 3, "list_size": 10, "domain_size": 20}, "_prompt_tokens": 45, "_cot_tokens": 1} | count_elements | 0 | instruct |
List: ['eighteen', 'nine', 'twelve', 'sixteen', 'nine', 'eleven', 'nine', 'twenty', 'fourteen', 'eleven']
How many times does 'nine' appear? Only return the number. | 3 | {"elements": ["eighteen", "nine", "twelve", "sixteen", "nine", "eleven", "nine", "twenty", "fourteen", "eleven"], "target": "nine", "_time": 0.00022983551025390625, "_task": "count_elements", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "max_count": 3, "list_size": 10, "domain_size": 20}, "_prompt_tokens": 54, "_cot_tokens": 1} | count_elements | 0 | instruct |
Consider the following social network graph:
Edges: 0-1, 0-2, 0-3, 0-4, 1-4, 2-4
Based on the number of connections, identify all nodes that are the most central (i.e., have the highest degree centrality). There may be more than one.
Your answer must be a Python list of node integers, sorted in increasing order. Example: `[3, 8]`. | [0] | {"graph_description": "Edges: 0-1, 0-2, 0-3, 0-4, 1-4, 2-4", "_time": 0.001306295394897461, "_task": "graph_node_centrality", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_nodes": 5}, "_prompt_tokens": 97, "_cot_tokens": 3} | graph_node_centrality | 0 | instruct |
Consider the following social network graph:
Node 0 is connected to: 1.
Node 1 is connected to: 0, 2, 4.
Node 2 is connected to: 1, 3.
Node 3 is connected to: 2, 4.
Node 4 is connected to: 1, 3.
Based on the number of connections, identify all nodes that are the most central (i.e., have the highest degree centrality). There may be more than one.
Your answer must be a Python list of node integers, sorted in increasing order. Example: `[3, 8]`. | [1] | {"graph_description": "Node 0 is connected to: 1.\nNode 1 is connected to: 0, 2, 4.\nNode 2 is connected to: 1, 3.\nNode 3 is connected to: 2, 4.\nNode 4 is connected to: 1, 3.", "_time": 0.0008115768432617188, "_task": "graph_node_centrality", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_nodes": 5}, "_prompt_tokens": 130, "_cot_tokens": 3} | graph_node_centrality | 0 | instruct |
Consider the following social network graph:
Node (0, 0) is connected to: (0, 1), (1, 0).
Node (0, 1) is connected to: (0, 0), (0, 2), (1, 1).
Node (0, 2) is connected to: (0, 1), (1, 2).
Node (1, 0) is connected to: (0, 0), (1, 1), (2, 0).
Node (1, 1) is connected to: (0, 1), (1, 0), (1, 2), (2, 1).
Node (1, 2) is connected to: (0, 2), (1, 1), (2, 2).
Node (2, 0) is connected to: (1, 0), (2, 1), (3, 0).
Node (2, 1) is connected to: (1, 1), (2, 0), (2, 2), (3, 1).
Node (2, 2) is connected to: (1, 2), (2, 1), (3, 2).
Node (3, 0) is connected to: (2, 0), (3, 1), (4, 0).
Node (3, 1) is connected to: (2, 1), (3, 0), (3, 2), (4, 1).
Node (3, 2) is connected to: (2, 2), (3, 1), (4, 2).
Node (4, 0) is connected to: (3, 0), (4, 1).
Node (4, 1) is connected to: (3, 1), (4, 0), (4, 2).
Node (4, 2) is connected to: (3, 2), (4, 1).
Based on the number of connections, identify all nodes that are the most central (i.e., have the highest degree centrality). There may be more than one.
Your answer must be a Python list of node integers, sorted in increasing order. Example: `[3, 8]`. | [(1, 1), (2, 1), (3, 1)] | {"graph_description": "Node (0, 0) is connected to: (0, 1), (1, 0).\nNode (0, 1) is connected to: (0, 0), (0, 2), (1, 1).\nNode (0, 2) is connected to: (0, 1), (1, 2).\nNode (1, 0) is connected to: (0, 0), (1, 1), (2, 0).\nNode (1, 1) is connected to: (0, 1), (1, 0), (1, 2), (2, 1).\nNode (1, 2) is connected to: (0, 2), (1, 1), (2, 2).\nNode (2, 0) is connected to: (1, 0), (2, 1), (3, 0).\nNode (2, 1) is connected to: (1, 1), (2, 0), (2, 2), (3, 1).\nNode (2, 2) is connected to: (1, 2), (2, 1), (3, 2).\nNode (3, 0) is connected to: (2, 0), (3, 1), (4, 0).\nNode (3, 1) is connected to: (2, 1), (3, 0), (3, 2), (4, 1).\nNode (3, 2) is connected to: (2, 2), (3, 1), (4, 2).\nNode (4, 0) is connected to: (3, 0), (4, 1).\nNode (4, 1) is connected to: (3, 1), (4, 0), (4, 2).\nNode (4, 2) is connected to: (3, 2), (4, 1).", "_time": 0.000858306884765625, "_task": "graph_node_centrality", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_nodes": 5}, "_prompt_tokens": 494, "_cot_tokens": 18} | graph_node_centrality | 0 | instruct |
Consider the following social network graph:
0: 0-1 0-2; 1: 1-0 1-2; 2: 2-0 2-1 2-4; 3: 3-4; 4: 4-2 4-3
Based on the number of connections, identify all nodes that are the most central (i.e., have the highest degree centrality). There may be more than one.
Your answer must be a Python list of node integers, sorted in increasing order. Example: `[3, 8]`. | [2] | {"graph_description": "0: 0-1 0-2; 1: 1-0 1-2; 2: 2-0 2-1 2-4; 3: 3-4; 4: 4-2 4-3", "_time": 0.0002734661102294922, "_task": "graph_node_centrality", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_nodes": 5}, "_prompt_tokens": 124, "_cot_tokens": 3} | graph_node_centrality | 0 | instruct |
Consider the following social network graph:
graph { (0, 0)--(0, 1); (0, 0)--(1, 0); (0, 1)--(0, 2); (0, 1)--(1, 1); (0, 2)--(1, 2); (1, 0)--(1, 1); (1, 0)--(2, 0); (1, 1)--(1, 2); (1, 1)--(2, 1); (1, 2)--(2, 2); (2, 0)--(2, 1); (2, 0)--(3, 0); (2, 1)--(2, 2); (2, 1)--(3, 1); (2, 2)--(3, 2); (3, 0)--(3, 1); (3, 0)--(4, 0); (3, 1)--(3, 2); (3, 1)--(4, 1); (3, 2)--(4, 2); (4, 0)--(4, 1); (4, 1)--(4, 2) }
Based on the number of connections, identify all nodes that are the most central (i.e., have the highest degree centrality). There may be more than one.
Your answer must be a Python list of node integers, sorted in increasing order. Example: `[3, 8]`. | [(1, 1), (2, 1), (3, 1)] | {"graph_description": "graph { (0, 0)--(0, 1); (0, 0)--(1, 0); (0, 1)--(0, 2); (0, 1)--(1, 1); (0, 2)--(1, 2); (1, 0)--(1, 1); (1, 0)--(2, 0); (1, 1)--(1, 2); (1, 1)--(2, 1); (1, 2)--(2, 2); (2, 0)--(2, 1); (2, 0)--(3, 0); (2, 1)--(2, 2); (2, 1)--(3, 1); (2, 2)--(3, 2); (3, 0)--(3, 1); (3, 0)--(4, 0); (3, 1)--(3, 2); (3, 1)--(4, 1); (3, 2)--(4, 2); (4, 0)--(4, 1); (4, 1)--(4, 2) }", "_time": 0.00039005279541015625, "_task": "graph_node_centrality", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_nodes": 5}, "_prompt_tokens": 332, "_cot_tokens": 18} | graph_node_centrality | 0 | instruct |
Consider the following social network graph:
graph { 0--1; 1--2; 2--3; 3--4 }
Based on the number of connections, identify all nodes that are the most central (i.e., have the highest degree centrality). There may be more than one.
Your answer must be a Python list of node integers, sorted in increasing order. Example: `[3, 8]`. | [1, 2, 3] | {"graph_description": "graph { 0--1; 1--2; 2--3; 3--4 }", "_time": 0.00025153160095214844, "_task": "graph_node_centrality", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_nodes": 5}, "_prompt_tokens": 87, "_cot_tokens": 9} | graph_node_centrality | 0 | instruct |
Consider the following social network graph:
Node 0 connects to 1, 2, 3, 4. Node 1 connects to 0, 4. Node 2 connects to 0. Node 3 connects to 0, 4. Node 4 connects to 0, 1, 3.
Based on the number of connections, identify all nodes that are the most central (i.e., have the highest degree centrality). There may be more than one.
Your answer must be a Python list of node integers, sorted in increasing order. Example: `[3, 8]`. | [0] | {"graph_description": "Node 0 connects to 1, 2, 3, 4. Node 1 connects to 0, 4. Node 2 connects to 0. Node 3 connects to 0, 4. Node 4 connects to 0, 1, 3.", "_time": 0.00023889541625976562, "_task": "graph_node_centrality", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_nodes": 5}, "_prompt_tokens": 126, "_cot_tokens": 3} | graph_node_centrality | 0 | instruct |
Consider the following social network graph:
{0: [1, 2, 3], 1: [0, 3, 4], 2: [0, 4], 3: [0, 1], 4: [1, 2]}
Based on the number of connections, identify all nodes that are the most central (i.e., have the highest degree centrality). There may be more than one.
Your answer must be a Python list of node integers, sorted in increasing order. Example: `[3, 8]`. | [0, 1] | {"graph_description": "{0: [1, 2, 3], 1: [0, 3, 4], 2: [0, 4], 3: [0, 1], 4: [1, 2]}", "_time": 0.00022339820861816406, "_task": "graph_node_centrality", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_nodes": 5}, "_prompt_tokens": 117, "_cot_tokens": 6} | graph_node_centrality | 0 | instruct |
Consider the following social network graph:
0: 0-1 0-4; 1: 1-0 1-2 1-3; 2: 2-1 2-3; 3: 3-1 3-2; 4: 4-0
Based on the number of connections, identify all nodes that are the most central (i.e., have the highest degree centrality). There may be more than one.
Your answer must be a Python list of node integers, sorted in increasing order. Example: `[3, 8]`. | [1] | {"graph_description": "0: 0-1 0-4; 1: 1-0 1-2 1-3; 2: 2-1 2-3; 3: 3-1 3-2; 4: 4-0", "_time": 0.00021886825561523438, "_task": "graph_node_centrality", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_nodes": 5}, "_prompt_tokens": 124, "_cot_tokens": 3} | graph_node_centrality | 0 | instruct |
Consider the following social network graph:
Nodes: [0, 1, 2, 3, 4]
Matrix:
[0, 1, 1, 0, 0]
[1, 0, 0, 1, 0]
[1, 0, 0, 0, 1]
[0, 1, 0, 0, 1]
[0, 0, 1, 1, 0]
Based on the number of connections, identify all nodes that are the most central (i.e., have the highest degree centrality). There may be more than one.
Your answer must be a Python list of node integers, sorted in increasing order. Example: `[3, 8]`. | [0, 1, 2, 3, 4] | {"graph_description": "Nodes: [0, 1, 2, 3, 4]\nMatrix:\n[0, 1, 1, 0, 0]\n[1, 0, 0, 1, 0]\n[1, 0, 0, 0, 1]\n[0, 1, 0, 0, 1]\n[0, 0, 1, 1, 0]", "_time": 0.0002732276916503906, "_task": "graph_node_centrality", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_nodes": 5}, "_prompt_tokens": 159, "_cot_tokens": 15} | graph_node_centrality | 0 | instruct |
Consider the following social network graph:
Node 0 is connected to: 2, 3.
Node 1 is connected to: 2, 4.
Node 2 is connected to: 0, 1.
Node 3 is connected to: 0, 4.
Node 4 is connected to: 1, 3.
Based on the number of connections, identify all nodes that are the most central (i.e., have the highest degree centrality). There may be more than one.
Your answer must be a Python list of node integers, sorted in increasing order. Example: `[3, 8]`. | [0, 1, 2, 3, 4] | {"graph_description": "Node 0 is connected to: 2, 3.\nNode 1 is connected to: 2, 4.\nNode 2 is connected to: 0, 1.\nNode 3 is connected to: 0, 4.\nNode 4 is connected to: 1, 3.", "_time": 0.0002663135528564453, "_task": "graph_node_centrality", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_nodes": 5}, "_prompt_tokens": 130, "_cot_tokens": 15} | graph_node_centrality | 0 | instruct |
Consider the following social network graph:
0: 0-1 0-4; 1: 1-0 1-2; 2: 2-1 2-3; 3: 3-2 3-4; 4: 4-0 4-3
Based on the number of connections, identify all nodes that are the most central (i.e., have the highest degree centrality). There may be more than one.
Your answer must be a Python list of node integers, sorted in increasing order. Example: `[3, 8]`. | [0, 1, 2, 3, 4] | {"graph_description": "0: 0-1 0-4; 1: 1-0 1-2; 2: 2-1 2-3; 3: 3-2 3-4; 4: 4-0 4-3", "_time": 0.0002467632293701172, "_task": "graph_node_centrality", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_nodes": 5}, "_prompt_tokens": 124, "_cot_tokens": 15} | graph_node_centrality | 0 | instruct |
Consider the following social network graph:
Node (0, 0) connects to (0, 1), (1, 0). Node (0, 1) connects to (0, 0), (0, 2), (1, 1). Node (0, 2) connects to (0, 1), (1, 2). Node (1, 0) connects to (0, 0), (1, 1), (2, 0). Node (1, 1) connects to (0, 1), (1, 0), (1, 2), (2, 1). Node (1, 2) connects to (0, 2), (1, 1), (2, 2). Node (2, 0) connects to (1, 0), (2, 1). Node (2, 1) connects to (1, 1), (2, 0), (2, 2). Node (2, 2) connects to (1, 2), (2, 1).
Based on the number of connections, identify all nodes that are the most central (i.e., have the highest degree centrality). There may be more than one.
Your answer must be a Python list of node integers, sorted in increasing order. Example: `[3, 8]`. | [(1, 1)] | {"graph_description": "Node (0, 0) connects to (0, 1), (1, 0). Node (0, 1) connects to (0, 0), (0, 2), (1, 1). Node (0, 2) connects to (0, 1), (1, 2). Node (1, 0) connects to (0, 0), (1, 1), (2, 0). Node (1, 1) connects to (0, 1), (1, 0), (1, 2), (2, 1). Node (1, 2) connects to (0, 2), (1, 1), (2, 2). Node (2, 0) connects to (1, 0), (2, 1). Node (2, 1) connects to (1, 1), (2, 0), (2, 2). Node (2, 2) connects to (1, 2), (2, 1).", "_time": 0.00038051605224609375, "_task": "graph_node_centrality", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_nodes": 5}, "_prompt_tokens": 290, "_cot_tokens": 6} | graph_node_centrality | 0 | instruct |
Consider the following social network graph:
{0: [1], 1: [0, 2], 2: [1, 3, 4], 3: [2, 4], 4: [2, 3]}
Based on the number of connections, identify all nodes that are the most central (i.e., have the highest degree centrality). There may be more than one.
Your answer must be a Python list of node integers, sorted in increasing order. Example: `[3, 8]`. | [2] | {"graph_description": "{0: [1], 1: [0, 2], 2: [1, 3, 4], 3: [2, 4], 4: [2, 3]}", "_time": 0.00023436546325683594, "_task": "graph_node_centrality", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_nodes": 5}, "_prompt_tokens": 111, "_cot_tokens": 3} | graph_node_centrality | 0 | instruct |
Consider the following social network graph:
Node 0 connects to 1, 2, 3, 4. Node 1 connects to 0, 2, 3, 4. Node 2 connects to 0, 1, 3, 4. Node 3 connects to 0, 1, 2, 4. Node 4 connects to 0, 1, 2, 3.
Based on the number of connections, identify all nodes that are the most central (i.e., have the highest degree centrality). There may be more than one.
Your answer must be a Python list of node integers, sorted in increasing order. Example: `[3, 8]`. | [0, 1, 2, 3, 4] | {"graph_description": "Node 0 connects to 1, 2, 3, 4. Node 1 connects to 0, 2, 3, 4. Node 2 connects to 0, 1, 3, 4. Node 3 connects to 0, 1, 2, 4. Node 4 connects to 0, 1, 2, 3.", "_time": 0.000247955322265625, "_task": "graph_node_centrality", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_nodes": 5}, "_prompt_tokens": 150, "_cot_tokens": 15} | graph_node_centrality | 0 | instruct |
Consider the following social network graph:
Nodes [0, 1, 2, 3, 4] and edges: (0, 1), (0, 3), (0, 4), (1, 2), (2, 3).
Based on the number of connections, identify all nodes that are the most central (i.e., have the highest degree centrality). There may be more than one.
Your answer must be a Python list of node integers, sorted in increasing order. Example: `[3, 8]`. | [0] | {"graph_description": "Nodes [0, 1, 2, 3, 4] and edges: (0, 1), (0, 3), (0, 4), (1, 2), (2, 3).", "_time": 0.00022363662719726562, "_task": "graph_node_centrality", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_nodes": 5}, "_prompt_tokens": 114, "_cot_tokens": 3} | graph_node_centrality | 0 | instruct |
Execute this SQL query on the table:
- city: East Tony
customer: Amy Dudley
job: Chief Operating Officer
rating: 2.6
- city: Port George
customer: Christine Hernandez
job: Production assistant, radio
rating: 2.5
- city: Elizabethhaven
customer: Tyler Medina
job: Science writer
rating: 4.8
- city: Mitchellstad
customer: Brandi Curry
job: Scientist, product/process development
rating: 4.1
- city: Port Markburgh
customer: Harold Harris
job: Consulting civil engineer
rating: 2.6
- city: South Williamburgh
customer: George Owens
job: Chartered loss adjuster
rating: 3.4
- city: Lake Catherinestad
customer: Phillip Lee
job: Site engineer
rating: 2.5
- city: Allisontown
customer: Phillip Harper
job: Health visitor
rating: 3.0
- city: South Louischester
customer: Bryan Vargas
job: Restaurant manager, fast food
rating: 4.6
- city: Brendahaven
customer: Brian Nelson
job: IT technical support officer
rating: 2.6
- city: Lake Robertoland
customer: Rachael Walls
job: Broadcast journalist
rating: 2.3
- city: West Elizabethfurt
customer: Andrew Morris
job: Chief of Staff
rating: 3.4
- city: East Dana
customer: Patricia Mendoza
job: Television/film/video producer
rating: 4.9
- city: Port Calvin
customer: Alicia Henderson
job: Statistician
rating: 1.5
- city: Lake Jessicamouth
customer: Ms. Lindsey Edwards PhD
job: Editor, film/video
rating: 1.7
- city: Carlsonton
customer: Peter Murillo
job: Public house manager
rating: 4.8
- city: Woodberg
customer: Peter Carter
job: Teacher, early years/pre
rating: 4.2
- city: Port Alyssaland
customer: Connie Maynard PhD
job: Surveyor, rural practice
rating: 1.6
- city: West Kimberly
customer: Calvin Wright
job: Chief Marketing Officer
rating: 4.2
- city: New Carmen
customer: Lauren Johnson
job: Dealer
rating: 1.4
SQL: SELECT ROUND(SUM(rating), 2) FROM dataframe
Return result as single value. | 62.7 | {"table": "- city: East Tony\n customer: Amy Dudley\n job: Chief Operating Officer\n rating: 2.6\n- city: Port George\n customer: Christine Hernandez\n job: Production assistant, radio\n rating: 2.5\n- city: Elizabethhaven\n customer: Tyler Medina\n job: Science writer\n rating: 4.8\n- city: Mitchellstad\n customer: Brandi Curry\n job: Scientist, product/process development\n rating: 4.1\n- city: Port Markburgh\n customer: Harold Harris\n job: Consulting civil engineer\n rating: 2.6\n- city: South Williamburgh\n customer: George Owens\n job: Chartered loss adjuster\n rating: 3.4\n- city: Lake Catherinestad\n customer: Phillip Lee\n job: Site engineer\n rating: 2.5\n- city: Allisontown\n customer: Phillip Harper\n job: Health visitor\n rating: 3.0\n- city: South Louischester\n customer: Bryan Vargas\n job: Restaurant manager, fast food\n rating: 4.6\n- city: Brendahaven\n customer: Brian Nelson\n job: IT technical support officer\n rating: 2.6\n- city: Lake Robertoland\n customer: Rachael Walls\n job: Broadcast journalist\n rating: 2.3\n- city: West Elizabethfurt\n customer: Andrew Morris\n job: Chief of Staff\n rating: 3.4\n- city: East Dana\n customer: Patricia Mendoza\n job: Television/film/video producer\n rating: 4.9\n- city: Port Calvin\n customer: Alicia Henderson\n job: Statistician\n rating: 1.5\n- city: Lake Jessicamouth\n customer: Ms. Lindsey Edwards PhD\n job: Editor, film/video\n rating: 1.7\n- city: Carlsonton\n customer: Peter Murillo\n job: Public house manager\n rating: 4.8\n- city: Woodberg\n customer: Peter Carter\n job: Teacher, early years/pre\n rating: 4.2\n- city: Port Alyssaland\n customer: Connie Maynard PhD\n job: Surveyor, rural practice\n rating: 1.6\n- city: West Kimberly\n customer: Calvin Wright\n job: Chief Marketing Officer\n rating: 4.2\n- city: New Carmen\n customer: Lauren Johnson\n job: Dealer\n rating: 1.4\n", "query": "SELECT ROUND(SUM(rating), 2) FROM dataframe", "is_scalar": true, "table_format": "to_yaml", "_time": 0.07079577445983887, "_task": "table_qa", "_level": 2, "_config": {"c": 1.0, "level": 2, "seed": null, "size": null, "num_rows": 20, "num_columns": 4}, "_prompt_tokens": 608, "_cot_tokens": 3} | table_qa | 2 | instruct |
Execute this SQL query on the table:
price,revenue,job,qty
41.66,341.37,Clothing/textile technologist,574
120.94,306.09,Prison officer,378
465.36,569.18,Armed forces operational officer,856
480.16,33.48,"Therapist, drama",558
123.08,711.33,Medical illustrator,100
450.33,654.32,Press photographer,150
123.25,859.88,Chartered management accountant,853
5.53,279.09,Freight forwarder,872
485.83,720.54,"Engineer, petroleum",898
252.72,386.98,"Psychologist, sport and exercise",309
188.14,738.93,Health and safety adviser,998
100.65,264.28,Probation officer,312
333.39,612.93,"Librarian, academic",866
269.15,866.8,Multimedia programmer,274
144.2,275.09,Hospital doctor,31
118.5,588.94,Quality manager,963
231.15,361.4,Advertising account executive,486
221.83,907.46,"Engineer, structural",117
51.32,139.57,Pathologist,205
341.08,955.92,International aid/development worker,749
SQL: SELECT COUNT(*) FROM dataframe WHERE revenue > 714.093
Return result as single value. | 6 | {"table": "price,revenue,job,qty\n41.66,341.37,Clothing/textile technologist,574\n120.94,306.09,Prison officer,378\n465.36,569.18,Armed forces operational officer,856\n480.16,33.48,\"Therapist, drama\",558\n123.08,711.33,Medical illustrator,100\n450.33,654.32,Press photographer,150\n123.25,859.88,Chartered management accountant,853\n5.53,279.09,Freight forwarder,872\n485.83,720.54,\"Engineer, petroleum\",898\n252.72,386.98,\"Psychologist, sport and exercise\",309\n188.14,738.93,Health and safety adviser,998\n100.65,264.28,Probation officer,312\n333.39,612.93,\"Librarian, academic\",866\n269.15,866.8,Multimedia programmer,274\n144.2,275.09,Hospital doctor,31\n118.5,588.94,Quality manager,963\n231.15,361.4,Advertising account executive,486\n221.83,907.46,\"Engineer, structural\",117\n51.32,139.57,Pathologist,205\n341.08,955.92,International aid/development worker,749\n", "query": "SELECT COUNT(*) FROM dataframe WHERE revenue > 714.093", "is_scalar": true, "table_format": "to_csv", "_time": 0.03538703918457031, "_task": "table_qa", "_level": 2, "_config": {"c": 1.0, "level": 2, "seed": null, "size": null, "num_rows": 20, "num_columns": 4}, "_prompt_tokens": 329, "_cot_tokens": 1} | table_qa | 2 | instruct |
Execute this SQL query on the table:
[
{
"customer":"Lindsey Holmes",
"email":"[email protected]",
"job":"Exercise physiologist",
"price":133.77
},
{
"customer":"Seth Williams",
"email":"[email protected]",
"job":"Web designer",
"price":77.69
},
{
"customer":"Stephanie Wright",
"email":"[email protected]",
"job":"Network engineer",
"price":464.34
},
{
"customer":"James Davis",
"email":"[email protected]",
"job":"Surveyor, hydrographic",
"price":137.78
},
{
"customer":"Alexis Roth",
"email":"[email protected]",
"job":"Sound technician, broadcasting\/film\/video",
"price":227.03
},
{
"customer":"Fred Hines",
"email":"[email protected]",
"job":"Curator",
"price":312.45
},
{
"customer":"Christopher Reynolds",
"email":"[email protected]",
"job":"Further education lecturer",
"price":307.66
},
{
"customer":"Sarah Morris",
"email":"[email protected]",
"job":"Structural engineer",
"price":126.33
},
{
"customer":"Jennifer Clark",
"email":"[email protected]",
"job":"Teacher, English as a foreign language",
"price":46.23
},
{
"customer":"Jonathan Turner",
"email":"[email protected]",
"job":"Audiological scientist",
"price":169.17
},
{
"customer":"Kenneth Adams",
"email":"[email protected]",
"job":"Information systems manager",
"price":404.65
},
{
"customer":"Courtney Davis",
"email":"[email protected]",
"job":"Dramatherapist",
"price":255.01
},
{
"customer":"Haley Martinez",
"email":"[email protected]",
"job":"Surveyor, hydrographic",
"price":222.87
},
{
"customer":"Ronald Wong",
"email":"[email protected]",
"job":"Control and instrumentation engineer",
"price":421.81
},
{
"customer":"Rodney Bernard",
"email":"[email protected]",
"job":"Engineer, energy",
"price":396.93
},
{
"customer":"Zachary Garrett",
"email":"[email protected]",
"job":"Surveyor, quantity",
"price":452.27
},
{
"customer":"Molly Russell",
"email":"[email protected]",
"job":"Barista",
"price":88.09
},
{
"customer":"Tyler Adams",
"email":"[email protected]",
"job":"Metallurgist",
"price":460.75
},
{
"customer":"Amber Le",
"email":"[email protected]",
"job":"Occupational psychologist",
"price":428.18
},
{
"customer":"Richard Reed",
"email":"[email protected]",
"job":"Computer games developer",
"price":39.81
}
]
SQL: SELECT customer, SUM(price) as v FROM dataframe GROUP BY customer ORDER BY v DESC LIMIT 2
Return result as CSV format (rows separated by newlines, values by commas). | Stephanie Wright,464.34
Tyler Adams,460.75 | {"table": "[\n {\n \"customer\":\"Lindsey Holmes\",\n \"email\":\"[email protected]\",\n \"job\":\"Exercise physiologist\",\n \"price\":133.77\n },\n {\n \"customer\":\"Seth Williams\",\n \"email\":\"[email protected]\",\n \"job\":\"Web designer\",\n \"price\":77.69\n },\n {\n \"customer\":\"Stephanie Wright\",\n \"email\":\"[email protected]\",\n \"job\":\"Network engineer\",\n \"price\":464.34\n },\n {\n \"customer\":\"James Davis\",\n \"email\":\"[email protected]\",\n \"job\":\"Surveyor, hydrographic\",\n \"price\":137.78\n },\n {\n \"customer\":\"Alexis Roth\",\n \"email\":\"[email protected]\",\n \"job\":\"Sound technician, broadcasting\\/film\\/video\",\n \"price\":227.03\n },\n {\n \"customer\":\"Fred Hines\",\n \"email\":\"[email protected]\",\n \"job\":\"Curator\",\n \"price\":312.45\n },\n {\n \"customer\":\"Christopher Reynolds\",\n \"email\":\"[email protected]\",\n \"job\":\"Further education lecturer\",\n \"price\":307.66\n },\n {\n \"customer\":\"Sarah Morris\",\n \"email\":\"[email protected]\",\n \"job\":\"Structural engineer\",\n \"price\":126.33\n },\n {\n \"customer\":\"Jennifer Clark\",\n \"email\":\"[email protected]\",\n \"job\":\"Teacher, English as a foreign language\",\n \"price\":46.23\n },\n {\n \"customer\":\"Jonathan Turner\",\n \"email\":\"[email protected]\",\n \"job\":\"Audiological scientist\",\n \"price\":169.17\n },\n {\n \"customer\":\"Kenneth Adams\",\n \"email\":\"[email protected]\",\n \"job\":\"Information systems manager\",\n \"price\":404.65\n },\n {\n \"customer\":\"Courtney Davis\",\n \"email\":\"[email protected]\",\n \"job\":\"Dramatherapist\",\n \"price\":255.01\n },\n {\n \"customer\":\"Haley Martinez\",\n \"email\":\"[email protected]\",\n \"job\":\"Surveyor, hydrographic\",\n \"price\":222.87\n },\n {\n \"customer\":\"Ronald Wong\",\n \"email\":\"[email protected]\",\n \"job\":\"Control and instrumentation engineer\",\n \"price\":421.81\n },\n {\n \"customer\":\"Rodney Bernard\",\n \"email\":\"[email protected]\",\n \"job\":\"Engineer, energy\",\n \"price\":396.93\n },\n {\n \"customer\":\"Zachary Garrett\",\n \"email\":\"[email protected]\",\n \"job\":\"Surveyor, quantity\",\n \"price\":452.27\n },\n {\n \"customer\":\"Molly Russell\",\n \"email\":\"[email protected]\",\n \"job\":\"Barista\",\n \"price\":88.09\n },\n {\n \"customer\":\"Tyler Adams\",\n \"email\":\"[email protected]\",\n \"job\":\"Metallurgist\",\n \"price\":460.75\n },\n {\n \"customer\":\"Amber Le\",\n \"email\":\"[email protected]\",\n \"job\":\"Occupational psychologist\",\n \"price\":428.18\n },\n {\n \"customer\":\"Richard Reed\",\n \"email\":\"[email protected]\",\n \"job\":\"Computer games developer\",\n \"price\":39.81\n }\n]", "query": "SELECT customer, SUM(price) as v FROM dataframe GROUP BY customer ORDER BY v DESC LIMIT 2", "is_scalar": false, "table_format": "to_json", "_time": 0.0443568229675293, "_task": "table_qa", "_level": 2, "_config": {"c": 1.0, "level": 2, "seed": null, "size": null, "num_rows": 20, "num_columns": 4}, "_prompt_tokens": 829, "_cot_tokens": 14} | table_qa | 2 | instruct |
Execute this SQL query on the table:
[
{
"revenue":524.52,
"rating":1.8,
"date":"2025-03-10T00:00:00.000",
"email":"[email protected]"
},
{
"revenue":501.52,
"rating":3.7,
"date":"2026-01-13T00:00:00.000",
"email":"[email protected]"
},
{
"revenue":677.62,
"rating":1.4,
"date":"2025-11-04T00:00:00.000",
"email":"[email protected]"
},
{
"revenue":153.43,
"rating":1.7,
"date":"2025-05-06T00:00:00.000",
"email":"[email protected]"
},
{
"revenue":384.85,
"rating":1.5,
"date":"2026-02-08T00:00:00.000",
"email":"[email protected]"
},
{
"revenue":246.0,
"rating":3.8,
"date":"2026-01-07T00:00:00.000",
"email":"[email protected]"
},
{
"revenue":972.58,
"rating":4.8,
"date":"2025-05-02T00:00:00.000",
"email":"[email protected]"
},
{
"revenue":219.93,
"rating":3.3,
"date":"2025-07-15T00:00:00.000",
"email":"[email protected]"
},
{
"revenue":258.3,
"rating":2.8,
"date":"2025-05-29T00:00:00.000",
"email":"[email protected]"
},
{
"revenue":144.02,
"rating":4.5,
"date":"2026-01-12T00:00:00.000",
"email":"[email protected]"
},
{
"revenue":607.38,
"rating":4.9,
"date":"2025-07-12T00:00:00.000",
"email":"[email protected]"
},
{
"revenue":252.27,
"rating":1.9,
"date":"2025-05-04T00:00:00.000",
"email":"[email protected]"
},
{
"revenue":492.85,
"rating":3.7,
"date":"2025-05-12T00:00:00.000",
"email":"[email protected]"
},
{
"revenue":50.14,
"rating":1.4,
"date":"2025-06-23T00:00:00.000",
"email":"[email protected]"
},
{
"revenue":993.35,
"rating":1.6,
"date":"2025-11-01T00:00:00.000",
"email":"[email protected]"
},
{
"revenue":799.65,
"rating":2.7,
"date":"2026-02-25T00:00:00.000",
"email":"[email protected]"
},
{
"revenue":973.04,
"rating":3.4,
"date":"2026-01-31T00:00:00.000",
"email":"[email protected]"
},
{
"revenue":82.64,
"rating":4.8,
"date":"2025-07-31T00:00:00.000",
"email":"[email protected]"
},
{
"revenue":457.27,
"rating":3.3,
"date":"2026-01-02T00:00:00.000",
"email":"[email protected]"
},
{
"revenue":688.85,
"rating":1.1,
"date":"2025-10-06T00:00:00.000",
"email":"[email protected]"
}
]
SQL: SELECT ROUND(AVG(rating * revenue), 2) FROM dataframe
Return result as single value. | 1367.81 | {"table": "[\n {\n \"revenue\":524.52,\n \"rating\":1.8,\n \"date\":\"2025-03-10T00:00:00.000\",\n \"email\":\"[email protected]\"\n },\n {\n \"revenue\":501.52,\n \"rating\":3.7,\n \"date\":\"2026-01-13T00:00:00.000\",\n \"email\":\"[email protected]\"\n },\n {\n \"revenue\":677.62,\n \"rating\":1.4,\n \"date\":\"2025-11-04T00:00:00.000\",\n \"email\":\"[email protected]\"\n },\n {\n \"revenue\":153.43,\n \"rating\":1.7,\n \"date\":\"2025-05-06T00:00:00.000\",\n \"email\":\"[email protected]\"\n },\n {\n \"revenue\":384.85,\n \"rating\":1.5,\n \"date\":\"2026-02-08T00:00:00.000\",\n \"email\":\"[email protected]\"\n },\n {\n \"revenue\":246.0,\n \"rating\":3.8,\n \"date\":\"2026-01-07T00:00:00.000\",\n \"email\":\"[email protected]\"\n },\n {\n \"revenue\":972.58,\n \"rating\":4.8,\n \"date\":\"2025-05-02T00:00:00.000\",\n \"email\":\"[email protected]\"\n },\n {\n \"revenue\":219.93,\n \"rating\":3.3,\n \"date\":\"2025-07-15T00:00:00.000\",\n \"email\":\"[email protected]\"\n },\n {\n \"revenue\":258.3,\n \"rating\":2.8,\n \"date\":\"2025-05-29T00:00:00.000\",\n \"email\":\"[email protected]\"\n },\n {\n \"revenue\":144.02,\n \"rating\":4.5,\n \"date\":\"2026-01-12T00:00:00.000\",\n \"email\":\"[email protected]\"\n },\n {\n \"revenue\":607.38,\n \"rating\":4.9,\n \"date\":\"2025-07-12T00:00:00.000\",\n \"email\":\"[email protected]\"\n },\n {\n \"revenue\":252.27,\n \"rating\":1.9,\n \"date\":\"2025-05-04T00:00:00.000\",\n \"email\":\"[email protected]\"\n },\n {\n \"revenue\":492.85,\n \"rating\":3.7,\n \"date\":\"2025-05-12T00:00:00.000\",\n \"email\":\"[email protected]\"\n },\n {\n \"revenue\":50.14,\n \"rating\":1.4,\n \"date\":\"2025-06-23T00:00:00.000\",\n \"email\":\"[email protected]\"\n },\n {\n \"revenue\":993.35,\n \"rating\":1.6,\n \"date\":\"2025-11-01T00:00:00.000\",\n \"email\":\"[email protected]\"\n },\n {\n \"revenue\":799.65,\n \"rating\":2.7,\n \"date\":\"2026-02-25T00:00:00.000\",\n \"email\":\"[email protected]\"\n },\n {\n \"revenue\":973.04,\n \"rating\":3.4,\n \"date\":\"2026-01-31T00:00:00.000\",\n \"email\":\"[email protected]\"\n },\n {\n \"revenue\":82.64,\n \"rating\":4.8,\n \"date\":\"2025-07-31T00:00:00.000\",\n \"email\":\"[email protected]\"\n },\n {\n \"revenue\":457.27,\n \"rating\":3.3,\n \"date\":\"2026-01-02T00:00:00.000\",\n \"email\":\"[email protected]\"\n },\n {\n \"revenue\":688.85,\n \"rating\":1.1,\n \"date\":\"2025-10-06T00:00:00.000\",\n \"email\":\"[email protected]\"\n }\n]", "query": "SELECT ROUND(AVG(rating * revenue), 2) FROM dataframe", "is_scalar": true, "table_format": "to_json", "_time": 0.03528594970703125, "_task": "table_qa", "_level": 2, "_config": {"c": 1.0, "level": 2, "seed": null, "size": null, "num_rows": 20, "num_columns": 4}, "_prompt_tokens": 1044, "_cot_tokens": 4} | table_qa | 2 | instruct |
Execute this SQL query on the table:
\begin{tabular}{llll}
job & email & product & date \\
Purchasing manager & [email protected] & Form & 2025-11-21 \\
Print production planner & [email protected] & Within & 2025-10-22 \\
Tourism officer & [email protected] & Its & 2025-11-22 \\
Chief Strategy Officer & [email protected] & Fall & 2025-07-05 \\
Geophysical data processor & [email protected] & Source & 2025-08-21 \\
Dramatherapist & [email protected] & Western & 2026-02-25 \\
Systems developer & [email protected] & Key & 2026-02-25 \\
Designer, graphic & [email protected] & Career & 2025-07-06 \\
Secondary school teacher & [email protected] & Fill & 2025-12-06 \\
Research officer, government & [email protected] & Concern & 2025-07-18 \\
Doctor, hospital & [email protected] & Service & 2025-03-17 \\
Waste management officer & [email protected] & Hotel & 2026-01-16 \\
Administrator, education & [email protected] & Leg & 2026-01-03 \\
Surveyor, quantity & [email protected] & Paper & 2026-01-29 \\
Technical sales engineer & [email protected] & East & 2025-09-21 \\
Location manager & [email protected] & General & 2025-06-07 \\
Fast food restaurant manager & [email protected] & Final & 2025-10-25 \\
Industrial/product designer & [email protected] & Tonight & 2025-09-21 \\
Training and development officer & [email protected] & Three & 2025-03-28 \\
Town planner & [email protected] & In & 2025-07-30 \\
\end{tabular}
SQL: SELECT COUNT(DISTINCT date) FROM dataframe
Return result as single value. | 18 | {"table": "\\begin{tabular}{llll}\njob & email & product & date \\\\\nPurchasing manager & [email protected] & Form & 2025-11-21 \\\\\nPrint production planner & [email protected] & Within & 2025-10-22 \\\\\nTourism officer & [email protected] & Its & 2025-11-22 \\\\\nChief Strategy Officer & [email protected] & Fall & 2025-07-05 \\\\\nGeophysical data processor & [email protected] & Source & 2025-08-21 \\\\\nDramatherapist & [email protected] & Western & 2026-02-25 \\\\\nSystems developer & [email protected] & Key & 2026-02-25 \\\\\nDesigner, graphic & [email protected] & Career & 2025-07-06 \\\\\nSecondary school teacher & [email protected] & Fill & 2025-12-06 \\\\\nResearch officer, government & [email protected] & Concern & 2025-07-18 \\\\\nDoctor, hospital & [email protected] & Service & 2025-03-17 \\\\\nWaste management officer & [email protected] & Hotel & 2026-01-16 \\\\\nAdministrator, education & [email protected] & Leg & 2026-01-03 \\\\\nSurveyor, quantity & [email protected] & Paper & 2026-01-29 \\\\\nTechnical sales engineer & [email protected] & East & 2025-09-21 \\\\\nLocation manager & [email protected] & General & 2025-06-07 \\\\\nFast food restaurant manager & [email protected] & Final & 2025-10-25 \\\\\nIndustrial/product designer & [email protected] & Tonight & 2025-09-21 \\\\\nTraining and development officer & [email protected] & Three & 2025-03-28 \\\\\nTown planner & [email protected] & In & 2025-07-30 \\\\\n\\end{tabular}\n", "query": "SELECT COUNT(DISTINCT date) FROM dataframe", "is_scalar": true, "table_format": "to_latex", "_time": 0.03828859329223633, "_task": "table_qa", "_level": 2, "_config": {"c": 1.0, "level": 2, "seed": null, "size": null, "num_rows": 20, "num_columns": 4}, "_prompt_tokens": 476, "_cot_tokens": 1} | table_qa | 2 | instruct |
Execute this SQL query on the table:
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>customer</th>
<th>email</th>
<th>company</th>
<th>product</th>
</tr>
</thead>
<tbody>
<tr>
<td>Troy Terry</td>
<td>[email protected]</td>
<td>Smith-Lee</td>
<td>Same</td>
</tr>
<tr>
<td>Phillip Davis</td>
<td>[email protected]</td>
<td>Gray Ltd</td>
<td>Past</td>
</tr>
<tr>
<td>James Bell</td>
<td>[email protected]</td>
<td>Sparks Group</td>
<td>Dinner</td>
</tr>
<tr>
<td>Ms. Brianna Smith</td>
<td>[email protected]</td>
<td>Caldwell, White and Savage</td>
<td>Without</td>
</tr>
<tr>
<td>Nicole Thomas</td>
<td>[email protected]</td>
<td>Mcbride, Mcintosh and Graves</td>
<td>Worker</td>
</tr>
<tr>
<td>Teresa Taylor</td>
<td>[email protected]</td>
<td>Cole Ltd</td>
<td>Organization</td>
</tr>
<tr>
<td>Zachary Brown</td>
<td>[email protected]</td>
<td>Ford, Bird and Hernandez</td>
<td>As</td>
</tr>
<tr>
<td>Laurie Chang</td>
<td>[email protected]</td>
<td>Benitez PLC</td>
<td>Evidence</td>
</tr>
<tr>
<td>Stephanie Vargas</td>
<td>[email protected]</td>
<td>Smith-Norton</td>
<td>Five</td>
</tr>
<tr>
<td>David Fisher</td>
<td>[email protected]</td>
<td>Anthony, Carpenter and Perkins</td>
<td>Size</td>
</tr>
<tr>
<td>Duane Bell</td>
<td>[email protected]</td>
<td>Sullivan, King and Glover</td>
<td>Current</td>
</tr>
<tr>
<td>Jennifer Miller</td>
<td>[email protected]</td>
<td>Sampson, Johnson and Rhodes</td>
<td>At</td>
</tr>
<tr>
<td>David Moore</td>
<td>[email protected]</td>
<td>Richmond-Wells</td>
<td>Move</td>
</tr>
<tr>
<td>Barry Perry</td>
<td>[email protected]</td>
<td>Porter Ltd</td>
<td>Child</td>
</tr>
<tr>
<td>Courtney Grant</td>
<td>[email protected]</td>
<td>Kelly-Frazier</td>
<td>Ok</td>
</tr>
<tr>
<td>Jonathan Johnson</td>
<td>[email protected]</td>
<td>Kennedy-Daniels</td>
<td>Number</td>
</tr>
<tr>
<td>Lisa Bryant</td>
<td>[email protected]</td>
<td>Clark, Moore and Vega</td>
<td>At</td>
</tr>
<tr>
<td>Elizabeth Munoz</td>
<td>[email protected]</td>
<td>Hernandez and Sons</td>
<td>Table</td>
</tr>
<tr>
<td>Jimmy Miller</td>
<td>[email protected]</td>
<td>Ferguson, Scott and Hines</td>
<td>Out</td>
</tr>
<tr>
<td>Benjamin Turner</td>
<td>[email protected]</td>
<td>Ryan-Rogers</td>
<td>Exist</td>
</tr>
</tbody>
</table>
SQL: SELECT COUNT(*) FROM dataframe WHERE company LIKE '%ord, Bird and Hernandez%'
Return result as single value. | 1 | {"table": "<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th>customer</th>\n <th>email</th>\n <th>company</th>\n <th>product</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td>Troy Terry</td>\n <td>[email protected]</td>\n <td>Smith-Lee</td>\n <td>Same</td>\n </tr>\n <tr>\n <td>Phillip Davis</td>\n <td>[email protected]</td>\n <td>Gray Ltd</td>\n <td>Past</td>\n </tr>\n <tr>\n <td>James Bell</td>\n <td>[email protected]</td>\n <td>Sparks Group</td>\n <td>Dinner</td>\n </tr>\n <tr>\n <td>Ms. Brianna Smith</td>\n <td>[email protected]</td>\n <td>Caldwell, White and Savage</td>\n <td>Without</td>\n </tr>\n <tr>\n <td>Nicole Thomas</td>\n <td>[email protected]</td>\n <td>Mcbride, Mcintosh and Graves</td>\n <td>Worker</td>\n </tr>\n <tr>\n <td>Teresa Taylor</td>\n <td>[email protected]</td>\n <td>Cole Ltd</td>\n <td>Organization</td>\n </tr>\n <tr>\n <td>Zachary Brown</td>\n <td>[email protected]</td>\n <td>Ford, Bird and Hernandez</td>\n <td>As</td>\n </tr>\n <tr>\n <td>Laurie Chang</td>\n <td>[email protected]</td>\n <td>Benitez PLC</td>\n <td>Evidence</td>\n </tr>\n <tr>\n <td>Stephanie Vargas</td>\n <td>[email protected]</td>\n <td>Smith-Norton</td>\n <td>Five</td>\n </tr>\n <tr>\n <td>David Fisher</td>\n <td>[email protected]</td>\n <td>Anthony, Carpenter and Perkins</td>\n <td>Size</td>\n </tr>\n <tr>\n <td>Duane Bell</td>\n <td>[email protected]</td>\n <td>Sullivan, King and Glover</td>\n <td>Current</td>\n </tr>\n <tr>\n <td>Jennifer Miller</td>\n <td>[email protected]</td>\n <td>Sampson, Johnson and Rhodes</td>\n <td>At</td>\n </tr>\n <tr>\n <td>David Moore</td>\n <td>[email protected]</td>\n <td>Richmond-Wells</td>\n <td>Move</td>\n </tr>\n <tr>\n <td>Barry Perry</td>\n <td>[email protected]</td>\n <td>Porter Ltd</td>\n <td>Child</td>\n </tr>\n <tr>\n <td>Courtney Grant</td>\n <td>[email protected]</td>\n <td>Kelly-Frazier</td>\n <td>Ok</td>\n </tr>\n <tr>\n <td>Jonathan Johnson</td>\n <td>[email protected]</td>\n <td>Kennedy-Daniels</td>\n <td>Number</td>\n </tr>\n <tr>\n <td>Lisa Bryant</td>\n <td>[email protected]</td>\n <td>Clark, Moore and Vega</td>\n <td>At</td>\n </tr>\n <tr>\n <td>Elizabeth Munoz</td>\n <td>[email protected]</td>\n <td>Hernandez and Sons</td>\n <td>Table</td>\n </tr>\n <tr>\n <td>Jimmy Miller</td>\n <td>[email protected]</td>\n <td>Ferguson, Scott and Hines</td>\n <td>Out</td>\n </tr>\n <tr>\n <td>Benjamin Turner</td>\n <td>[email protected]</td>\n <td>Ryan-Rogers</td>\n <td>Exist</td>\n </tr>\n </tbody>\n</table>", "query": "SELECT COUNT(*) FROM dataframe WHERE company LIKE '%ord, Bird and Hernandez%'", "is_scalar": true, "table_format": "to_html", "_time": 0.03367304801940918, "_task": "table_qa", "_level": 2, "_config": {"c": 1.0, "level": 2, "seed": null, "size": null, "num_rows": 20, "num_columns": 4}, "_prompt_tokens": 1091, "_cot_tokens": 1} | table_qa | 2 | instruct |
Execute this SQL query on the table:
price,job,qty,date
402.54,"Editor, film/video",740,2025-05-31
358.97,Software engineer,399,2026-02-25
56.46,Psychotherapist,590,2025-11-22
169.47,Research scientist (maths),657,2025-09-26
40.81,Regulatory affairs officer,379,2026-01-23
372.19,"Designer, ceramics/pottery",717,2025-04-26
163.46,Health service manager,139,2025-10-02
211.2,"Designer, fashion/clothing",757,2025-07-15
126.37,Conference centre manager,602,2025-11-14
298.5,Cabin crew,875,2025-04-18
171.43,Mechanical engineer,501,2025-09-12
388.45,Barrister,570,2026-01-31
191.91,"Buyer, retail",801,2025-06-04
468.13,Insurance broker,464,2026-01-24
194.41,Hospital doctor,311,2025-04-27
132.38,"Accountant, chartered certified",725,2026-02-04
443.08,Print production planner,950,2025-07-24
10.43,Education administrator,254,2025-08-06
186.81,Emergency planning/management officer,125,2025-10-22
153.39,TEFL teacher,630,2025-12-07
SQL: SELECT COUNT(DISTINCT date) FROM dataframe
Return result as single value. | 20 | {"table": "price,job,qty,date\n402.54,\"Editor, film/video\",740,2025-05-31\n358.97,Software engineer,399,2026-02-25\n56.46,Psychotherapist,590,2025-11-22\n169.47,Research scientist (maths),657,2025-09-26\n40.81,Regulatory affairs officer,379,2026-01-23\n372.19,\"Designer, ceramics/pottery\",717,2025-04-26\n163.46,Health service manager,139,2025-10-02\n211.2,\"Designer, fashion/clothing\",757,2025-07-15\n126.37,Conference centre manager,602,2025-11-14\n298.5,Cabin crew,875,2025-04-18\n171.43,Mechanical engineer,501,2025-09-12\n388.45,Barrister,570,2026-01-31\n191.91,\"Buyer, retail\",801,2025-06-04\n468.13,Insurance broker,464,2026-01-24\n194.41,Hospital doctor,311,2025-04-27\n132.38,\"Accountant, chartered certified\",725,2026-02-04\n443.08,Print production planner,950,2025-07-24\n10.43,Education administrator,254,2025-08-06\n186.81,Emergency planning/management officer,125,2025-10-22\n153.39,TEFL teacher,630,2025-12-07\n", "query": "SELECT COUNT(DISTINCT date) FROM dataframe", "is_scalar": true, "table_format": "to_csv", "_time": 0.03547096252441406, "_task": "table_qa", "_level": 2, "_config": {"c": 1.0, "level": 2, "seed": null, "size": null, "num_rows": 20, "num_columns": 4}, "_prompt_tokens": 378, "_cot_tokens": 1} | table_qa | 2 | instruct |
Execute this SQL query on the table:
- city: Simsmouth
country: Colombia
customer: Rachel Graham
price: 348.94
- city: Mcdowellmouth
country: Benin
customer: Jeffery Leonard
price: 65.31
- city: Lake Lee
country: Mongolia
customer: Sandra Jacobson
price: 460.34
- city: Williamsville
country: Syrian Arab Republic
customer: Jeffery Roman
price: 360.06
- city: Washingtonhaven
country: Belize
customer: Amber Thornton
price: 375.55
- city: West Troy
country: Heard Island and McDonald Islands
customer: Edwin Nash
price: 496.21
- city: North Audreyland
country: Guyana
customer: Larry Martin
price: 39.59
- city: West Timothy
country: Haiti
customer: Peter Jones
price: 66.39
- city: Laurenchester
country: Sweden
customer: Kimberly Clark
price: 231.27
- city: Sheilastad
country: Faroe Islands
customer: Erica Ochoa
price: 368.24
- city: New Jenniferhaven
country: Belize
customer: Christopher Thomas
price: 466.37
- city: Lisabury
country: Albania
customer: Hannah Hamilton
price: 357.0
- city: Wolfestad
country: Morocco
customer: Maria Bradley
price: 398.82
- city: South Kyle
country: Korea
customer: Elizabeth Powers DDS
price: 209.89
- city: North Levishire
country: Hong Kong
customer: Mr. Richard Taylor
price: 374.76
- city: Karenburgh
country: Denmark
customer: Sarah Ayala
price: 370.4
- city: South Amymouth
country: South Georgia and the South Sandwich Islands
customer: Tony Whitehead
price: 269.6
- city: Dominguezton
country: Macao
customer: Javier Sanchez
price: 221.98
- city: Port Christopherbury
country: Montserrat
customer: Mrs. Carol Miller DDS
price: 380.6
- city: Wileytown
country: Netherlands
customer: Gina Swanson
price: 79.46
SQL: SELECT COUNT(*) FROM dataframe WHERE city LIKE '%imsmouth%'
Return result as single value. | 1 | {"table": "- city: Simsmouth\n country: Colombia\n customer: Rachel Graham\n price: 348.94\n- city: Mcdowellmouth\n country: Benin\n customer: Jeffery Leonard\n price: 65.31\n- city: Lake Lee\n country: Mongolia\n customer: Sandra Jacobson\n price: 460.34\n- city: Williamsville\n country: Syrian Arab Republic\n customer: Jeffery Roman\n price: 360.06\n- city: Washingtonhaven\n country: Belize\n customer: Amber Thornton\n price: 375.55\n- city: West Troy\n country: Heard Island and McDonald Islands\n customer: Edwin Nash\n price: 496.21\n- city: North Audreyland\n country: Guyana\n customer: Larry Martin\n price: 39.59\n- city: West Timothy\n country: Haiti\n customer: Peter Jones\n price: 66.39\n- city: Laurenchester\n country: Sweden\n customer: Kimberly Clark\n price: 231.27\n- city: Sheilastad\n country: Faroe Islands\n customer: Erica Ochoa\n price: 368.24\n- city: New Jenniferhaven\n country: Belize\n customer: Christopher Thomas\n price: 466.37\n- city: Lisabury\n country: Albania\n customer: Hannah Hamilton\n price: 357.0\n- city: Wolfestad\n country: Morocco\n customer: Maria Bradley\n price: 398.82\n- city: South Kyle\n country: Korea\n customer: Elizabeth Powers DDS\n price: 209.89\n- city: North Levishire\n country: Hong Kong\n customer: Mr. Richard Taylor\n price: 374.76\n- city: Karenburgh\n country: Denmark\n customer: Sarah Ayala\n price: 370.4\n- city: South Amymouth\n country: South Georgia and the South Sandwich Islands\n customer: Tony Whitehead\n price: 269.6\n- city: Dominguezton\n country: Macao\n customer: Javier Sanchez\n price: 221.98\n- city: Port Christopherbury\n country: Montserrat\n customer: Mrs. Carol Miller DDS\n price: 380.6\n- city: Wileytown\n country: Netherlands\n customer: Gina Swanson\n price: 79.46\n", "query": "SELECT COUNT(*) FROM dataframe WHERE city LIKE '%imsmouth%'", "is_scalar": true, "table_format": "to_yaml", "_time": 0.03296184539794922, "_task": "table_qa", "_level": 2, "_config": {"c": 1.0, "level": 2, "seed": null, "size": null, "num_rows": 20, "num_columns": 4}, "_prompt_tokens": 579, "_cot_tokens": 1} | table_qa | 2 | instruct |
Execute this SQL query on the table:
- country: Paraguay
date: 2025-05-17
job: Warden/ranger
product: Machine
- country: British Indian Ocean Territory (Chagos Archipelago)
date: 2025-10-27
job: Press sub
product: Finally
- country: Gambia
date: 2025-10-26
job: Water engineer
product: Idea
- country: Kazakhstan
date: 2025-11-28
job: Barrister's clerk
product: Recognize
- country: Italy
date: 2025-05-30
job: Administrator, sports
product: Feeling
- country: Uganda
date: 2025-09-26
job: Physiotherapist
product: Today
- country: Costa Rica
date: 2025-08-08
job: Financial adviser
product: Second
- country: Norway
date: 2025-03-10
job: Housing manager/officer
product: Good
- country: Italy
date: 2025-08-05
job: Psychotherapist, dance movement
product: Offer
- country: Central African Republic
date: 2025-03-08
job: Designer, industrial/product
product: Experience
- country: Bouvet Island (Bouvetoya)
date: 2025-07-08
job: Event organiser
product: Drive
- country: Turkmenistan
date: 2025-10-16
job: Ergonomist
product: High
- country: Equatorial Guinea
date: 2025-03-05
job: Scientist, research (maths)
product: Study
- country: Australia
date: 2025-10-31
job: Embryologist, clinical
product: Assume
- country: Greenland
date: 2025-09-17
job: Operations geologist
product: Lead
- country: Austria
date: 2025-10-09
job: Technical author
product: Campaign
- country: Yemen
date: 2026-02-08
job: Theatre director
product: Box
- country: Cote d'Ivoire
date: 2025-11-16
job: Energy manager
product: Imagine
- country: Lesotho
date: 2026-01-10
job: Advertising copywriter
product: Decade
- country: Liberia
date: 2025-10-21
job: Scientist, research (maths)
product: Arrive
SQL: SELECT COUNT(DISTINCT job) FROM dataframe
Return result as single value. | 19 | {"table": "- country: Paraguay\n date: 2025-05-17\n job: Warden/ranger\n product: Machine\n- country: British Indian Ocean Territory (Chagos Archipelago)\n date: 2025-10-27\n job: Press sub\n product: Finally\n- country: Gambia\n date: 2025-10-26\n job: Water engineer\n product: Idea\n- country: Kazakhstan\n date: 2025-11-28\n job: Barrister's clerk\n product: Recognize\n- country: Italy\n date: 2025-05-30\n job: Administrator, sports\n product: Feeling\n- country: Uganda\n date: 2025-09-26\n job: Physiotherapist\n product: Today\n- country: Costa Rica\n date: 2025-08-08\n job: Financial adviser\n product: Second\n- country: Norway\n date: 2025-03-10\n job: Housing manager/officer\n product: Good\n- country: Italy\n date: 2025-08-05\n job: Psychotherapist, dance movement\n product: Offer\n- country: Central African Republic\n date: 2025-03-08\n job: Designer, industrial/product\n product: Experience\n- country: Bouvet Island (Bouvetoya)\n date: 2025-07-08\n job: Event organiser\n product: Drive\n- country: Turkmenistan\n date: 2025-10-16\n job: Ergonomist\n product: High\n- country: Equatorial Guinea\n date: 2025-03-05\n job: Scientist, research (maths)\n product: Study\n- country: Australia\n date: 2025-10-31\n job: Embryologist, clinical\n product: Assume\n- country: Greenland\n date: 2025-09-17\n job: Operations geologist\n product: Lead\n- country: Austria\n date: 2025-10-09\n job: Technical author\n product: Campaign\n- country: Yemen\n date: 2026-02-08\n job: Theatre director\n product: Box\n- country: Cote d'Ivoire\n date: 2025-11-16\n job: Energy manager\n product: Imagine\n- country: Lesotho\n date: 2026-01-10\n job: Advertising copywriter\n product: Decade\n- country: Liberia\n date: 2025-10-21\n job: Scientist, research (maths)\n product: Arrive\n", "query": "SELECT COUNT(DISTINCT job) FROM dataframe", "is_scalar": true, "table_format": "to_yaml", "_time": 0.03442859649658203, "_task": "table_qa", "_level": 2, "_config": {"c": 1.0, "level": 2, "seed": null, "size": null, "num_rows": 20, "num_columns": 4}, "_prompt_tokens": 622, "_cot_tokens": 1} | table_qa | 2 | instruct |
Execute this SQL query on the table:
- customer: Gregory Dunn
job: Magazine journalist
price: 251.87
qty: 668
- customer: Alexis Davis
job: Probation officer
price: 219.97
qty: 422
- customer: Sally Sanders
job: Engineer, drilling
price: 342.75
qty: 658
- customer: Joseph Ochoa
job: Radiation protection practitioner
price: 171.85
qty: 499
- customer: Christine Fritz
job: Mudlogger
price: 147.11
qty: 386
- customer: Jessica Miller
job: Clinical biochemist
price: 300.76
qty: 286
- customer: Michael Thomas
job: Further education lecturer
price: 262.96
qty: 75
- customer: Travis Morgan
job: Sports development officer
price: 152.51
qty: 546
- customer: Margaret Thomas
job: Press photographer
price: 271.91
qty: 209
- customer: Julia Chan
job: TEFL teacher
price: 110.83
qty: 869
- customer: Dwayne Cross
job: Landscape architect
price: 300.44
qty: 76
- customer: Natalie Hess
job: Chemical engineer
price: 151.26
qty: 57
- customer: Jose Tucker
job: Engineer, biomedical
price: 289.48
qty: 129
- customer: Matthew Hughes
job: Librarian, academic
price: 440.44
qty: 115
- customer: Erin Henry
job: Farm manager
price: 430.21
qty: 56
- customer: Stephanie Baker
job: Investment banker, operational
price: 153.0
qty: 860
- customer: James Harris
job: Actor
price: 153.18
qty: 828
- customer: Ethan Harris
job: Geoscientist
price: 434.99
qty: 92
- customer: Marcus Fisher
job: Chief Financial Officer
price: 405.58
qty: 299
- customer: James Davis
job: Training and development officer
price: 226.7
qty: 208
SQL: SELECT COUNT(*) FROM dataframe WHERE customer = 'Gregory Dunn' AND price > 260.89
Return result as single value. | 0 | {"table": "- customer: Gregory Dunn\n job: Magazine journalist\n price: 251.87\n qty: 668\n- customer: Alexis Davis\n job: Probation officer\n price: 219.97\n qty: 422\n- customer: Sally Sanders\n job: Engineer, drilling\n price: 342.75\n qty: 658\n- customer: Joseph Ochoa\n job: Radiation protection practitioner\n price: 171.85\n qty: 499\n- customer: Christine Fritz\n job: Mudlogger\n price: 147.11\n qty: 386\n- customer: Jessica Miller\n job: Clinical biochemist\n price: 300.76\n qty: 286\n- customer: Michael Thomas\n job: Further education lecturer\n price: 262.96\n qty: 75\n- customer: Travis Morgan\n job: Sports development officer\n price: 152.51\n qty: 546\n- customer: Margaret Thomas\n job: Press photographer\n price: 271.91\n qty: 209\n- customer: Julia Chan\n job: TEFL teacher\n price: 110.83\n qty: 869\n- customer: Dwayne Cross\n job: Landscape architect\n price: 300.44\n qty: 76\n- customer: Natalie Hess\n job: Chemical engineer\n price: 151.26\n qty: 57\n- customer: Jose Tucker\n job: Engineer, biomedical\n price: 289.48\n qty: 129\n- customer: Matthew Hughes\n job: Librarian, academic\n price: 440.44\n qty: 115\n- customer: Erin Henry\n job: Farm manager\n price: 430.21\n qty: 56\n- customer: Stephanie Baker\n job: Investment banker, operational\n price: 153.0\n qty: 860\n- customer: James Harris\n job: Actor\n price: 153.18\n qty: 828\n- customer: Ethan Harris\n job: Geoscientist\n price: 434.99\n qty: 92\n- customer: Marcus Fisher\n job: Chief Financial Officer\n price: 405.58\n qty: 299\n- customer: James Davis\n job: Training and development officer\n price: 226.7\n qty: 208\n", "query": "SELECT COUNT(*) FROM dataframe WHERE customer = 'Gregory Dunn' AND price > 260.89", "is_scalar": true, "table_format": "to_yaml", "_time": 0.03965473175048828, "_task": "table_qa", "_level": 2, "_config": {"c": 1.0, "level": 2, "seed": null, "size": null, "num_rows": 20, "num_columns": 4}, "_prompt_tokens": 577, "_cot_tokens": 1} | table_qa | 2 | instruct |
Execute this SQL query on the table:
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>date</th>
<th>email</th>
<th>product</th>
<th>qty</th>
</tr>
</thead>
<tbody>
<tr>
<td>2025-05-25</td>
<td>[email protected]</td>
<td>Stay</td>
<td>747</td>
</tr>
<tr>
<td>2025-05-21</td>
<td>[email protected]</td>
<td>Whether</td>
<td>519</td>
</tr>
<tr>
<td>2026-02-22</td>
<td>[email protected]</td>
<td>Turn</td>
<td>321</td>
</tr>
<tr>
<td>2025-03-17</td>
<td>[email protected]</td>
<td>What</td>
<td>184</td>
</tr>
<tr>
<td>2025-09-11</td>
<td>[email protected]</td>
<td>Agent</td>
<td>590</td>
</tr>
<tr>
<td>2025-05-17</td>
<td>[email protected]</td>
<td>Other</td>
<td>797</td>
</tr>
<tr>
<td>2025-04-01</td>
<td>[email protected]</td>
<td>Number</td>
<td>614</td>
</tr>
<tr>
<td>2025-08-09</td>
<td>[email protected]</td>
<td>Coach</td>
<td>642</td>
</tr>
<tr>
<td>2025-04-19</td>
<td>[email protected]</td>
<td>Pick</td>
<td>120</td>
</tr>
<tr>
<td>2025-09-28</td>
<td>[email protected]</td>
<td>Build</td>
<td>342</td>
</tr>
<tr>
<td>2025-06-25</td>
<td>[email protected]</td>
<td>Rest</td>
<td>761</td>
</tr>
<tr>
<td>2026-01-16</td>
<td>[email protected]</td>
<td>Decision</td>
<td>531</td>
</tr>
<tr>
<td>2025-11-24</td>
<td>[email protected]</td>
<td>Be</td>
<td>622</td>
</tr>
<tr>
<td>2025-11-24</td>
<td>[email protected]</td>
<td>Rather</td>
<td>918</td>
</tr>
<tr>
<td>2025-03-20</td>
<td>[email protected]</td>
<td>Affect</td>
<td>623</td>
</tr>
<tr>
<td>2025-09-21</td>
<td>[email protected]</td>
<td>Western</td>
<td>81</td>
</tr>
<tr>
<td>2025-05-12</td>
<td>[email protected]</td>
<td>Truth</td>
<td>534</td>
</tr>
<tr>
<td>2025-05-15</td>
<td>[email protected]</td>
<td>Leg</td>
<td>608</td>
</tr>
<tr>
<td>2025-12-15</td>
<td>[email protected]</td>
<td>Rather</td>
<td>862</td>
</tr>
<tr>
<td>2025-08-14</td>
<td>[email protected]</td>
<td>Worker</td>
<td>166</td>
</tr>
</tbody>
</table>
SQL: SELECT email, SUM(qty) as v FROM dataframe GROUP BY email ORDER BY v DESC LIMIT 1
Return result as CSV format (rows separated by newlines, values by commas). | [email protected],918.0 | {"table": "<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th>date</th>\n <th>email</th>\n <th>product</th>\n <th>qty</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td>2025-05-25</td>\n <td>[email protected]</td>\n <td>Stay</td>\n <td>747</td>\n </tr>\n <tr>\n <td>2025-05-21</td>\n <td>[email protected]</td>\n <td>Whether</td>\n <td>519</td>\n </tr>\n <tr>\n <td>2026-02-22</td>\n <td>[email protected]</td>\n <td>Turn</td>\n <td>321</td>\n </tr>\n <tr>\n <td>2025-03-17</td>\n <td>[email protected]</td>\n <td>What</td>\n <td>184</td>\n </tr>\n <tr>\n <td>2025-09-11</td>\n <td>[email protected]</td>\n <td>Agent</td>\n <td>590</td>\n </tr>\n <tr>\n <td>2025-05-17</td>\n <td>[email protected]</td>\n <td>Other</td>\n <td>797</td>\n </tr>\n <tr>\n <td>2025-04-01</td>\n <td>[email protected]</td>\n <td>Number</td>\n <td>614</td>\n </tr>\n <tr>\n <td>2025-08-09</td>\n <td>[email protected]</td>\n <td>Coach</td>\n <td>642</td>\n </tr>\n <tr>\n <td>2025-04-19</td>\n <td>[email protected]</td>\n <td>Pick</td>\n <td>120</td>\n </tr>\n <tr>\n <td>2025-09-28</td>\n <td>[email protected]</td>\n <td>Build</td>\n <td>342</td>\n </tr>\n <tr>\n <td>2025-06-25</td>\n <td>[email protected]</td>\n <td>Rest</td>\n <td>761</td>\n </tr>\n <tr>\n <td>2026-01-16</td>\n <td>[email protected]</td>\n <td>Decision</td>\n <td>531</td>\n </tr>\n <tr>\n <td>2025-11-24</td>\n <td>[email protected]</td>\n <td>Be</td>\n <td>622</td>\n </tr>\n <tr>\n <td>2025-11-24</td>\n <td>[email protected]</td>\n <td>Rather</td>\n <td>918</td>\n </tr>\n <tr>\n <td>2025-03-20</td>\n <td>[email protected]</td>\n <td>Affect</td>\n <td>623</td>\n </tr>\n <tr>\n <td>2025-09-21</td>\n <td>[email protected]</td>\n <td>Western</td>\n <td>81</td>\n </tr>\n <tr>\n <td>2025-05-12</td>\n <td>[email protected]</td>\n <td>Truth</td>\n <td>534</td>\n </tr>\n <tr>\n <td>2025-05-15</td>\n <td>[email protected]</td>\n <td>Leg</td>\n <td>608</td>\n </tr>\n <tr>\n <td>2025-12-15</td>\n <td>[email protected]</td>\n <td>Rather</td>\n <td>862</td>\n </tr>\n <tr>\n <td>2025-08-14</td>\n <td>[email protected]</td>\n <td>Worker</td>\n <td>166</td>\n </tr>\n </tbody>\n</table>", "query": "SELECT email, SUM(qty) as v FROM dataframe GROUP BY email ORDER BY v DESC LIMIT 1", "is_scalar": false, "table_format": "to_html", "_time": 0.04175758361816406, "_task": "table_qa", "_level": 2, "_config": {"c": 1.0, "level": 2, "seed": null, "size": null, "num_rows": 20, "num_columns": 4}, "_prompt_tokens": 1107, "_cot_tokens": 9} | table_qa | 2 | instruct |
Execute this SQL query on the table:
date,job,price,email
2025-09-28,"Designer, blown glass/stained glass",145.82,[email protected]
2025-03-27,"Physicist, medical",47.54,[email protected]
2026-02-26,Research scientist (life sciences),290.81,[email protected]
2025-06-01,Clinical cytogeneticist,91.09,[email protected]
2026-02-28,Toxicologist,235.22,[email protected]
2025-06-09,Geoscientist,146.61,[email protected]
2025-08-29,"Psychologist, prison and probation services",458.94,[email protected]
2026-01-30,"Engineer, aeronautical",460.34,[email protected]
2026-02-19,Proofreader,227.45,[email protected]
2025-07-17,Food technologist,169.89,[email protected]
2025-05-04,Education administrator,276.98,[email protected]
2025-09-19,Barrister,102.06,[email protected]
2025-11-16,"Production assistant, television",382.98,[email protected]
2025-04-29,Chartered legal executive (England and Wales),222.6,[email protected]
2025-12-01,Armed forces operational officer,171.47,[email protected]
2025-06-17,Manufacturing systems engineer,321.65,[email protected]
2025-06-05,"Surveyor, hydrographic",35.97,[email protected]
2025-05-20,Naval architect,198.39,[email protected]
2025-05-20,Magazine features editor,152.99,[email protected]
2025-09-26,Public affairs consultant,263.9,[email protected]
SQL: SELECT ROUND(MAX(price), 2) FROM dataframe
Return result as single value. | 460.34 | {"table": "date,job,price,email\n2025-09-28,\"Designer, blown glass/stained glass\",145.82,[email protected]\n2025-03-27,\"Physicist, medical\",47.54,[email protected]\n2026-02-26,Research scientist (life sciences),290.81,[email protected]\n2025-06-01,Clinical cytogeneticist,91.09,[email protected]\n2026-02-28,Toxicologist,235.22,[email protected]\n2025-06-09,Geoscientist,146.61,[email protected]\n2025-08-29,\"Psychologist, prison and probation services\",458.94,[email protected]\n2026-01-30,\"Engineer, aeronautical\",460.34,[email protected]\n2026-02-19,Proofreader,227.45,[email protected]\n2025-07-17,Food technologist,169.89,[email protected]\n2025-05-04,Education administrator,276.98,[email protected]\n2025-09-19,Barrister,102.06,[email protected]\n2025-11-16,\"Production assistant, television\",382.98,[email protected]\n2025-04-29,Chartered legal executive (England and Wales),222.6,[email protected]\n2025-12-01,Armed forces operational officer,171.47,[email protected]\n2025-06-17,Manufacturing systems engineer,321.65,[email protected]\n2025-06-05,\"Surveyor, hydrographic\",35.97,[email protected]\n2025-05-20,Naval architect,198.39,[email protected]\n2025-05-20,Magazine features editor,152.99,[email protected]\n2025-09-26,Public affairs consultant,263.9,[email protected]\n", "query": "SELECT ROUND(MAX(price), 2) FROM dataframe", "is_scalar": true, "table_format": "to_csv", "_time": 0.030745983123779297, "_task": "table_qa", "_level": 2, "_config": {"c": 1.0, "level": 2, "seed": null, "size": null, "num_rows": 20, "num_columns": 4}, "_prompt_tokens": 473, "_cot_tokens": 3} | table_qa | 2 | instruct |
Execute this SQL query on the table:
| price | product | revenue | customer |
|--------:|:----------|----------:|:-----------------|
| 150.37 | Food | 821.76 | Curtis Liu |
| 51.86 | Life | 367.58 | David Wilson |
| 5.94 | Chance | 764.97 | Julie Jarvis |
| 248.25 | Shoulder | 383.58 | Joseph Knight |
| 39.76 | Reveal | 831.19 | Kyle Rodriguez |
| 32.9 | Forward | 628.67 | Dillon Barnes |
| 97.74 | Space | 979.77 | Michael Phillips |
| 164.15 | World | 59.04 | Karen Reyes |
| 122.86 | Seem | 33.42 | Timothy Castillo |
| 144.85 | Behind | 904.83 | Robert Bowman |
| 117.03 | Field | 931.59 | Sarah Castillo |
| 389.88 | Rock | 296.57 | Tara Gardner |
| 434.37 | Total | 488.73 | Paul Love |
| 42.08 | Reflect | 273.02 | Thomas Porter |
| 328.98 | Owner | 108.32 | Morgan Green |
| 62.16 | Of | 876.88 | Morgan Lewis |
| 355.77 | Sense | 785.82 | Brett Diaz |
| 450.06 | Action | 372.68 | Dr. Tammy Hood |
| 433.39 | Thought | 517.77 | Rachael Munoz |
| 295.55 | Memory | 145.64 | Amber Garcia |
SQL: SELECT * FROM dataframe ORDER BY price ASC LIMIT 2
Return result as CSV format (rows separated by newlines, values by commas). | 5.94,Chance,764.97,Julie Jarvis
32.9,Forward,628.67,Dillon Barnes | {"table": "| price | product | revenue | customer |\n|--------:|:----------|----------:|:-----------------|\n| 150.37 | Food | 821.76 | Curtis Liu |\n| 51.86 | Life | 367.58 | David Wilson |\n| 5.94 | Chance | 764.97 | Julie Jarvis |\n| 248.25 | Shoulder | 383.58 | Joseph Knight |\n| 39.76 | Reveal | 831.19 | Kyle Rodriguez |\n| 32.9 | Forward | 628.67 | Dillon Barnes |\n| 97.74 | Space | 979.77 | Michael Phillips |\n| 164.15 | World | 59.04 | Karen Reyes |\n| 122.86 | Seem | 33.42 | Timothy Castillo |\n| 144.85 | Behind | 904.83 | Robert Bowman |\n| 117.03 | Field | 931.59 | Sarah Castillo |\n| 389.88 | Rock | 296.57 | Tara Gardner |\n| 434.37 | Total | 488.73 | Paul Love |\n| 42.08 | Reflect | 273.02 | Thomas Porter |\n| 328.98 | Owner | 108.32 | Morgan Green |\n| 62.16 | Of | 876.88 | Morgan Lewis |\n| 355.77 | Sense | 785.82 | Brett Diaz |\n| 450.06 | Action | 372.68 | Dr. Tammy Hood |\n| 433.39 | Thought | 517.77 | Rachael Munoz |\n| 295.55 | Memory | 145.64 | Amber Garcia |", "query": "SELECT * FROM dataframe ORDER BY price ASC LIMIT 2", "is_scalar": false, "table_format": "to_markdown", "_time": 0.03132271766662598, "_task": "table_qa", "_level": 2, "_config": {"c": 1.0, "level": 2, "seed": null, "size": null, "num_rows": 20, "num_columns": 4}, "_prompt_tokens": 470, "_cot_tokens": 26} | table_qa | 2 | instruct |
Execute this SQL query on the table:
| customer | job | rating | product |
|:-----------------|:----------------------------|---------:|:--------------|
| John Brooks | Glass blower/designer | 2.1 | Money |
| Alison Woodard | Paramedic | 2.8 | Environmental |
| Anita Ward | Call centre manager | 2 | Pressure |
| Jeff Mercer | Health promotion specialist | 2.4 | Despite |
| Zachary Smith | Geologist, engineering | 4.6 | Bar |
| Timothy Bennett | Adult guidance worker | 2.7 | Democrat |
| Erik Jimenez | Gaffer | 1.2 | Republican |
| Sharon Sanchez | Contracting civil engineer | 4.1 | Long |
| Thomas Thomas | Surveyor, mining | 1.9 | Inside |
| William Roberts | Fine artist | 2.5 | Situation |
| Timothy Stewart | Occupational hygienist | 1.3 | Maintain |
| Jodi Brady | Private music teacher | 4.6 | Provide |
| Pamela Miller | Fish farm manager | 3.1 | Senior |
| Michael Richmond | Retail buyer | 3.5 | Believe |
| Sarah Johnson | Arts development officer | 1.9 | Method |
| William Wilson | Psychologist, occupational | 3.2 | Data |
| Rebecca Flores | Primary school teacher | 3.9 | Some |
| Grace Walker | TEFL teacher | 1.1 | This |
| Laura Gill | Engineer, communications | 4 | Thought |
| Penny Navarro | Librarian, academic | 3 | Important |
SQL: SELECT COUNT(*) FROM dataframe WHERE customer = 'John Brooks' AND rating > 2.795
Return result as single value. | 0 | {"table": "| customer | job | rating | product |\n|:-----------------|:----------------------------|---------:|:--------------|\n| John Brooks | Glass blower/designer | 2.1 | Money |\n| Alison Woodard | Paramedic | 2.8 | Environmental |\n| Anita Ward | Call centre manager | 2 | Pressure |\n| Jeff Mercer | Health promotion specialist | 2.4 | Despite |\n| Zachary Smith | Geologist, engineering | 4.6 | Bar |\n| Timothy Bennett | Adult guidance worker | 2.7 | Democrat |\n| Erik Jimenez | Gaffer | 1.2 | Republican |\n| Sharon Sanchez | Contracting civil engineer | 4.1 | Long |\n| Thomas Thomas | Surveyor, mining | 1.9 | Inside |\n| William Roberts | Fine artist | 2.5 | Situation |\n| Timothy Stewart | Occupational hygienist | 1.3 | Maintain |\n| Jodi Brady | Private music teacher | 4.6 | Provide |\n| Pamela Miller | Fish farm manager | 3.1 | Senior |\n| Michael Richmond | Retail buyer | 3.5 | Believe |\n| Sarah Johnson | Arts development officer | 1.9 | Method |\n| William Wilson | Psychologist, occupational | 3.2 | Data |\n| Rebecca Flores | Primary school teacher | 3.9 | Some |\n| Grace Walker | TEFL teacher | 1.1 | This |\n| Laura Gill | Engineer, communications | 4 | Thought |\n| Penny Navarro | Librarian, academic | 3 | Important |", "query": "SELECT COUNT(*) FROM dataframe WHERE customer = 'John Brooks' AND rating > 2.795", "is_scalar": true, "table_format": "to_markdown", "_time": 0.0444483757019043, "_task": "table_qa", "_level": 2, "_config": {"c": 1.0, "level": 2, "seed": null, "size": null, "num_rows": 20, "num_columns": 4}, "_prompt_tokens": 444, "_cot_tokens": 1} | table_qa | 2 | instruct |
Execute this SQL query on the table:
product revenue date email
Church 594.21 2025-05-21 [email protected]
Choose 283.77 2025-05-29 [email protected]
Leader 405.43 2025-06-24 [email protected]
Tough 820.20 2025-12-07 [email protected]
Consumer 145.82 2025-04-13 [email protected]
Soldier 819.60 2025-08-12 [email protected]
Land 259.29 2025-05-16 [email protected]
May 816.27 2025-05-16 [email protected]
Full 132.00 2025-06-24 [email protected]
Property 208.71 2025-05-15 [email protected]
Inside 526.87 2025-03-22 [email protected]
Whole 602.29 2025-07-07 [email protected]
Phone 908.66 2025-09-26 [email protected]
Table 492.67 2025-08-24 [email protected]
Very 313.76 2026-01-15 [email protected]
Family 596.44 2025-12-05 [email protected]
Remember 463.57 2025-04-12 [email protected]
Everyone 358.23 2025-11-09 [email protected]
Manager 153.62 2025-04-26 [email protected]
Walk 252.55 2025-04-27 [email protected]
SQL: SELECT * FROM dataframe ORDER BY revenue ASC LIMIT 2
Return result as CSV format (rows separated by newlines, values by commas). | Full,132.0,2025-06-24,[email protected]
Consumer,145.82,2025-04-13,[email protected] | {"table": " product revenue date email\n Church 594.21 2025-05-21 [email protected]\n Choose 283.77 2025-05-29 [email protected]\n Leader 405.43 2025-06-24 [email protected]\n Tough 820.20 2025-12-07 [email protected]\nConsumer 145.82 2025-04-13 [email protected]\n Soldier 819.60 2025-08-12 [email protected]\n Land 259.29 2025-05-16 [email protected]\n May 816.27 2025-05-16 [email protected]\n Full 132.00 2025-06-24 [email protected]\nProperty 208.71 2025-05-15 [email protected]\n Inside 526.87 2025-03-22 [email protected]\n Whole 602.29 2025-07-07 [email protected]\n Phone 908.66 2025-09-26 [email protected]\n Table 492.67 2025-08-24 [email protected]\n Very 313.76 2026-01-15 [email protected]\n Family 596.44 2025-12-05 [email protected]\nRemember 463.57 2025-04-12 [email protected]\nEveryone 358.23 2025-11-09 [email protected]\n Manager 153.62 2025-04-26 [email protected]\n Walk 252.55 2025-04-27 [email protected]", "query": "SELECT * FROM dataframe ORDER BY revenue ASC LIMIT 2", "is_scalar": false, "table_format": "to_string", "_time": 0.039543867111206055, "_task": "table_qa", "_level": 2, "_config": {"c": 1.0, "level": 2, "seed": null, "size": null, "num_rows": 20, "num_columns": 4}, "_prompt_tokens": 459, "_cot_tokens": 38} | table_qa | 2 | instruct |
Execute this SQL query on the table:
price,product,company,rating
133.27,Seem,Thompson Ltd,2.5
116.26,Official,Mckenzie-Green,3.6
196.68,Author,Cuevas LLC,2.2
491.33,Improve,"Hansen, Gardner and Morris",2.1
400.72,Argue,"Garrett, Baird and Warren",1.6
483.32,Term,Jordan LLC,5.0
410.96,Head,Kirk LLC,2.7
137.04,Real,Keller and Sons,4.8
434.21,Executive,"Elliott, Hinton and Andrade",3.5
22.91,Relationship,Beard-Tyler,3.3
219.6,Put,Petersen LLC,3.2
24.15,Late,"Jones, Brown and Reynolds",3.3
147.42,Plan,Roach and Sons,2.5
316.3,Occur,"Hernandez, Hobbs and Foster",4.5
168.31,New,"Adams, Marquez and Gutierrez",4.0
138.74,Loss,"Lloyd, Cowan and Robertson",4.5
244.77,Agreement,Hopkins Inc,3.9
32.64,Anyone,Moyer Inc,3.5
14.81,Then,Carter Group,4.0
315.2,Painting,Thompson and Sons,4.9
SQL: SELECT company, SUM(price) as v FROM dataframe GROUP BY company ORDER BY v DESC LIMIT 1
Return result as CSV format (rows separated by newlines, values by commas). | "Hansen, Gardner and Morris",491.33 | {"table": "price,product,company,rating\n133.27,Seem,Thompson Ltd,2.5\n116.26,Official,Mckenzie-Green,3.6\n196.68,Author,Cuevas LLC,2.2\n491.33,Improve,\"Hansen, Gardner and Morris\",2.1\n400.72,Argue,\"Garrett, Baird and Warren\",1.6\n483.32,Term,Jordan LLC,5.0\n410.96,Head,Kirk LLC,2.7\n137.04,Real,Keller and Sons,4.8\n434.21,Executive,\"Elliott, Hinton and Andrade\",3.5\n22.91,Relationship,Beard-Tyler,3.3\n219.6,Put,Petersen LLC,3.2\n24.15,Late,\"Jones, Brown and Reynolds\",3.3\n147.42,Plan,Roach and Sons,2.5\n316.3,Occur,\"Hernandez, Hobbs and Foster\",4.5\n168.31,New,\"Adams, Marquez and Gutierrez\",4.0\n138.74,Loss,\"Lloyd, Cowan and Robertson\",4.5\n244.77,Agreement,Hopkins Inc,3.9\n32.64,Anyone,Moyer Inc,3.5\n14.81,Then,Carter Group,4.0\n315.2,Painting,Thompson and Sons,4.9\n", "query": "SELECT company, SUM(price) as v FROM dataframe GROUP BY company ORDER BY v DESC LIMIT 1", "is_scalar": false, "table_format": "to_csv", "_time": 0.03977394104003906, "_task": "table_qa", "_level": 2, "_config": {"c": 1.0, "level": 2, "seed": null, "size": null, "num_rows": 20, "num_columns": 4}, "_prompt_tokens": 370, "_cot_tokens": 10} | table_qa | 2 | instruct |
List: [20, 10, 19, 6, 20, 11, 20, 5, 20, 10, 18]
How many times does 20 appear? Only return the number. | 4 | {"elements": [20, 10, 19, 6, 20, 11, 20, 5, 20, 10, 18], "target": 20, "_time": 0.00037670135498046875, "_task": "count_elements", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "max_count": 4, "list_size": 11, "domain_size": 40}, "_prompt_tokens": 48, "_cot_tokens": 1} | count_elements | 1 | instruct |
List: ['January 10, 2020', 'January 19, 2020', 'January 05, 2020', 'January 10, 2020', 'January 18, 2020', 'January 14, 2020', 'January 07, 2020', 'January 16, 2020', 'January 10, 2020', 'January 19, 2020', 'January 20, 2020']
How many times does 'January 10, 2020' appear? Only return the number. | 3 | {"elements": ["January 10, 2020", "January 19, 2020", "January 05, 2020", "January 10, 2020", "January 18, 2020", "January 14, 2020", "January 07, 2020", "January 16, 2020", "January 10, 2020", "January 19, 2020", "January 20, 2020"], "target": "January 10, 2020", "_time": 0.0003330707550048828, "_task": "count_elements", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "max_count": 4, "list_size": 11, "domain_size": 40}, "_prompt_tokens": 121, "_cot_tokens": 1} | count_elements | 1 | instruct |
List: [8, 12, 17, 2, 20, 2, 2, 17, 10, 15, 10]
How many times does 18 appear? Only return the number. | 0 | {"elements": [8, 12, 17, 2, 20, 2, 2, 17, 10, 15, 10], "target": 18, "_time": 0.0002703666687011719, "_task": "count_elements", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "max_count": 4, "list_size": 11, "domain_size": 40}, "_prompt_tokens": 48, "_cot_tokens": 1} | count_elements | 1 | instruct |
List: [8, 11, 12, 12, 12, 17, 20, 6, 8, 10, 19]
How many times does 20 appear? Only return the number. | 1 | {"elements": [8, 11, 12, 12, 12, 17, 20, 6, 8, 10, 19], "target": 20, "_time": 0.0002732276916503906, "_task": "count_elements", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "max_count": 4, "list_size": 11, "domain_size": 40}, "_prompt_tokens": 48, "_cot_tokens": 1} | count_elements | 1 | instruct |
List: ['eleven', 'seven', 'four', 'twenty', 'fourteen', 'nineteen', 'nineteen', 'eleven', 'one', 'twelve', 'twenty']
How many times does 'twelve' appear? Only return the number. | 1 | {"elements": ["eleven", "seven", "four", "twenty", "fourteen", "nineteen", "nineteen", "eleven", "one", "twelve", "twenty"], "target": "twelve", "_time": 0.0002925395965576172, "_task": "count_elements", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "max_count": 4, "list_size": 11, "domain_size": 40}, "_prompt_tokens": 60, "_cot_tokens": 1} | count_elements | 1 | instruct |
List: ['twenty', 'fourteen', 'eighteen', 'thirteen', 'seven', 'twelve', 'nine', 'eleven', 'twenty', 'twelve', 'six']
How many times does 'eight' appear? Only return the number. | 0 | {"elements": ["twenty", "fourteen", "eighteen", "thirteen", "seven", "twelve", "nine", "eleven", "twenty", "twelve", "six"], "target": "eight", "_time": 0.000278472900390625, "_task": "count_elements", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "max_count": 4, "list_size": 11, "domain_size": 40}, "_prompt_tokens": 58, "_cot_tokens": 1} | count_elements | 1 | instruct |
List: [12, 7, 17, 11, 19, 10, 16, 5, 15, 19, 2]
How many times does 20 appear? Only return the number. | 0 | {"elements": [12, 7, 17, 11, 19, 10, 16, 5, 15, 19, 2], "target": 20, "_time": 0.00026154518127441406, "_task": "count_elements", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "max_count": 4, "list_size": 11, "domain_size": 40}, "_prompt_tokens": 48, "_cot_tokens": 1} | count_elements | 1 | instruct |
List: ['January 19, 2020', 'January 12, 2020', 'January 06, 2020', 'January 19, 2020', 'January 07, 2020', 'January 16, 2020', 'January 03, 2020', 'January 17, 2020', 'January 19, 2020', 'January 19, 2020', 'January 07, 2020']
How many times does 'January 19, 2020' appear? Only return the number. | 4 | {"elements": ["January 19, 2020", "January 12, 2020", "January 06, 2020", "January 19, 2020", "January 07, 2020", "January 16, 2020", "January 03, 2020", "January 17, 2020", "January 19, 2020", "January 19, 2020", "January 07, 2020"], "target": "January 19, 2020", "_time": 0.0003027915954589844, "_task": "count_elements", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "max_count": 4, "list_size": 11, "domain_size": 40}, "_prompt_tokens": 121, "_cot_tokens": 1} | count_elements | 1 | instruct |
List: ['nine', 'three', 'five', 'twelve', 'three', 'four', 'sixteen', 'nineteen', 'five', 'seventeen', 'nine']
How many times does 'five' appear? Only return the number. | 2 | {"elements": ["nine", "three", "five", "twelve", "three", "four", "sixteen", "nineteen", "five", "seventeen", "nine"], "target": "five", "_time": 0.0002696514129638672, "_task": "count_elements", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "max_count": 4, "list_size": 11, "domain_size": 40}, "_prompt_tokens": 54, "_cot_tokens": 1} | count_elements | 1 | instruct |
List: [19, 19, 15, 6, 5, 7, 16, 19, 6, 7, 12]
How many times does 19 appear? Only return the number. | 3 | {"elements": [19, 19, 15, 6, 5, 7, 16, 19, 6, 7, 12], "target": 19, "_time": 0.0002601146697998047, "_task": "count_elements", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "max_count": 4, "list_size": 11, "domain_size": 40}, "_prompt_tokens": 48, "_cot_tokens": 1} | count_elements | 1 | instruct |
List: [4, 9, 5, 10, 11, 15, 5, 17, 10, 3, 20]
How many times does 3 appear? Only return the number. | 1 | {"elements": [4, 9, 5, 10, 11, 15, 5, 17, 10, 3, 20], "target": 3, "_time": 0.0002582073211669922, "_task": "count_elements", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "max_count": 4, "list_size": 11, "domain_size": 40}, "_prompt_tokens": 48, "_cot_tokens": 1} | count_elements | 1 | instruct |
List: ['January 05, 2020', 'January 03, 2020', 'January 09, 2020', 'January 09, 2020', 'January 05, 2020', 'January 18, 2020', 'January 19, 2020', 'January 07, 2020', 'January 09, 2020', 'January 09, 2020', 'January 06, 2020']
How many times does 'January 09, 2020' appear? Only return the number. | 4 | {"elements": ["January 05, 2020", "January 03, 2020", "January 09, 2020", "January 09, 2020", "January 05, 2020", "January 18, 2020", "January 19, 2020", "January 07, 2020", "January 09, 2020", "January 09, 2020", "January 06, 2020"], "target": "January 09, 2020", "_time": 0.00030040740966796875, "_task": "count_elements", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "max_count": 4, "list_size": 11, "domain_size": 40}, "_prompt_tokens": 121, "_cot_tokens": 1} | count_elements | 1 | instruct |
List: [18, 1, 19, 11, 19, 12, 20, 18, 3, 20, 2]
How many times does 20 appear? Only return the number. | 2 | {"elements": [18, 1, 19, 11, 19, 12, 20, 18, 3, 20, 2], "target": 20, "_time": 0.0002689361572265625, "_task": "count_elements", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "max_count": 4, "list_size": 11, "domain_size": 40}, "_prompt_tokens": 48, "_cot_tokens": 1} | count_elements | 1 | instruct |
List: [7, 3, 19, 9, 5, 7, 10, 20, 3, 5, 13]
How many times does 17 appear? Only return the number. | 0 | {"elements": [7, 3, 19, 9, 5, 7, 10, 20, 3, 5, 13], "target": 17, "_time": 0.00025773048400878906, "_task": "count_elements", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "max_count": 4, "list_size": 11, "domain_size": 40}, "_prompt_tokens": 48, "_cot_tokens": 1} | count_elements | 1 | instruct |
List: ['January 15, 2020', 'January 16, 2020', 'January 15, 2020', 'January 10, 2020', 'January 07, 2020', 'January 01, 2020', 'January 13, 2020', 'January 02, 2020', 'January 01, 2020', 'January 13, 2020', 'January 03, 2020']
How many times does 'January 13, 2020' appear? Only return the number. | 2 | {"elements": ["January 15, 2020", "January 16, 2020", "January 15, 2020", "January 10, 2020", "January 07, 2020", "January 01, 2020", "January 13, 2020", "January 02, 2020", "January 01, 2020", "January 13, 2020", "January 03, 2020"], "target": "January 13, 2020", "_time": 0.00029754638671875, "_task": "count_elements", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "max_count": 4, "list_size": 11, "domain_size": 40}, "_prompt_tokens": 121, "_cot_tokens": 1} | count_elements | 1 | instruct |
List: ['five', 'fourteen', 'eighteen', 'six', 'twenty', 'eighteen', 'two', 'seven', 'five', 'eight', 'ten']
How many times does 'five' appear? Only return the number. | 2 | {"elements": ["five", "fourteen", "eighteen", "six", "twenty", "eighteen", "two", "seven", "five", "eight", "ten"], "target": "five", "_time": 0.0002701282501220703, "_task": "count_elements", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "max_count": 4, "list_size": 11, "domain_size": 40}, "_prompt_tokens": 55, "_cot_tokens": 1} | count_elements | 1 | instruct |
Convert the following table from to_html to to_yaml.
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>rating</th>
<th>country</th>
</tr>
</thead>
<tbody>
<tr>
<td>3.0</td>
<td>Uganda</td>
</tr>
<tr>
<td>2.8</td>
<td>Falkland Islands (Malvinas)</td>
</tr>
<tr>
<td>4.2</td>
<td>Lebanon</td>
</tr>
<tr>
<td>2.6</td>
<td>Panama</td>
</tr>
<tr>
<td>2.2</td>
<td>Ecuador</td>
</tr>
</tbody>
</table>
Output only the converted table. | - country: Uganda
rating: 3.0
- country: Falkland Islands (Malvinas)
rating: 2.8
- country: Lebanon
rating: 4.2
- country: Panama
rating: 2.6
- country: Ecuador
rating: 2.2
| {"source_table": "<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th>rating</th>\n <th>country</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td>3.0</td>\n <td>Uganda</td>\n </tr>\n <tr>\n <td>2.8</td>\n <td>Falkland Islands (Malvinas)</td>\n </tr>\n <tr>\n <td>4.2</td>\n <td>Lebanon</td>\n </tr>\n <tr>\n <td>2.6</td>\n <td>Panama</td>\n </tr>\n <tr>\n <td>2.2</td>\n <td>Ecuador</td>\n </tr>\n </tbody>\n</table>", "source_format": "to_html", "target_format": "to_yaml", "_time": 0.002297639846801758, "_task": "table_conversion", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_rows": 5, "num_columns": 2}, "_prompt_tokens": 216, "_cot_tokens": 71} | table_conversion | 0 | instruct |
Convert the following table from to_json to to_html.
[
{
"company":"Ramsey Ltd",
"email":"[email protected]"
},
{
"company":"Mooney LLC",
"email":"[email protected]"
},
{
"company":"Giles, Riley and Greene",
"email":"[email protected]"
},
{
"company":"Gonzalez-Torres",
"email":"[email protected]"
},
{
"company":"Ramos Ltd",
"email":"[email protected]"
}
]
Output only the converted table. | <table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>company</th>
<th>email</th>
</tr>
</thead>
<tbody>
<tr>
<td>Ramsey Ltd</td>
<td>[email protected]</td>
</tr>
<tr>
<td>Mooney LLC</td>
<td>[email protected]</td>
</tr>
<tr>
<td>Giles, Riley and Greene</td>
<td>[email protected]</td>
</tr>
<tr>
<td>Gonzalez-Torres</td>
<td>[email protected]</td>
</tr>
<tr>
<td>Ramos Ltd</td>
<td>[email protected]</td>
</tr>
</tbody>
</table> | {"source_table": "[\n {\n \"company\":\"Ramsey Ltd\",\n \"email\":\"[email protected]\"\n },\n {\n \"company\":\"Mooney LLC\",\n \"email\":\"[email protected]\"\n },\n {\n \"company\":\"Giles, Riley and Greene\",\n \"email\":\"[email protected]\"\n },\n {\n \"company\":\"Gonzalez-Torres\",\n \"email\":\"[email protected]\"\n },\n {\n \"company\":\"Ramos Ltd\",\n \"email\":\"[email protected]\"\n }\n]", "source_format": "to_json", "target_format": "to_html", "_time": 0.0024688243865966797, "_task": "table_conversion", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_rows": 5, "num_columns": 2}, "_prompt_tokens": 137, "_cot_tokens": 217} | table_conversion | 0 | instruct |
Convert the following table from to_json to to_latex.
[
{
"city":"Leonardton",
"company":"Miller-Vaughn"
},
{
"city":"Millertown",
"company":"Chandler-Turner"
},
{
"city":"West Zachary",
"company":"Kennedy Inc"
},
{
"city":"South Tiffany",
"company":"Kelly-Williams"
},
{
"city":"North Daniellechester",
"company":"Johnson PLC"
}
]
Output only the converted table. | \begin{tabular}{ll}
city & company \\
Leonardton & Miller-Vaughn \\
Millertown & Chandler-Turner \\
West Zachary & Kennedy Inc \\
South Tiffany & Kelly-Williams \\
North Daniellechester & Johnson PLC \\
\end{tabular}
| {"source_table": "[\n {\n \"city\":\"Leonardton\",\n \"company\":\"Miller-Vaughn\"\n },\n {\n \"city\":\"Millertown\",\n \"company\":\"Chandler-Turner\"\n },\n {\n \"city\":\"West Zachary\",\n \"company\":\"Kennedy Inc\"\n },\n {\n \"city\":\"South Tiffany\",\n \"company\":\"Kelly-Williams\"\n },\n {\n \"city\":\"North Daniellechester\",\n \"company\":\"Johnson PLC\"\n }\n]", "source_format": "to_json", "target_format": "to_latex", "_time": 0.0022745132446289062, "_task": "table_conversion", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_rows": 5, "num_columns": 2}, "_prompt_tokens": 121, "_cot_tokens": 62} | table_conversion | 0 | instruct |
Convert the following table from to_yaml to to_html.
- country: Canada
product: Age
- country: Lebanon
product: Himself
- country: Saint Helena
product: Technology
- country: Guinea-Bissau
product: Apply
- country: Cape Verde
product: View
Output only the converted table. | <table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>country</th>
<th>product</th>
</tr>
</thead>
<tbody>
<tr>
<td>Canada</td>
<td>Age</td>
</tr>
<tr>
<td>Lebanon</td>
<td>Himself</td>
</tr>
<tr>
<td>Saint Helena</td>
<td>Technology</td>
</tr>
<tr>
<td>Guinea-Bissau</td>
<td>Apply</td>
</tr>
<tr>
<td>Cape Verde</td>
<td>View</td>
</tr>
</tbody>
</table> | {"source_table": "- country: Canada\n product: Age\n- country: Lebanon\n product: Himself\n- country: Saint Helena\n product: Technology\n- country: Guinea-Bissau\n product: Apply\n- country: Cape Verde\n product: View\n", "source_format": "to_yaml", "target_format": "to_html", "_time": 0.0019974708557128906, "_task": "table_conversion", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_rows": 5, "num_columns": 2}, "_prompt_tokens": 72, "_cot_tokens": 188} | table_conversion | 0 | instruct |
Convert the following table from to_latex to to_markdown.
\begin{tabular}{rr}
qty & price \\
684 & 95.440000 \\
376 & 85.990000 \\
841 & 81.330000 \\
823 & 167.080000 \\
615 & 199.300000 \\
\end{tabular}
Output only the converted table. | | qty | price |
|------:|--------:|
| 684 | 95.44 |
| 376 | 85.99 |
| 841 | 81.33 |
| 823 | 167.08 |
| 615 | 199.3 | | {"source_table": "\\begin{tabular}{rr}\nqty & price \\\\\n684 & 95.440000 \\\\\n376 & 85.990000 \\\\\n841 & 81.330000 \\\\\n823 & 167.080000 \\\\\n615 & 199.300000 \\\\\n\\end{tabular}\n", "source_format": "to_latex", "target_format": "to_markdown", "_time": 0.0017614364624023438, "_task": "table_conversion", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_rows": 5, "num_columns": 2}, "_prompt_tokens": 83, "_cot_tokens": 70} | table_conversion | 0 | instruct |
Convert the following table from to_markdown to to_csv.
| date | qty |
|:-----------|------:|
| 2025-07-23 | 708 |
| 2025-11-12 | 440 |
| 2025-11-13 | 86 |
| 2025-11-18 | 470 |
| 2026-01-10 | 227 |
Output only the converted table. | date,qty
2025-07-23,708
2025-11-12,440
2025-11-13,86
2025-11-18,470
2026-01-10,227
| {"source_table": "| date | qty |\n|:-----------|------:|\n| 2025-07-23 | 708 |\n| 2025-11-12 | 440 |\n| 2025-11-13 | 86 |\n| 2025-11-18 | 470 |\n| 2026-01-10 | 227 |", "source_format": "to_markdown", "target_format": "to_csv", "_time": 0.0013582706451416016, "_task": "table_conversion", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_rows": 5, "num_columns": 2}, "_prompt_tokens": 97, "_cot_tokens": 49} | table_conversion | 0 | instruct |
Convert the following table from to_json to to_csv.
[
{
"rating":1.6,
"country":"Albania"
},
{
"rating":4.1,
"country":"Suriname"
},
{
"rating":4.8,
"country":"North Macedonia"
},
{
"rating":1.7,
"country":"United States of America"
},
{
"rating":1.3,
"country":"Reunion"
}
]
Output only the converted table. | rating,country
1.6,Albania
4.1,Suriname
4.8,North Macedonia
1.7,United States of America
1.3,Reunion
| {"source_table": "[\n {\n \"rating\":1.6,\n \"country\":\"Albania\"\n },\n {\n \"rating\":4.1,\n \"country\":\"Suriname\"\n },\n {\n \"rating\":4.8,\n \"country\":\"North Macedonia\"\n },\n {\n \"rating\":1.7,\n \"country\":\"United States of America\"\n },\n {\n \"rating\":1.3,\n \"country\":\"Reunion\"\n }\n]", "source_format": "to_json", "target_format": "to_csv", "_time": 0.0008890628814697266, "_task": "table_conversion", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_rows": 5, "num_columns": 2}, "_prompt_tokens": 117, "_cot_tokens": 42} | table_conversion | 0 | instruct |
Convert the following table from to_yaml to to_markdown.
- company: Brown-Robbins
email: [email protected]
- company: Zimmerman Group
email: [email protected]
- company: Walker, Daniels and Williamson
email: [email protected]
- company: Sharp and Sons
email: [email protected]
- company: Odom, Mckee and Yu
email: [email protected]
Output only the converted table. | | company | email |
|:-------------------------------|:-------------------------|
| Brown-Robbins | [email protected] |
| Zimmerman Group | [email protected] |
| Walker, Daniels and Williamson | [email protected] |
| Sharp and Sons | [email protected] |
| Odom, Mckee and Yu | [email protected] | | {"source_table": "- company: Brown-Robbins\n email: [email protected]\n- company: Zimmerman Group\n email: [email protected]\n- company: Walker, Daniels and Williamson\n email: [email protected]\n- company: Sharp and Sons\n email: [email protected]\n- company: Odom, Mckee and Yu\n email: [email protected]\n", "source_format": "to_yaml", "target_format": "to_markdown", "_time": 0.0026884078979492188, "_task": "table_conversion", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_rows": 5, "num_columns": 2}, "_prompt_tokens": 105, "_cot_tokens": 86} | table_conversion | 0 | instruct |
Convert the following table from to_html to to_json.
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>rating</th>
<th>country</th>
</tr>
</thead>
<tbody>
<tr>
<td>1.2</td>
<td>Christmas Island</td>
</tr>
<tr>
<td>1.5</td>
<td>Holy See (Vatican City State)</td>
</tr>
<tr>
<td>3.5</td>
<td>Congo</td>
</tr>
<tr>
<td>3.8</td>
<td>Pitcairn Islands</td>
</tr>
<tr>
<td>1.2</td>
<td>Kazakhstan</td>
</tr>
</tbody>
</table>
Output only the converted table. | [
{
"rating":1.2,
"country":"Christmas Island"
},
{
"rating":1.5,
"country":"Holy See (Vatican City State)"
},
{
"rating":3.5,
"country":"Congo"
},
{
"rating":3.8,
"country":"Pitcairn Islands"
},
{
"rating":1.2,
"country":"Kazakhstan"
}
] | {"source_table": "<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th>rating</th>\n <th>country</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td>1.2</td>\n <td>Christmas Island</td>\n </tr>\n <tr>\n <td>1.5</td>\n <td>Holy See (Vatican City State)</td>\n </tr>\n <tr>\n <td>3.5</td>\n <td>Congo</td>\n </tr>\n <tr>\n <td>3.8</td>\n <td>Pitcairn Islands</td>\n </tr>\n <tr>\n <td>1.2</td>\n <td>Kazakhstan</td>\n </tr>\n </tbody>\n</table>", "source_format": "to_html", "target_format": "to_json", "_time": 0.001344919204711914, "_task": "table_conversion", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_rows": 5, "num_columns": 2}, "_prompt_tokens": 219, "_cot_tokens": 106} | table_conversion | 0 | instruct |
Convert the following table from to_csv to to_latex.
revenue,date
267.43,2025-07-15
405.61,2025-09-24
25.8,2025-08-17
117.23,2025-09-05
259.78,2026-02-12
Output only the converted table. | \begin{tabular}{rl}
revenue & date \\
267.430000 & 2025-07-15 \\
405.610000 & 2025-09-24 \\
25.800000 & 2025-08-17 \\
117.230000 & 2025-09-05 \\
259.780000 & 2026-02-12 \\
\end{tabular}
| {"source_table": "revenue,date\n267.43,2025-07-15\n405.61,2025-09-24\n25.8,2025-08-17\n117.23,2025-09-05\n259.78,2026-02-12\n", "source_format": "to_csv", "target_format": "to_latex", "_time": 0.0017611980438232422, "_task": "table_conversion", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_rows": 5, "num_columns": 2}, "_prompt_tokens": 77, "_cot_tokens": 90} | table_conversion | 0 | instruct |
Convert the following table from to_string to to_json.
company city
Franklin and Sons North Monique
Chambers, Rivera and Velez Harrellton
Johnson PLC Davidhaven
Martin and Sons North Alexandramouth
Thompson Group North Victorbury
Output only the converted table. | [
{
"company":"Franklin and Sons",
"city":"North Monique"
},
{
"company":"Chambers, Rivera and Velez",
"city":"Harrellton"
},
{
"company":"Johnson PLC",
"city":"Davidhaven"
},
{
"company":"Martin and Sons",
"city":"North Alexandramouth"
},
{
"company":"Thompson Group",
"city":"North Victorbury"
}
] | {"source_table": " company city\n Franklin and Sons North Monique\nChambers, Rivera and Velez Harrellton\n Johnson PLC Davidhaven\n Martin and Sons North Alexandramouth\n Thompson Group North Victorbury", "source_format": "to_string", "target_format": "to_json", "_time": 0.0018515586853027344, "_task": "table_conversion", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_rows": 5, "num_columns": 2}, "_prompt_tokens": 69, "_cot_tokens": 110} | table_conversion | 0 | instruct |
Convert the following table from to_string to to_markdown.
date company
2025-07-13 Lambert, Winters and Davis
2025-04-02 Lee-Johnson
2025-10-20 Adams LLC
2025-03-26 Curry, Smith and Turner
2025-06-08 Mills-Navarro
Output only the converted table. | | date | company |
|:-----------|:---------------------------|
| 2025-07-13 | Lambert, Winters and Davis |
| 2025-04-02 | Lee-Johnson |
| 2025-10-20 | Adams LLC |
| 2025-03-26 | Curry, Smith and Turner |
| 2025-06-08 | Mills-Navarro | | {"source_table": " date company\n2025-07-13 Lambert, Winters and Davis\n2025-04-02 Lee-Johnson\n2025-10-20 Adams LLC\n2025-03-26 Curry, Smith and Turner\n2025-06-08 Mills-Navarro", "source_format": "to_string", "target_format": "to_markdown", "_time": 0.002052783966064453, "_task": "table_conversion", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_rows": 5, "num_columns": 2}, "_prompt_tokens": 82, "_cot_tokens": 89} | table_conversion | 0 | instruct |
Convert the following table from to_csv to to_json.
qty,job
546,Diagnostic radiographer
728,Tour manager
772,"Accountant, chartered public finance"
765,Dealer
859,Insurance account manager
Output only the converted table. | [
{
"qty":546,
"job":"Diagnostic radiographer"
},
{
"qty":728,
"job":"Tour manager"
},
{
"qty":772,
"job":"Accountant, chartered public finance"
},
{
"qty":765,
"job":"Dealer"
},
{
"qty":859,
"job":"Insurance account manager"
}
] | {"source_table": "qty,job\n546,Diagnostic radiographer\n728,Tour manager\n772,\"Accountant, chartered public finance\"\n765,Dealer\n859,Insurance account manager\n", "source_format": "to_csv", "target_format": "to_json", "_time": 0.0008571147918701172, "_task": "table_conversion", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_rows": 5, "num_columns": 2}, "_prompt_tokens": 52, "_cot_tokens": 93} | table_conversion | 0 | instruct |
Convert the following table from to_string to to_markdown.
revenue rating
215.62 3.1
398.40 3.7
787.55 3.6
865.22 1.4
114.67 1.5
Output only the converted table. | | revenue | rating |
|----------:|---------:|
| 215.62 | 3.1 |
| 398.4 | 3.7 |
| 787.55 | 3.6 |
| 865.22 | 1.4 |
| 114.67 | 1.5 | | {"source_table": " revenue rating\n 215.62 3.1\n 398.40 3.7\n 787.55 3.6\n 865.22 1.4\n 114.67 1.5", "source_format": "to_string", "target_format": "to_markdown", "_time": 0.001470327377319336, "_task": "table_conversion", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_rows": 5, "num_columns": 2}, "_prompt_tokens": 77, "_cot_tokens": 80} | table_conversion | 0 | instruct |
Convert the following table from to_latex to to_html.
\begin{tabular}{ll}
city & country \\
South Charles & Sri Lanka \\
Walkerton & Indonesia \\
Lake Jean & Algeria \\
Brucemouth & Maldives \\
Schneiderchester & Saint Martin \\
\end{tabular}
Output only the converted table. | <table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>city</th>
<th>country</th>
</tr>
</thead>
<tbody>
<tr>
<td>South Charles</td>
<td>Sri Lanka</td>
</tr>
<tr>
<td>Walkerton</td>
<td>Indonesia</td>
</tr>
<tr>
<td>Lake Jean</td>
<td>Algeria</td>
</tr>
<tr>
<td>Brucemouth</td>
<td>Maldives</td>
</tr>
<tr>
<td>Schneiderchester</td>
<td>Saint Martin</td>
</tr>
</tbody>
</table> | {"source_table": "\\begin{tabular}{ll}\ncity & country \\\\\nSouth Charles & Sri Lanka \\\\\nWalkerton & Indonesia \\\\\nLake Jean & Algeria \\\\\nBrucemouth & Maldives \\\\\nSchneiderchester & Saint Martin \\\\\n\\end{tabular}\n", "source_format": "to_latex", "target_format": "to_html", "_time": 0.0021474361419677734, "_task": "table_conversion", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_rows": 5, "num_columns": 2}, "_prompt_tokens": 71, "_cot_tokens": 191} | table_conversion | 0 | instruct |
Convert the following table from to_csv to to_yaml.
job,city
Media planner,South Michaelmouth
Ambulance person,Gomezton
"Surveyor, insurance",Raymondberg
Operations geologist,New Michelleview
Multimedia programmer,Heatherchester
Output only the converted table. | - city: South Michaelmouth
job: Media planner
- city: Gomezton
job: Ambulance person
- city: Raymondberg
job: Surveyor, insurance
- city: New Michelleview
job: Operations geologist
- city: Heatherchester
job: Multimedia programmer
| {"source_table": "job,city\nMedia planner,South Michaelmouth\nAmbulance person,Gomezton\n\"Surveyor, insurance\",Raymondberg\nOperations geologist,New Michelleview\nMultimedia programmer,Heatherchester\n", "source_format": "to_csv", "target_format": "to_yaml", "_time": 0.0018086433410644531, "_task": "table_conversion", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_rows": 5, "num_columns": 2}, "_prompt_tokens": 61, "_cot_tokens": 66} | table_conversion | 0 | instruct |
System:
P(X_1) = {'0': 0.5, '1': 0.5}
P(X_2|X_1=0) = {'0': 0.6, '1': 0.4}
P(X_2|X_1=1) = {'0': 0.3, '1': 0.7}
P(X_0) = {'0': 0.6, '1': 0.4}
Observed conditions:
Doing/Imposing that the state X_1 is equal to 1
Task: Compute probability distribution for X_0 (possible values: [0, 1]).
Output: Python dict mapping each value to its probability, rounded to 1 decimals.
Example: {0: 0.1, 1: 0.9} | {0: 0.6, 1: 0.4} | {"target_var_values": [0, 1], "bif_description": "// CANONICAL\n// variable: X_1\n// state_names: {'X_1': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_2\n// state_names: {'X_2': [0, 1], 'X_1': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_0\n// state_names: {'X_0': [0, 1]}\n// type: TabularCPD\n\nnetwork unknown {\n}\nvariable X_0 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_1 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_2 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nprobability ( X_0 ) {\n table 0.6, 0.4 ;\n}\nprobability ( X_1 ) {\n table 0.5, 0.5 ;\n}\nprobability ( X_2 | X_1 ) {\n ( 0 ) 0.6, 0.4;\n ( 1 ) 0.3, 0.7;\n\n}\n", "scenario": "Doing/Imposing that the state X_1 is equal to 1", "target": "X_0", "variables": ["X_1", "X_2", "X_0"], "n_round": 1, "cot": "Goal: Compute Causal Effect: P(X_0 | do(X_1=1))\nSurgery: P(X_1)= Point Mass at X_1=1.\nResult: P(X_0) = {0: 0.6, 1: 0.4}", "_time": 1.8128530979156494, "_task": "bayesian_intervention", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "n_nodes": 4, "max_domain_size": 3, "edge_prob": 0.5, "graph_generation_mode": "erdos", "n_round": 2, "cpt_relative_threshold": 0.5, "cot_scientific_notation": false, "is_verbose": false, "concise_cot": true}, "_prompt_tokens": 179, "_cot_tokens": 75} | bayesian_intervention | 1 | instruct |
System:
P(X_1) = {'0': 0.22, '1': 0.78}
P(X_2|X_1=0) = {'0': 0.25, '1': 0.16, '2': 0.59}
P(X_2|X_1=1) = {'0': 0.3, '1': 0.39, '2': 0.31}
P(X_0) = {'0': 0.84, '1': 0.08, '2': 0.08}
Observed conditions:
Doing/Imposing that the state X_2 is equal to 1
Task: Compute probability distribution for X_0 (possible values: [0, 1, 2]).
Output: Python dict mapping each value to its probability, rounded to 2 decimals.
Example: {0: 0.12, 1: 0.88} | {0: 0.84, 1: 0.08, 2: 0.08} | {"target_var_values": [0, 1, 2], "bif_description": "// CANONICAL\n// variable: X_1\n// state_names: {'X_1': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_2\n// state_names: {'X_2': [0, 1, 2], 'X_1': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_0\n// state_names: {'X_0': [0, 1, 2]}\n// type: TabularCPD\n\nnetwork unknown {\n}\nvariable X_0 {\n type discrete [ 3 ] { 0, 1, 2 };\n property weight = None ;\n}\nvariable X_1 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_2 {\n type discrete [ 3 ] { 0, 1, 2 };\n property weight = None ;\n}\nprobability ( X_0 ) {\n table 0.84, 0.08, 0.08 ;\n}\nprobability ( X_1 ) {\n table 0.22, 0.78 ;\n}\nprobability ( X_2 | X_1 ) {\n ( 0 ) 0.25, 0.16, 0.59;\n ( 1 ) 0.3, 0.39, 0.31;\n\n}\n", "scenario": "Doing/Imposing that the state X_2 is equal to 1", "target": "X_0", "variables": ["X_1", "X_2", "X_0"], "n_round": 2, "cot": "Goal: Compute Causal Effect: P(X_0 | do(X_2=1))\nSurgery: Cut incoming edges to intervened node 'X_2': ['X_1'] -> X_2; P(X_2)= Point Mass at X_2=1.\nResult: P(X_0) = {0: 0.84, 1: 0.08, 2: 0.08}", "_time": 1.520015001296997, "_task": "bayesian_intervention", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "n_nodes": 3, "max_domain_size": 3, "edge_prob": 0.5, "graph_generation_mode": "erdos", "n_round": 2, "cpt_relative_threshold": 0.5, "cot_scientific_notation": false, "is_verbose": false, "concise_cot": true}, "_prompt_tokens": 206, "_cot_tokens": 113} | bayesian_intervention | 1 | instruct |
System:
P(X_0) = {'0': 0.5, '1': 0.5}
P(X_1|X_0=0) = {'0': 0.6, '1': 0.4}
P(X_1|X_0=1) = {'0': 0.7, '1': 0.3}
P(X_2|X_0=0) = {'0': 0.6, '1': 0.4}
P(X_2|X_0=1) = {'0': 0.5, '1': 0.5}
Observed conditions:
Doing/Imposing that the state X_1 is equal to 1. Observing/Knowing that the state X_2 is equal to 1
Task: Compute probability distribution for X_0 (possible values: [0, 1]).
Output: Python dict mapping each value to its probability, rounded to 1 decimals.
Example: {0: 0.1, 1: 0.9} | {0: 0.4, 1: 0.6} | {"target_var_values": [0, 1], "bif_description": "// CANONICAL\n// variable: X_0\n// state_names: {'X_0': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_1\n// state_names: {'X_1': [0, 1], 'X_0': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_2\n// state_names: {'X_2': [0, 1], 'X_0': [0, 1]}\n// type: TabularCPD\n\nnetwork unknown {\n}\nvariable X_0 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_1 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_2 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nprobability ( X_0 ) {\n table 0.5, 0.5 ;\n}\nprobability ( X_1 | X_0 ) {\n ( 0 ) 0.6, 0.4;\n ( 1 ) 0.7, 0.3;\n\n}\nprobability ( X_2 | X_0 ) {\n ( 0 ) 0.6, 0.4;\n ( 1 ) 0.5, 0.5;\n\n}\n", "scenario": "Doing/Imposing that the state X_1 is equal to 1. Observing/Knowing that the state X_2 is equal to 1", "target": "X_0", "variables": ["X_0", "X_1", "X_2"], "n_round": 1, "cot": "Goal: Compute Causal Effect: P(X_0 | do(X_1=1), X_2=1)\nSurgery: Cut incoming edges to intervened node 'X_1': ['X_0'] -> X_1; P(X_1)= Point Mass at X_1=1.\nNormalize (sum=0.4) -> P(X_0 | X_2=1) = {0: 0.4, 1: 0.6}", "_time": 1.5496621131896973, "_task": "bayesian_intervention", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "n_nodes": 3, "max_domain_size": 2, "edge_prob": 0.5, "graph_generation_mode": "erdos", "n_round": 1, "cpt_relative_threshold": 0.5, "cot_scientific_notation": false, "is_verbose": false, "concise_cot": true}, "_prompt_tokens": 230, "_cot_tokens": 116} | bayesian_intervention | 1 | instruct |
System:
P(X_0) = {'0': 0.54, '1': 0.46}
P(X_1|X_0=0) = {'0': 0.38, '1': 0.62}
P(X_1|X_0=1) = {'0': 0.01, '1': 0.99}
P(X_2) = {'0': 0.78, '1': 0.02, '2': 0.2}
Observed conditions:
Doing/Imposing that the state X_1 is equal to 1. Observing/Knowing that the state X_0 is equal to 1
Task: Compute probability distribution for X_2 (possible values: [0, 1, 2]).
Output: Python dict mapping each value to its probability, rounded to 2 decimals.
Example: {0: 0.12, 1: 0.88} | {0: 0.78, 1: 0.02, 2: 0.2} | {"target_var_values": [0, 1, 2], "bif_description": "// CANONICAL\n// variable: X_0\n// state_names: {'X_0': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_1\n// state_names: {'X_1': [0, 1], 'X_0': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_2\n// state_names: {'X_2': [0, 1, 2]}\n// type: TabularCPD\n\nnetwork unknown {\n}\nvariable X_0 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_1 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_2 {\n type discrete [ 3 ] { 0, 1, 2 };\n property weight = None ;\n}\nprobability ( X_0 ) {\n table 0.54, 0.46 ;\n}\nprobability ( X_1 | X_0 ) {\n ( 0 ) 0.38, 0.62;\n ( 1 ) 0.01, 0.99;\n\n}\nprobability ( X_2 ) {\n table 0.78, 0.02, 0.2 ;\n}\n", "scenario": "Doing/Imposing that the state X_1 is equal to 1. Observing/Knowing that the state X_0 is equal to 1", "target": "X_2", "variables": ["X_0", "X_1", "X_2"], "n_round": 2, "cot": "Goal: Compute Causal Effect: P(X_2 | do(X_1=1), X_0=1)\nSurgery: Cut incoming edges to intervened node 'X_1': ['X_0'] -> X_1; P(X_1)= Point Mass at X_1=1.\nResult: P(X_2) = {0: 0.78, 1: 0.02, 2: 0.2}", "_time": 1.5672757625579834, "_task": "bayesian_intervention", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "n_nodes": 4, "max_domain_size": 3, "edge_prob": 0.5, "graph_generation_mode": "erdos", "n_round": 1, "cpt_relative_threshold": 0.5, "cot_scientific_notation": false, "is_verbose": false, "concise_cot": true}, "_prompt_tokens": 206, "_cot_tokens": 119} | bayesian_intervention | 1 | instruct |
System:
P(X_0) = {'0': 0.16, '1': 0.84}
P(X_1|X_0=0) = {'0': 0.48, '1': 0.52}
P(X_1|X_0=1) = {'0': 0.8, '1': 0.2}
P(X_2|X_0=0, X_1=0) = {'0': 0.52, '1': 0.48}
P(X_2|X_0=0, X_1=1) = {'0': 0.57, '1': 0.43}
P(X_2|X_0=1, X_1=0) = {'0': 0.33, '1': 0.67}
P(X_2|X_0=1, X_1=1) = {'0': 0.63, '1': 0.37}
P(X_3|X_0=0, X_1=0) = {'0': 0.46, '1': 0.54}
P(X_3|X_0=0, X_1=1) = {'0': 0.53, '1': 0.47}
P(X_3|X_0=1, X_1=0) = {'0': 0.55, '1': 0.45}
P(X_3|X_0=1, X_1=1) = {'0': 0.1, '1': 0.9}
Observed conditions:
Doing/Imposing that the state X_2 is equal to 0. Observing/Knowing that the state X_3 is equal to 0
Task: Compute probability distribution for X_0 (possible values: [0, 1]).
Output: Python dict mapping each value to its probability, rounded to 2 decimals.
Example: {0: 0.12, 1: 0.88} | {0: 0.17, 1: 0.83} | {"target_var_values": [0, 1], "bif_description": "// CANONICAL\n// variable: X_0\n// state_names: {'X_0': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_1\n// state_names: {'X_1': [0, 1], 'X_0': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_2\n// state_names: {'X_2': [0, 1], 'X_0': [0, 1], 'X_1': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_3\n// state_names: {'X_3': [0, 1], 'X_0': [0, 1], 'X_1': [0, 1]}\n// type: TabularCPD\n\nnetwork unknown {\n}\nvariable X_0 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_1 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_2 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_3 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nprobability ( X_0 ) {\n table 0.16, 0.84 ;\n}\nprobability ( X_1 | X_0 ) {\n ( 0 ) 0.48, 0.52;\n ( 1 ) 0.8, 0.2;\n\n}\nprobability ( X_2 | X_0, X_1 ) {\n ( 0, 0 ) 0.52, 0.48;\n ( 0, 1 ) 0.57, 0.43;\n ( 1, 0 ) 0.33, 0.67;\n ( 1, 1 ) 0.63, 0.37;\n\n}\nprobability ( X_3 | X_0, X_1 ) {\n ( 0, 0 ) 0.46, 0.54;\n ( 0, 1 ) 0.53, 0.47;\n ( 1, 0 ) 0.55, 0.45;\n ( 1, 1 ) 0.1, 0.9;\n\n}\n", "scenario": "Doing/Imposing that the state X_2 is equal to 0. Observing/Knowing that the state X_3 is equal to 0", "target": "X_0", "variables": ["X_0", "X_1", "X_2", "X_3"], "n_round": 2, "cot": "Goal: Compute Causal Effect: P(X_0 | do(X_2=0), X_3=0)\nSurgery: Cut incoming edges to intervened node 'X_2': ['X_0', 'X_1'] -> X_2; P(X_2)= Point Mass at X_2=0.\nElim order: ['X_1']\nSum out X_1 -> P(X_3=0 | X_0) = {0: 0.5, 1: 0.46}\nNormalize (sum=0.47) -> P(X_0 | X_3=0) = {0: 0.17, 1: 0.83}", "_time": 1.5757648944854736, "_task": "bayesian_intervention", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "n_nodes": 3, "max_domain_size": 3, "edge_prob": 0.5, "graph_generation_mode": "erdos", "n_round": 1, "cpt_relative_threshold": 0.5, "cot_scientific_notation": false, "is_verbose": false, "concise_cot": true}, "_prompt_tokens": 452, "_cot_tokens": 164} | bayesian_intervention | 1 | instruct |
System:
P(X_0) = {'0': 0.7, '1': 0.3}
P(X_1) = {'0': 0.4, '1': 0.6}
P(X_2) = {'0': 0.5, '1': 0.5}
Observed conditions:
Doing/Imposing that the state X_1 is equal to 0. Observing/Knowing that the state X_2 is equal to 1
Task: Compute probability distribution for X_0 (possible values: [0, 1]).
Output: Python dict mapping each value to its probability, rounded to 1 decimals.
Example: {0: 0.1, 1: 0.9} | {0: 0.7, 1: 0.3} | {"target_var_values": [0, 1], "bif_description": "// CANONICAL\n// variable: X_0\n// state_names: {'X_0': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_1\n// state_names: {'X_1': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_2\n// state_names: {'X_2': [0, 1]}\n// type: TabularCPD\n\nnetwork unknown {\n}\nvariable X_0 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_1 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_2 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nprobability ( X_0 ) {\n table 0.7, 0.3 ;\n}\nprobability ( X_1 ) {\n table 0.4, 0.6 ;\n}\nprobability ( X_2 ) {\n table 0.5, 0.5 ;\n}\n", "scenario": "Doing/Imposing that the state X_1 is equal to 0. Observing/Knowing that the state X_2 is equal to 1", "target": "X_0", "variables": ["X_0", "X_1", "X_2"], "n_round": 1, "cot": "Goal: Compute Causal Effect: P(X_0 | do(X_1=0), X_2=1)\nSurgery: P(X_1)= Point Mass at X_1=0.\nResult: P(X_0) = {0: 0.7, 1: 0.3}", "_time": 1.5366177558898926, "_task": "bayesian_intervention", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "n_nodes": 4, "max_domain_size": 3, "edge_prob": 0.5, "graph_generation_mode": "erdos", "n_round": 2, "cpt_relative_threshold": 0.5, "cot_scientific_notation": false, "is_verbose": false, "concise_cot": true}, "_prompt_tokens": 160, "_cot_tokens": 81} | bayesian_intervention | 1 | instruct |
System:
P(X_0) = {'0': 0.22, '1': 0.78}
P(X_1) = {'0': 0.04, '1': 0.96}
P(X_2) = {'0': 0.76, '1': 0.24}
Observed conditions:
Doing/Imposing that the state X_0 is equal to 1. Observing/Knowing that the state X_2 is equal to 0
Task: Compute probability distribution for X_1 (possible values: [0, 1]).
Output: Python dict mapping each value to its probability, rounded to 2 decimals.
Example: {0: 0.12, 1: 0.88} | {0: 0.04, 1: 0.96} | {"target_var_values": [0, 1], "bif_description": "// CANONICAL\n// variable: X_0\n// state_names: {'X_0': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_1\n// state_names: {'X_1': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_2\n// state_names: {'X_2': [0, 1]}\n// type: TabularCPD\n\nnetwork unknown {\n}\nvariable X_0 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_1 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_2 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nprobability ( X_0 ) {\n table 0.22, 0.78 ;\n}\nprobability ( X_1 ) {\n table 0.04, 0.96 ;\n}\nprobability ( X_2 ) {\n table 0.76, 0.24 ;\n}\n", "scenario": "Doing/Imposing that the state X_0 is equal to 1. Observing/Knowing that the state X_2 is equal to 0", "target": "X_1", "variables": ["X_0", "X_1", "X_2"], "n_round": 2, "cot": "Goal: Compute Causal Effect: P(X_1 | do(X_0=1), X_2=0)\nSurgery: P(X_0)= Point Mass at X_0=1.\nResult: P(X_1) = {0: 0.04, 1: 0.96}", "_time": 1.5397059917449951, "_task": "bayesian_intervention", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "n_nodes": 4, "max_domain_size": 3, "edge_prob": 0.5, "graph_generation_mode": "erdos", "n_round": 2, "cpt_relative_threshold": 0.5, "cot_scientific_notation": false, "is_verbose": false, "concise_cot": true}, "_prompt_tokens": 160, "_cot_tokens": 81} | bayesian_intervention | 1 | instruct |
System:
P(X_0) = {'0': 0.2, '1': 0.8}
P(X_1) = {'0': 0.4, '1': 0.6}
P(X_2) = {'0': 0.5, '1': 0.5}
P(X_3) = {'0': 0.5, '1': 0.5}
Observed conditions:
Doing/Imposing that the state X_1 is equal to 0. Observing/Knowing that the state X_2 is equal to 1
Task: Compute probability distribution for X_0 (possible values: [0, 1]).
Output: Python dict mapping each value to its probability, rounded to 1 decimals.
Example: {0: 0.1, 1: 0.9} | {0: 0.2, 1: 0.8} | {"target_var_values": [0, 1], "bif_description": "// CANONICAL\n// variable: X_0\n// state_names: {'X_0': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_1\n// state_names: {'X_1': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_2\n// state_names: {'X_2': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_3\n// state_names: {'X_3': [0, 1]}\n// type: TabularCPD\n\nnetwork unknown {\n}\nvariable X_0 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_1 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_2 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_3 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nprobability ( X_0 ) {\n table 0.2, 0.8 ;\n}\nprobability ( X_1 ) {\n table 0.4, 0.6 ;\n}\nprobability ( X_2 ) {\n table 0.5, 0.5 ;\n}\nprobability ( X_3 ) {\n table 0.5, 0.5 ;\n}\n", "scenario": "Doing/Imposing that the state X_1 is equal to 0. Observing/Knowing that the state X_2 is equal to 1", "target": "X_0", "variables": ["X_0", "X_1", "X_2", "X_3"], "n_round": 1, "cot": "Goal: Compute Causal Effect: P(X_0 | do(X_1=0), X_2=1)\nSurgery: P(X_1)= Point Mass at X_1=0.\nResult: P(X_0) = {0: 0.2, 1: 0.8}", "_time": 1.5542805194854736, "_task": "bayesian_intervention", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "n_nodes": 3, "max_domain_size": 3, "edge_prob": 0.5, "graph_generation_mode": "erdos", "n_round": 2, "cpt_relative_threshold": 0.5, "cot_scientific_notation": false, "is_verbose": false, "concise_cot": true}, "_prompt_tokens": 183, "_cot_tokens": 81} | bayesian_intervention | 1 | instruct |
System:
P(X_0) = {'0': 0.0, '1': 1.0}
P(X_1|X_0=0) = {'0': 0.3, '1': 0.7}
P(X_1|X_0=1) = {'0': 0.8, '1': 0.2}
P(X_2|X_0=0) = {'0': 0.1, '1': 0.9}
P(X_2|X_0=1) = {'0': 0.1, '1': 0.9}
P(X_3) = {'0': 0.4, '1': 0.6}
Observed conditions:
Doing/Imposing that the state X_3 is equal to 0. Observing/Knowing that the state X_1 is equal to 1
Task: Compute probability distribution for X_2 (possible values: [0, 1]).
Output: Python dict mapping each value to its probability, rounded to 1 decimals.
Example: {0: 0.1, 1: 0.9} | {0: 0.1, 1: 0.9} | {"target_var_values": [0, 1], "bif_description": "// CANONICAL\n// variable: X_0\n// state_names: {'X_0': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_1\n// state_names: {'X_1': [0, 1], 'X_0': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_2\n// state_names: {'X_2': [0, 1], 'X_0': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_3\n// state_names: {'X_3': [0, 1]}\n// type: TabularCPD\n\nnetwork unknown {\n}\nvariable X_0 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_1 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_2 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_3 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nprobability ( X_0 ) {\n table 0.0, 1.0 ;\n}\nprobability ( X_1 | X_0 ) {\n ( 0 ) 0.3, 0.7;\n ( 1 ) 0.8, 0.2;\n\n}\nprobability ( X_2 | X_0 ) {\n ( 0 ) 0.1, 0.9;\n ( 1 ) 0.1, 0.9;\n\n}\nprobability ( X_3 ) {\n table 0.4, 0.6 ;\n}\n", "scenario": "Doing/Imposing that the state X_3 is equal to 0. Observing/Knowing that the state X_1 is equal to 1", "target": "X_2", "variables": ["X_0", "X_1", "X_2", "X_3"], "n_round": 1, "cot": "Goal: Compute Causal Effect: P(X_2 | do(X_3=0), X_1=1)\nSurgery: P(X_3)= Point Mass at X_3=0.\nElim order: ['X_0']\nSum out X_0 -> P(X_1=1, X_2) = {0: 0.0, 1: 0.2}\nNormalize (sum=0.2) -> P(X_2 | X_1=1) = {0: 0.1, 1: 0.9}", "_time": 1.5952861309051514, "_task": "bayesian_intervention", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "n_nodes": 4, "max_domain_size": 3, "edge_prob": 0.5, "graph_generation_mode": "erdos", "n_round": 1, "cpt_relative_threshold": 0.5, "cot_scientific_notation": false, "is_verbose": false, "concise_cot": true}, "_prompt_tokens": 253, "_cot_tokens": 137} | bayesian_intervention | 1 | instruct |
System:
P(X_0) = {'0': 0.24, '1': 0.3, '2': 0.46}
P(X_1|X_0=0) = {'0': 0.05, '1': 0.45, '2': 0.5}
P(X_1|X_0=1) = {'0': 0.32, '1': 0.18, '2': 0.5}
P(X_1|X_0=2) = {'0': 0.03, '1': 0.53, '2': 0.44}
P(X_2) = {'0': 0.46, '1': 0.14, '2': 0.4}
Observed conditions:
Doing/Imposing that the state X_1 is equal to 0
Task: Compute probability distribution for X_2 (possible values: [0, 1, 2]).
Output: Python dict mapping each value to its probability, rounded to 2 decimals.
Example: {0: 0.12, 1: 0.88} | {0: 0.46, 1: 0.14, 2: 0.4} | {"target_var_values": [0, 1, 2], "bif_description": "// CANONICAL\n// variable: X_0\n// state_names: {'X_0': [0, 1, 2]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_1\n// state_names: {'X_1': [0, 1, 2], 'X_0': [0, 1, 2]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_2\n// state_names: {'X_2': [0, 1, 2]}\n// type: TabularCPD\n\nnetwork unknown {\n}\nvariable X_0 {\n type discrete [ 3 ] { 0, 1, 2 };\n property weight = None ;\n}\nvariable X_1 {\n type discrete [ 3 ] { 0, 1, 2 };\n property weight = None ;\n}\nvariable X_2 {\n type discrete [ 3 ] { 0, 1, 2 };\n property weight = None ;\n}\nprobability ( X_0 ) {\n table 0.24, 0.3, 0.46 ;\n}\nprobability ( X_1 | X_0 ) {\n ( 0 ) 0.05, 0.45, 0.5;\n ( 1 ) 0.32, 0.18, 0.5;\n ( 2 ) 0.03, 0.53, 0.44;\n\n}\nprobability ( X_2 ) {\n table 0.46, 0.14, 0.4 ;\n}\n", "scenario": "Doing/Imposing that the state X_1 is equal to 0", "target": "X_2", "variables": ["X_0", "X_1", "X_2"], "n_round": 2, "cot": "Goal: Compute Causal Effect: P(X_2 | do(X_1=0))\nSurgery: Cut incoming edges to intervened node 'X_1': ['X_0'] -> X_1; P(X_1)= Point Mass at X_1=0.\nResult: P(X_2) = {0: 0.46, 1: 0.14, 2: 0.4}", "_time": 1.7038514614105225, "_task": "bayesian_intervention", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "n_nodes": 4, "max_domain_size": 3, "edge_prob": 0.5, "graph_generation_mode": "erdos", "n_round": 2, "cpt_relative_threshold": 0.5, "cot_scientific_notation": false, "is_verbose": false, "concise_cot": true}, "_prompt_tokens": 251, "_cot_tokens": 113} | bayesian_intervention | 1 | instruct |
System:
P(X_0) = {'0': 0.6, '1': 0.3, '2': 0.1}
P(X_1|X_0=0) = {'0': 0.1, '1': 0.5, '2': 0.4}
P(X_1|X_0=1) = {'0': 0.5, '1': 0.4, '2': 0.1}
P(X_1|X_0=2) = {'0': 0.4, '1': 0.4, '2': 0.2}
X_3 ~ Noisy-MIN(leak=None, influences={'X_0': {'1': [0.0, 0.4, 0.6], '2': [0.0, 0.0, 1.0]}, 'X_2': {'1': [0.0, 0.0, 1.0], '2': [0.0, 0.0, 1.0]}})
P(X_2|X_1=0) = {'0': 0.2, '1': 0.2, '2': 0.6}
P(X_2|X_1=1) = {'0': 0.5, '1': 0.3, '2': 0.2}
P(X_2|X_1=2) = {'0': 0.5, '1': 0.1, '2': 0.4}
Observed conditions:
Doing/Imposing that the state X_3 is equal to 1. Observing/Knowing that the state X_0 is equal to 2, and the state X_2 is equal to 0
Task: Compute probability distribution for X_1 (possible values: [0, 1, 2]).
Output: Python dict mapping each value to its probability, rounded to 1 decimals.
Example: {0: 0.1, 1: 0.9} | {0: 0.2, 1: 0.5, 2: 0.3} | {"target_var_values": [0, 1, 2], "bif_description": "// CANONICAL\n// variable: X_0\n// state_names: {'X_0': [0, 1, 2]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_1\n// state_names: {'X_1': [0, 1, 2], 'X_0': [0, 1, 2]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_3\n// state_names: {'X_3': [0, 1, 2], 'X_0': [0, 1, 2], 'X_2': [0, 1, 2]}\n// type: MultilevelInfluenceModel\n// mode: MIN\n// leak: None\n// influence_tables: {'X_0': {0: [0.0, 0.6, 0.4], 1: [0.0, 0.4, 0.6], 2: [0.0, 0.0, 1.0]}, 'X_2': {0: [0.3, 0.5, 0.2], 1: [0.0, 0.0, 1.0], 2: [0.0, 0.0, 1.0]}}\n// parents: ['X_0', 'X_2']\n// CANONICAL\n// variable: X_2\n// state_names: {'X_2': [0, 1, 2], 'X_1': [0, 1, 2]}\n// type: TabularCPD\n\nnetwork unknown {\n}\nvariable X_0 {\n type discrete [ 3 ] { 0, 1, 2 };\n property weight = None ;\n}\nvariable X_1 {\n type discrete [ 3 ] { 0, 1, 2 };\n property weight = None ;\n}\nvariable X_2 {\n type discrete [ 3 ] { 0, 1, 2 };\n property weight = None ;\n}\nvariable X_3 {\n type discrete [ 3 ] { 0, 1, 2 };\n property weight = None ;\n}\nprobability ( X_0 ) {\n table 0.6, 0.3, 0.1 ;\n}\nprobability ( X_1 | X_0 ) {\n ( 0 ) 0.1, 0.5, 0.4;\n ( 1 ) 0.5, 0.4, 0.1;\n ( 2 ) 0.4, 0.4, 0.2;\n\n}\nprobability ( X_2 | X_1 ) {\n ( 0 ) 0.2, 0.2, 0.6;\n ( 1 ) 0.5, 0.3, 0.2;\n ( 2 ) 0.5, 0.1, 0.4;\n\n}\nprobability ( X_3 | X_0, X_2 ) {\n ( 0, 0 ) 0.3333333333333333, 0.3333333333333333, 0.3333333333333333;\n ( 0, 1 ) 0.3333333333333333, 0.3333333333333333, 0.3333333333333333;\n ( 0, 2 ) 0.3333333333333333, 0.3333333333333333, 0.3333333333333333;\n ( 1, 0 ) 0.3333333333333333, 0.3333333333333333, 0.3333333333333333;\n ( 1, 1 ) 0.3333333333333333, 0.3333333333333333, 0.3333333333333333;\n ( 1, 2 ) 0.3333333333333333, 0.3333333333333333, 0.3333333333333333;\n ( 2, 0 ) 0.3333333333333333, 0.3333333333333333, 0.3333333333333333;\n ( 2, 1 ) 0.3333333333333333, 0.3333333333333333, 0.3333333333333333;\n ( 2, 2 ) 0.3333333333333333, 0.3333333333333333, 0.3333333333333333;\n\n}\n", "scenario": "Doing/Imposing that the state X_3 is equal to 1. Observing/Knowing that the state X_0 is equal to 2, and the state X_2 is equal to 0", "target": "X_1", "variables": ["X_0", "X_1", "X_3", "X_2"], "n_round": 1, "cot": "Goal: Compute Causal Effect: P(X_1 | do(X_3=1), X_0=2, X_2=0)\nSurgery: Cut incoming edges to intervened node 'X_3': ['X_0', 'X_2'] -> X_3; P(X_3)= Point Mass at X_3=1.\nNormalize (sum=0.4) -> P(X_1 | X_0=2, X_2=0) = {0: 0.2, 1: 0.5, 2: 0.3}", "_time": 1.6275856494903564, "_task": "bayesian_intervention", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "n_nodes": 3, "max_domain_size": 3, "edge_prob": 0.5, "graph_generation_mode": "erdos", "n_round": 2, "cpt_relative_threshold": 0.5, "cot_scientific_notation": false, "is_verbose": false, "concise_cot": true}, "_prompt_tokens": 457, "_cot_tokens": 149} | bayesian_intervention | 1 | instruct |
System:
P(X_0) = {'0': 0.3, '1': 0.7}
P(X_1|X_0=0) = {'0': 0.5, '1': 0.5}
P(X_1|X_0=1) = {'0': 0.7, '1': 0.3}
P(X_2|X_1=0) = {'0': 0.2, '1': 0.8}
P(X_2|X_1=1) = {'0': 0.9, '1': 0.1}
Observed conditions:
Doing/Imposing that the state X_2 is equal to 0
Task: Compute probability distribution for X_1 (possible values: [0, 1]).
Output: Python dict mapping each value to its probability, rounded to 1 decimals.
Example: {0: 0.1, 1: 0.9} | {0: 0.6, 1: 0.4} | {"target_var_values": [0, 1], "bif_description": "// CANONICAL\n// variable: X_0\n// state_names: {'X_0': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_1\n// state_names: {'X_1': [0, 1], 'X_0': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_2\n// state_names: {'X_2': [0, 1], 'X_1': [0, 1]}\n// type: TabularCPD\n\nnetwork unknown {\n}\nvariable X_0 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_1 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_2 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nprobability ( X_0 ) {\n table 0.3, 0.7 ;\n}\nprobability ( X_1 | X_0 ) {\n ( 0 ) 0.5, 0.5;\n ( 1 ) 0.7, 0.3;\n\n}\nprobability ( X_2 | X_1 ) {\n ( 0 ) 0.2, 0.8;\n ( 1 ) 0.9, 0.1;\n\n}\n", "scenario": "Doing/Imposing that the state X_2 is equal to 0", "target": "X_1", "variables": ["X_0", "X_1", "X_2"], "n_round": 1, "cot": "Goal: Compute Causal Effect: P(X_1 | do(X_2=0))\nSurgery: Cut incoming edges to intervened node 'X_2': ['X_1'] -> X_2; P(X_2)= Point Mass at X_2=0.\nElim order: ['X_0']\nSum out X_0 -> P(X_1) = {0: 0.6, 1: 0.4}\nResult: P(X_1) = {0: 0.6, 1: 0.4}", "_time": 1.60451340675354, "_task": "bayesian_intervention", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "n_nodes": 4, "max_domain_size": 2, "edge_prob": 0.5, "graph_generation_mode": "erdos", "n_round": 2, "cpt_relative_threshold": 0.5, "cot_scientific_notation": false, "is_verbose": false, "concise_cot": true}, "_prompt_tokens": 214, "_cot_tokens": 134} | bayesian_intervention | 1 | instruct |
System:
P(X_0) = {'0': 0.05, '1': 0.35, '2': 0.6}
P(X_1|X_0=0) = {'0': 0.15, '1': 0.85}
P(X_1|X_0=1) = {'0': 0.3, '1': 0.7}
P(X_1|X_0=2) = {'0': 0.48, '1': 0.52}
X_3 ~ Noisy-MAX(leak=None, influences={'X_0': {'1': [0.5, 0.5], '2': [0.44, 0.56]}, 'X_1': {'1': [0.33, 0.67]}, 'X_2': {'1': [0.79, 0.21], '2': [0.5, 0.5]}})
P(X_2|X_1=0) = {'0': 0.27, '1': 0.37, '2': 0.36}
P(X_2|X_1=1) = {'0': 0.42, '1': 0.11, '2': 0.47}
Observed conditions:
Doing/Imposing that the state X_2 is equal to 0. Observing/Knowing that the state X_3 is equal to 1
Task: Compute probability distribution for X_1 (possible values: [0, 1]).
Output: Python dict mapping each value to its probability, rounded to 2 decimals.
Example: {0: 0.12, 1: 0.88} | {0: 0.3, 1: 0.7} | {"target_var_values": [0, 1], "bif_description": "// CANONICAL\n// variable: X_0\n// state_names: {'X_0': [0, 1, 2]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_1\n// state_names: {'X_1': [0, 1], 'X_0': [0, 1, 2]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_3\n// state_names: {'X_3': [0, 1], 'X_0': [0, 1, 2], 'X_1': [0, 1], 'X_2': [0, 1, 2]}\n// type: MultilevelInfluenceModel\n// mode: MAX\n// leak: None\n// influence_tables: {'X_0': {0: [1.0, 0.0], 1: [0.5, 0.5], 2: [0.44, 0.56]}, 'X_1': {0: [1.0, 0.0], 1: [0.33, 0.67]}, 'X_2': {0: [1.0, 0.0], 1: [0.79, 0.21], 2: [0.5, 0.5]}}\n// parents: ['X_0', 'X_1', 'X_2']\n// CANONICAL\n// variable: X_2\n// state_names: {'X_2': [0, 1, 2], 'X_1': [0, 1]}\n// type: TabularCPD\n\nnetwork unknown {\n}\nvariable X_0 {\n type discrete [ 3 ] { 0, 1, 2 };\n property weight = None ;\n}\nvariable X_1 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_2 {\n type discrete [ 3 ] { 0, 1, 2 };\n property weight = None ;\n}\nvariable X_3 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nprobability ( X_0 ) {\n table 0.05, 0.35, 0.6 ;\n}\nprobability ( X_1 | X_0 ) {\n ( 0 ) 0.15, 0.85;\n ( 1 ) 0.3, 0.7;\n ( 2 ) 0.48, 0.52;\n\n}\nprobability ( X_2 | X_1 ) {\n ( 0 ) 0.27, 0.37, 0.36;\n ( 1 ) 0.42, 0.11, 0.47;\n\n}\nprobability ( X_3 | X_0, X_1, X_2 ) {\n ( 0, 0, 0 ) 0.5, 0.5;\n ( 0, 0, 1 ) 0.5, 0.5;\n ( 0, 0, 2 ) 0.5, 0.5;\n ( 0, 1, 0 ) 0.5, 0.5;\n ( 0, 1, 1 ) 0.5, 0.5;\n ( 0, 1, 2 ) 0.5, 0.5;\n ( 1, 0, 0 ) 0.5, 0.5;\n ( 1, 0, 1 ) 0.5, 0.5;\n ( 1, 0, 2 ) 0.5, 0.5;\n ( 1, 1, 0 ) 0.5, 0.5;\n ( 1, 1, 1 ) 0.5, 0.5;\n ( 1, 1, 2 ) 0.5, 0.5;\n ( 2, 0, 0 ) 0.5, 0.5;\n ( 2, 0, 1 ) 0.5, 0.5;\n ( 2, 0, 2 ) 0.5, 0.5;\n ( 2, 1, 0 ) 0.5, 0.5;\n ( 2, 1, 1 ) 0.5, 0.5;\n ( 2, 1, 2 ) 0.5, 0.5;\n\n}\n", "scenario": "Doing/Imposing that the state X_2 is equal to 0. Observing/Knowing that the state X_3 is equal to 1", "target": "X_1", "variables": ["X_0", "X_1", "X_3", "X_2"], "n_round": 2, "cot": "Goal: Compute Causal Effect: P(X_1 | do(X_2=0), X_3=1)\nSurgery: Cut incoming edges to intervened node 'X_2': ['X_1'] -> X_2; P(X_2)= Point Mass at X_2=0.\nElim order: ['X_2', 'X_0']\nSum out X_2 -> P(X_3=1 | X_0, X_1, do(X_2=0)) = [Distribution over ['X_0', 'X_1']]\nSum out X_0 -> P(X_1, X_3=1 | do(X_2=0)) = {0: 0.21, 1: 0.5}\nNormalize (sum=0.71) -> P(X_1 | X_3=1, do(X_2=0)) = {0: 0.3, 1: 0.7}", "_time": 1.6946065425872803, "_task": "bayesian_intervention", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "n_nodes": 4, "max_domain_size": 2, "edge_prob": 0.5, "graph_generation_mode": "erdos", "n_round": 2, "cpt_relative_threshold": 0.5, "cot_scientific_notation": false, "is_verbose": false, "concise_cot": true}, "_prompt_tokens": 379, "_cot_tokens": 220} | bayesian_intervention | 1 | instruct |
System:
P(X_2) = {'0': 0.46, '1': 0.54}
P(X_3|X_2=0) = {'0': 0.86, '1': 0.14}
P(X_3|X_2=1) = {'0': 0.34, '1': 0.66}
P(X_0) = {'0': 0.88, '1': 0.03, '2': 0.09}
P(X_1) = {'0': 0.68, '1': 0.03, '2': 0.29}
Observed conditions:
Doing/Imposing that the state X_1 is equal to 0. Observing/Knowing that the state X_0 is equal to 0, and the state X_2 is equal to 0
Task: Compute probability distribution for X_3 (possible values: [0, 1]).
Output: Python dict mapping each value to its probability, rounded to 2 decimals.
Example: {0: 0.12, 1: 0.88} | {0: 0.86, 1: 0.14} | {"target_var_values": [0, 1], "bif_description": "// CANONICAL\n// variable: X_2\n// state_names: {'X_2': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_3\n// state_names: {'X_3': [0, 1], 'X_2': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_0\n// state_names: {'X_0': [0, 1, 2]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_1\n// state_names: {'X_1': [0, 1, 2]}\n// type: TabularCPD\n\nnetwork unknown {\n}\nvariable X_0 {\n type discrete [ 3 ] { 0, 1, 2 };\n property weight = None ;\n}\nvariable X_1 {\n type discrete [ 3 ] { 0, 1, 2 };\n property weight = None ;\n}\nvariable X_2 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_3 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nprobability ( X_0 ) {\n table 0.88, 0.03, 0.09 ;\n}\nprobability ( X_1 ) {\n table 0.68, 0.03, 0.29 ;\n}\nprobability ( X_2 ) {\n table 0.46, 0.54 ;\n}\nprobability ( X_3 | X_2 ) {\n ( 0 ) 0.86, 0.14;\n ( 1 ) 0.34, 0.66;\n\n}\n", "scenario": "Doing/Imposing that the state X_1 is equal to 0. Observing/Knowing that the state X_0 is equal to 0, and the state X_2 is equal to 0", "target": "X_3", "variables": ["X_2", "X_3", "X_0", "X_1"], "n_round": 2, "cot": "Goal: Compute Causal Effect: P(X_3 | do(X_1=0), X_0=0, X_2=0)\nSurgery: P(X_1)= Point Mass at X_1=0.\nResult: P(X_3 | X_2=0) = {0: 0.86, 1: 0.14}", "_time": 1.6841776371002197, "_task": "bayesian_intervention", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "n_nodes": 3, "max_domain_size": 3, "edge_prob": 0.5, "graph_generation_mode": "erdos", "n_round": 1, "cpt_relative_threshold": 0.5, "cot_scientific_notation": false, "is_verbose": false, "concise_cot": true}, "_prompt_tokens": 246, "_cot_tokens": 93} | bayesian_intervention | 1 | instruct |
System:
P(X_0) = {'0': 0.69, '1': 0.31}
P(X_1) = {'0': 0.8, '1': 0.2}
P(X_2) = {'0': 0.17, '1': 0.83}
Observed conditions:
Doing/Imposing that the state X_1 is equal to 0
Task: Compute probability distribution for X_2 (possible values: [0, 1]).
Output: Python dict mapping each value to its probability, rounded to 2 decimals.
Example: {0: 0.12, 1: 0.88} | {0: 0.17, 1: 0.83} | {"target_var_values": [0, 1], "bif_description": "// CANONICAL\n// variable: X_0\n// state_names: {'X_0': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_1\n// state_names: {'X_1': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_2\n// state_names: {'X_2': [0, 1]}\n// type: TabularCPD\n\nnetwork unknown {\n}\nvariable X_0 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_1 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_2 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nprobability ( X_0 ) {\n table 0.69, 0.31 ;\n}\nprobability ( X_1 ) {\n table 0.8, 0.2 ;\n}\nprobability ( X_2 ) {\n table 0.17, 0.83 ;\n}\n", "scenario": "Doing/Imposing that the state X_1 is equal to 0", "target": "X_2", "variables": ["X_0", "X_1", "X_2"], "n_round": 2, "cot": "Goal: Compute Causal Effect: P(X_2 | do(X_1=0))\nSurgery: P(X_1)= Point Mass at X_1=0.\nResult: P(X_2) = {0: 0.17, 1: 0.83}", "_time": 1.640880823135376, "_task": "bayesian_intervention", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "n_nodes": 4, "max_domain_size": 2, "edge_prob": 0.5, "graph_generation_mode": "erdos", "n_round": 1, "cpt_relative_threshold": 0.5, "cot_scientific_notation": false, "is_verbose": false, "concise_cot": true}, "_prompt_tokens": 144, "_cot_tokens": 75} | bayesian_intervention | 1 | instruct |
System:
P(X_0) = {'0': 0.2, '1': 0.8}
X_3 ~ Noisy-MAX(leak=None, influences={'X_0': {'1': [0.4, 0.3, 0.3]}, 'X_1': {'1': [1.0, 0.0, 0.0], '2': [0.7, 0.3, 0.0]}})
P(X_1) = {'0': 0.4, '1': 0.2, '2': 0.4}
P(X_2) = {'0': 0.6, '1': 0.4}
Observed conditions:
Doing/Imposing that the state X_0 is equal to 0. Observing/Knowing that the state X_3 is equal to 1, and the state X_2 is equal to 1
Task: Compute probability distribution for X_1 (possible values: [0, 1, 2]).
Output: Python dict mapping each value to its probability, rounded to 1 decimals.
Example: {0: 0.1, 1: 0.9} | {0: 0.0, 1: 0.0, 2: 1.0} | {"target_var_values": [0, 1, 2], "bif_description": "// CANONICAL\n// variable: X_0\n// state_names: {'X_0': [0, 1]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_3\n// state_names: {'X_3': [0, 1, 2], 'X_0': [0, 1], 'X_1': [0, 1, 2]}\n// type: MultilevelInfluenceModel\n// mode: MAX\n// leak: None\n// influence_tables: {'X_0': {0: [1.0, 0.0, 0.0], 1: [0.4, 0.3, 0.3]}, 'X_1': {0: [1.0, 0.0, 0.0], 1: [1.0, 0.0, 0.0], 2: [0.7, 0.3, 0.0]}}\n// parents: ['X_0', 'X_1']\n// CANONICAL\n// variable: X_1\n// state_names: {'X_1': [0, 1, 2]}\n// type: TabularCPD\n// CANONICAL\n// variable: X_2\n// state_names: {'X_2': [0, 1]}\n// type: TabularCPD\n\nnetwork unknown {\n}\nvariable X_0 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_1 {\n type discrete [ 3 ] { 0, 1, 2 };\n property weight = None ;\n}\nvariable X_2 {\n type discrete [ 2 ] { 0, 1 };\n property weight = None ;\n}\nvariable X_3 {\n type discrete [ 3 ] { 0, 1, 2 };\n property weight = None ;\n}\nprobability ( X_0 ) {\n table 0.2, 0.8 ;\n}\nprobability ( X_1 ) {\n table 0.4, 0.2, 0.4 ;\n}\nprobability ( X_2 ) {\n table 0.6, 0.4 ;\n}\nprobability ( X_3 | X_0, X_1 ) {\n ( 0, 0 ) 0.3333333333333333, 0.3333333333333333, 0.3333333333333333;\n ( 0, 1 ) 0.3333333333333333, 0.3333333333333333, 0.3333333333333333;\n ( 0, 2 ) 0.3333333333333333, 0.3333333333333333, 0.3333333333333333;\n ( 1, 0 ) 0.3333333333333333, 0.3333333333333333, 0.3333333333333333;\n ( 1, 1 ) 0.3333333333333333, 0.3333333333333333, 0.3333333333333333;\n ( 1, 2 ) 0.3333333333333333, 0.3333333333333333, 0.3333333333333333;\n\n}\n", "scenario": "Doing/Imposing that the state X_0 is equal to 0. Observing/Knowing that the state X_3 is equal to 1, and the state X_2 is equal to 1", "target": "X_1", "variables": ["X_0", "X_3", "X_1", "X_2"], "n_round": 1, "cot": "Goal: Compute Causal Effect: P(X_1 | do(X_0=0), X_3=1, X_2=1)\nSurgery: P(X_0)= Point Mass at X_0=0.\nElim order: ['X_0']\nSum out X_0 -> P(X_3=1 | X_1, do(X_0=0)) = {0: 0.0, 1: 0.0, 2: 0.3}\nNormalize (sum=0.1) -> P(X_1 | X_3=1, do(X_0=0)) = {0: 0.0, 1: 0.0, 2: 1.0}", "_time": 1.7018978595733643, "_task": "bayesian_intervention", "_level": 1, "_config": {"c": 1.0, "level": 1, "seed": null, "size": null, "n_nodes": 4, "max_domain_size": 2, "edge_prob": 0.5, "graph_generation_mode": "erdos", "n_round": 1, "cpt_relative_threshold": 0.5, "cot_scientific_notation": false, "is_verbose": false, "concise_cot": true}, "_prompt_tokens": 263, "_cot_tokens": 181} | bayesian_intervention | 1 | instruct |
Consider the graph below, which contains exactly one cycle.
Nodes: [0, 1, 2, 3, 4]
Matrix:
[0, 1, 0, 0, 1]
[1, 0, 1, 0, 0]
[0, 1, 0, 1, 0]
[0, 0, 1, 0, 1]
[1, 0, 0, 1, 0]
Identify all the nodes that form the cycle.
Your answer must be a Python list of node integers, sorted in increasing order. Example: `[2, 5, 7, 8]`. | [0, 1, 2, 3, 4] | {"graph_description": "Nodes: [0, 1, 2, 3, 4]\nMatrix:\n[0, 1, 0, 0, 1]\n[1, 0, 1, 0, 0]\n[0, 1, 0, 1, 0]\n[0, 0, 1, 0, 1]\n[1, 0, 0, 1, 0]", "_time": 0.0005991458892822266, "_task": "graph_cycle_detection", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_nodes": 5}, "_prompt_tokens": 145, "_cot_tokens": 15} | graph_cycle_detection | 0 | instruct |
Consider the graph below, which contains exactly one cycle.
Nodes: [0, 1, 2, 3, 4]
Matrix:
[0, 1, 0, 1, 0]
[1, 0, 1, 0, 0]
[0, 1, 0, 1, 0]
[1, 0, 1, 0, 1]
[0, 0, 0, 1, 0]
Identify all the nodes that form the cycle.
Your answer must be a Python list of node integers, sorted in increasing order. Example: `[2, 5, 7, 8]`. | [0, 1, 2, 3] | {"graph_description": "Nodes: [0, 1, 2, 3, 4]\nMatrix:\n[0, 1, 0, 1, 0]\n[1, 0, 1, 0, 0]\n[0, 1, 0, 1, 0]\n[1, 0, 1, 0, 1]\n[0, 0, 0, 1, 0]", "_time": 0.00028014183044433594, "_task": "graph_cycle_detection", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_nodes": 5}, "_prompt_tokens": 145, "_cot_tokens": 12} | graph_cycle_detection | 0 | instruct |
Consider the graph below, which contains exactly one cycle.
Nodes: [0, 1, 2, 3, 4]
Matrix:
[0, 1, 1, 0, 0]
[1, 0, 1, 0, 0]
[1, 1, 0, 1, 0]
[0, 0, 1, 0, 1]
[0, 0, 0, 1, 0]
Identify all the nodes that form the cycle.
Your answer must be a Python list of node integers, sorted in increasing order. Example: `[2, 5, 7, 8]`. | [0, 1, 2] | {"graph_description": "Nodes: [0, 1, 2, 3, 4]\nMatrix:\n[0, 1, 1, 0, 0]\n[1, 0, 1, 0, 0]\n[1, 1, 0, 1, 0]\n[0, 0, 1, 0, 1]\n[0, 0, 0, 1, 0]", "_time": 0.00025963783264160156, "_task": "graph_cycle_detection", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_nodes": 5}, "_prompt_tokens": 145, "_cot_tokens": 9} | graph_cycle_detection | 0 | instruct |
Consider the graph below, which contains exactly one cycle.
{0: [1], 1: [0, 2, 3], 2: [1, 3], 3: [1, 2, 4], 4: [3]}
Identify all the nodes that form the cycle.
Your answer must be a Python list of node integers, sorted in increasing order. Example: `[2, 5, 7, 8]`. | [1, 2, 3] | {"graph_description": "{0: [1], 1: [0, 2, 3], 2: [1, 3], 3: [1, 2, 4], 4: [3]}", "_time": 0.00023984909057617188, "_task": "graph_cycle_detection", "_level": 0, "_config": {"c": 1.0, "level": 0, "seed": null, "size": null, "num_nodes": 5}, "_prompt_tokens": 97, "_cot_tokens": 9} | graph_cycle_detection | 0 | instruct |
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