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eugenio castellotti | https://en.wikipedia.org/wiki/Eugenio_Castellotti | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1228351-1.html.csv | comparative | eugenio castellotti scored more points in 1956 than he did in 1957 . | {'row_1': '4', 'row_2': '5', 'col': '5', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1956'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose year record fuzzily matches to 1956 .', 'tostr': 'filter_eq { all_rows ; year ; 1956 }'}, 'points'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; year ; 1956 } ; points }', 'tointer': 'select the rows whose year record fuzzily matches to 1956 . take the points record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'year', '1957'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record fuzzily matches to 1957 .', 'tostr': 'filter_eq { all_rows ; year ; 1957 }'}, 'points'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; year ; 1957 } ; points }', 'tointer': 'select the rows whose year record fuzzily matches to 1957 . take the points record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; year ; 1956 } ; points } ; hop { filter_eq { all_rows ; year ; 1957 } ; points } } = true', 'tointer': 'select the rows whose year record fuzzily matches to 1956 . take the points record of this row . select the rows whose year record fuzzily matches to 1957 . take the points record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; year ; 1956 } ; points } ; hop { filter_eq { all_rows ; year ; 1957 } ; points } } = true | select the rows whose year record fuzzily matches to 1956 . take the points record of this row . select the rows whose year record fuzzily matches to 1957 . take the points record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'year_7': 7, '1956_8': 8, 'points_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'year_11': 11, '1957_12': 12, 'points_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'year_7': 'year', '1956_8': '1956', 'points_9': 'points', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'year_11': 'year', '1957_12': '1957', 'points_13': 'points'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'year_7': [0], '1956_8': [0], 'points_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'year_11': [1], '1957_12': [1], 'points_13': [3]} | ['year', 'team', 'chassis', 'engine', 'points'] | [['1955', 'scuderia lancia', 'lancia d50', 'lancia ds50 2.5 v8', '12'], ['1955', 'scuderia ferrari', 'ferrari 625', 'ferrari 107 2.5 l4', '12'], ['1955', 'scuderia ferrari', 'ferrari 555', 'ferrari 106 2.5 l4', '12'], ['1956', 'scuderia ferrari', 'lancia ferrari d50', 'lancia ferrari ds50 2.5 v8', '7.5'], ['1957', 'scuderia ferrari', 'lancia ferrari 801', 'lancia ferrari ds50 2.5 v8', '0']] |
driver deaths in motorsport | https://en.wikipedia.org/wiki/Driver_deaths_in_motorsport | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1632486-11.html.csv | majority | the majority of the motorsport driver deaths were in the stock car discipline . | {'scope': 'all', 'col': '1', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'stock car', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'discipline', 'stock car'], 'result': True, 'ind': 0, 'tointer': 'for the discipline records of all rows , most of them fuzzily match to stock car .', 'tostr': 'most_eq { all_rows ; discipline ; stock car } = true'} | most_eq { all_rows ; discipline ; stock car } = true | for the discipline records of all rows , most of them fuzzily match to stock car . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'discipline_3': 3, 'stock car_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'discipline_3': 'discipline', 'stock car_4': 'stock car'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'discipline_3': [0], 'stock car_4': [0]} | ['discipline', 'championship', 'circuit', 'event', 'session'] | [['stock car', 'sprint cup series', 'daytona international speedway', 'uno twin 125 qualifiers', 'qualifying'], ['stock car', 'whelen modified tour', 'martinsville speedway', 'winn - dixie 500', 'race'], ['drag racing', 'nhra winston drag racing series', 'indianapolis raceway park', 'mac tools us nationals', 'qualifying'], ['stock car', 'arca series', 'daytona international speedway', 'daytona arca 200', 'race'], ['open wheel', 'usac national championship', 'williams grove speedway', 'indianapolis sweepstakes', 'race']] |
1968 san francisco 49ers season | https://en.wikipedia.org/wiki/1968_San_Francisco_49ers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17407008-2.html.csv | ordinal | there was an attendance of 46978 during the 9th game of the 1968 san francisco 49ers season . | {'scope': 'all', 'row': '9', 'col': '1', 'order': '9', 'col_other': '5', 'max_or_min': 'min_to_max', 'value_mentioned': 'yes', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'nth_min', 'args': ['all_rows', 'week', '9'], 'result': '9', 'ind': 0, 'tostr': 'nth_min { all_rows ; week ; 9 }', 'tointer': 'the 9th minimum week record of all rows is 9 .'}, '9'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_min { all_rows ; week ; 9 } ; 9 }', 'tointer': 'the 9th minimum week record of all rows is 9 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'week', '9'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; week ; 9 }'}, 'attendance'], 'result': '46978', 'ind': 3, 'tostr': 'hop { nth_argmin { all_rows ; week ; 9 } ; attendance }'}, '46978'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { nth_argmin { all_rows ; week ; 9 } ; attendance } ; 46978 }', 'tointer': 'the attendance record of the row with 9th minimum week record is 46978 .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { nth_min { all_rows ; week ; 9 } ; 9 } ; eq { hop { nth_argmin { all_rows ; week ; 9 } ; attendance } ; 46978 } } = true', 'tointer': 'the 9th minimum week record of all rows is 9 . the attendance record of the row with 9th minimum week record is 46978 .'} | and { eq { nth_min { all_rows ; week ; 9 } ; 9 } ; eq { hop { nth_argmin { all_rows ; week ; 9 } ; attendance } ; 46978 } } = true | the 9th minimum week record of all rows is 9 . the attendance record of the row with 9th minimum week record is 46978 . | 6 | 6 | {'and_5': 5, 'result_6': 6, 'eq_1': 1, 'nth_min_0': 0, 'all_rows_7': 7, 'week_8': 8, '9_9': 9, '9_10': 10, 'eq_4': 4, 'num_hop_3': 3, 'nth_argmin_2': 2, 'all_rows_11': 11, 'week_12': 12, '9_13': 13, 'attendance_14': 14, '46978_15': 15} | {'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'nth_min_0': 'nth_min', 'all_rows_7': 'all_rows', 'week_8': 'week', '9_9': '9', '9_10': '9', 'eq_4': 'eq', 'num_hop_3': 'num_hop', 'nth_argmin_2': 'nth_argmin', 'all_rows_11': 'all_rows', 'week_12': 'week', '9_13': '9', 'attendance_14': 'attendance', '46978_15': '46978'} | {'and_5': [6], 'result_6': [], 'eq_1': [5], 'nth_min_0': [1], 'all_rows_7': [0], 'week_8': [0], '9_9': [0], '9_10': [1], 'eq_4': [5], 'num_hop_3': [4], 'nth_argmin_2': [3], 'all_rows_11': [2], 'week_12': [2], '9_13': [2], 'attendance_14': [3], '46978_15': [4]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 15 , 1968', 'baltimore colts', 'l 27 - 10', '56864'], ['2', 'september 22 , 1968', 'st louis cardinals', 'w 35 - 17', '27557'], ['3', 'september 29 , 1968', 'atlanta falcons', 'w 28 - 13', '27477'], ['4', 'october 6 , 1968', 'los angeles rams', 'l 24 - 10', '69520'], ['5', 'october 13 , 1968', 'baltimore colts', 'l 42 - 14', '32822'], ['6', 'october 20 , 1968', 'new york giants', 'w 26 - 10', '62958'], ['7', 'october 27 , 1968', 'detroit lions', 'w 14 - 7', '53555'], ['8', 'november 3 , 1968', 'cleveland browns', 'l 33 - 21', '31359'], ['9', 'november 10 , 1968', 'chicago bears', 'l 27 - 19', '46978'], ['10', 'november 17 , 1968', 'los angeles rams', 't 20 - 20', '41815'], ['11', 'november 24 , 1968', 'pittsburgh steelers', 'w 45 - 28', '21408'], ['12', 'december 1 , 1968', 'green bay packers', 'w 27 - 20', '47218'], ['13', 'december 8 , 1968', 'minnesota vikings', 'l 30 - 20', '29049'], ['14', 'december 15 , 1968', 'atlanta falcons', 'w 14 - 12', '44977']] |
charmed ( season 3 ) | https://en.wikipedia.org/wiki/Charmed_%28season_3%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21165255-1.html.csv | unique | the honeymoon 's over is the only epsiode of charmed ( season 3 ) that was written by brad kern . | {'scope': 'all', 'row': '1', 'col': '5', 'col_other': '3', 'criterion': 'equal', 'value': 'brad kern', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'written by', 'brad kern'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose written by record fuzzily matches to brad kern .', 'tostr': 'filter_eq { all_rows ; written by ; brad kern }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; written by ; brad kern } }', 'tointer': 'select the rows whose written by record fuzzily matches to brad kern . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'written by', 'brad kern'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose written by record fuzzily matches to brad kern .', 'tostr': 'filter_eq { all_rows ; written by ; brad kern }'}, 'title'], 'result': "the honeymoon 's over", 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; written by ; brad kern } ; title }'}, "the honeymoon 's over"], 'result': True, 'ind': 3, 'tostr': "eq { hop { filter_eq { all_rows ; written by ; brad kern } ; title } ; the honeymoon 's over }", 'tointer': "the title record of this unqiue row is the honeymoon 's over ."}], 'result': True, 'ind': 4, 'tostr': "and { only { filter_eq { all_rows ; written by ; brad kern } } ; eq { hop { filter_eq { all_rows ; written by ; brad kern } ; title } ; the honeymoon 's over } } = true", 'tointer': "select the rows whose written by record fuzzily matches to brad kern . there is only one such row in the table . the title record of this unqiue row is the honeymoon 's over ."} | and { only { filter_eq { all_rows ; written by ; brad kern } } ; eq { hop { filter_eq { all_rows ; written by ; brad kern } ; title } ; the honeymoon 's over } } = true | select the rows whose written by record fuzzily matches to brad kern . there is only one such row in the table . the title record of this unqiue row is the honeymoon 's over . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'written by_7': 7, 'brad kern_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'title_9': 9, "the honeymoon 's over_10": 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'written by_7': 'written by', 'brad kern_8': 'brad kern', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'title_9': 'title', "the honeymoon 's over_10": "the honeymoon 's over"} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'written by_7': [0], 'brad kern_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'title_9': [2], "the honeymoon 's over_10": [3]} | ['no in series', 'no in season', 'title', 'directed by', 'written by', 'original air date', 'production code', 'us viewers ( millions )'] | [['45', '1', "the honeymoon 's over", 'jim conway', 'brad kern', 'october 5 , 2000', '4300045', '7.7'], ['46', '2', 'magic hour', 'john behring', 'zack estrin & chris levinson', 'october 12 , 2000', '4300046', '5.1'], ['47', '3', 'once upon a time', 'joel j feigenbaum', 'krista vernoff', 'october 19 , 2000', '4300047', '5.4'], ['48', '4', "all halliwell 's eve", 'anson williams', 'sheryl j anderson', 'october 26 , 2000', '4300048', '6.5'], ['49', '5', 'sight unseen', 'perry lang', 'william schmidt', 'november 2 , 2000', '4300049', '5.7'], ['50', '6', 'primrose empath', 'mel damski', 'daniel cerone', 'november 9 , 2000', '4300051', '6.1'], ['51', '7', 'power outage', 'craig zisk', 'monica breen & alison schapker', 'november 16 , 2000', '4300050', '5.7'], ['52', '8', 'sleuthing with the enemy', 'noel nosseck', 'peter hume', 'december 14 , 2000', '4300052', '5.5'], ['53', '9', 'coyote piper', 'chris long', 'krista vernoff', 'january 11 , 2001', '4300053', '5.1'], ['54', '10', 'we all scream for ice cream', 'allan kroeker', 'chris levinson & zack estrin', 'january 18 , 2001', '4300054', '5.4'], ['55', '11', 'blinded by the whitelighter', 'david straiton', 'nell scovell', 'january 25 , 2001', '4300055', '5.4'], ['56', '12', 'wrestling with demons', 'joel j feigenbaum', 'sheryl j anderson', 'february 1 , 2001', '4300056', '5.9'], ['57', '13', 'bride and gloom', 'chris long', 'william schmidt', 'february 8 , 2001', '4300057', '5.4'], ['58', '14', 'the good , the bad and the cursed', 'shannen doherty', 'monica breen & alison schapker', 'february 15 , 2001', '4300058', '5.1'], ['59', '15', 'just harried', 'mel damski', 'daniel cerone', 'february 22 , 2001', '4300059', '5.8'], ['60', '16', 'death takes a halliwell', 'jon pare', 'krista vernoff', 'march 15 , 2001', '4300060', '5.4'], ['61', '17', 'pre - witched', 'david straiton', 'chris levinson & zack estrin', 'march 22 , 2001', '4300061', '5.1'], ['62', '18', 'sin francisco', 'joel j feigenbaum', 'nell scovell', 'april 19 , 2001', '4300062', '4.0'], ['63', '19', 'the demon who came in from the cold', 'anson williams', 'sheryl j anderson', 'april 26 , 2001', '4300063', '3.6'], ['64', '20', 'exit strategy', 'joel j feigenbaum', 'peter hume & daniel cerone', 'may 3 , 2001', '4300064', '4.1']] |
2008 - 09 new york knicks season | https://en.wikipedia.org/wiki/2008%E2%80%9309_New_York_Knicks_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17060277-8.html.csv | aggregation | nate robinson averaged 32.8 points in the 6 games where he was the high scorer for the knicks in february of 2009 . | {'scope': 'subset', 'col': '5', 'type': 'average', 'result': '32.8', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'nate robinson'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'high points', 'nate robinson'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; high points ; nate robinson }', 'tointer': 'select the rows whose high points record fuzzily matches to nate robinson .'}, 'high points'], 'result': '32.8', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; high points ; nate robinson } ; high points }'}, '32.8'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; high points ; nate robinson } ; high points } ; 32.8 } = true', 'tointer': 'select the rows whose high points record fuzzily matches to nate robinson . the average of the high points record of these rows is 32.8 .'} | round_eq { avg { filter_eq { all_rows ; high points ; nate robinson } ; high points } ; 32.8 } = true | select the rows whose high points record fuzzily matches to nate robinson . the average of the high points record of these rows is 32.8 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'high points_5': 5, 'nate robinson_6': 6, 'high points_7': 7, '32.8_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'high points_5': 'high points', 'nate robinson_6': 'nate robinson', 'high points_7': 'high points', '32.8_8': '32.8'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'high points_5': [0], 'nate robinson_6': [0], 'high points_7': [1], '32.8_8': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['47', 'february 2', 'la lakers', 'l 117 - 126 ( ot )', 'al harrington ( 24 )', 'david lee ( 12 )', 'chris duhon ( 9 )', 'madison square garden 19763', '21 - 26'], ['48', 'february 4', 'cleveland', 'l 102 - 107 ( ot )', 'al harrington ( 39 )', 'al harrington ( 13 )', 'chris duhon ( 5 )', 'madison square garden 19763', '21 - 27'], ['49', 'february 6', 'boston', 'l 100 - 110 ( ot )', 'al harrington ( 27 )', 'david lee ( 18 )', 'chris duhon ( 7 )', 'madison square garden 19763', '21 - 28'], ['50', 'february 8', 'portland', 'l 108 - 109 ( ot )', 'david lee ( 29 )', 'david lee ( 11 )', 'chris duhon ( 10 )', 'rose garden 20609', '21 - 29'], ['51', 'february 10', 'golden state', 'l 127 - 144 ( ot )', 'nate robinson ( 30 )', 'david lee ( 11 )', 'chris duhon ( 9 )', 'oracle arena 19098', '21 - 30'], ['52', 'february 11', 'la clippers', 'l 124 - 128 ( ot )', 'nate robinson ( 33 )', 'david lee ( 12 )', 'nate robinson ( 15 )', 'staples center 16928', '21 - 31'], ['53', 'february 17', 'san antonio', 'w 112 - 107 ( ot )', 'nate robinson ( 32 )', 'david lee ( 12 )', 'chris duhon ( 8 )', 'madison square garden 19763', '22 - 31'], ['54', 'february 20', 'toronto', 'w 127 - 97 ( ot )', 'wilson chandler ( 32 )', 'david lee ( 15 )', 'nate robinson ( 7 )', 'madison square garden 19763', '23 - 31'], ['55', 'february 22', 'toronto', 'l 100 - 111 ( ot )', 'al harrington ( 31 )', 'david lee ( 15 )', 'nate robinson ( 8 )', 'air canada centre 19800', '23 - 32'], ['56', 'february 23', 'indiana', 'w 123 - 119 ( ot )', 'nate robinson ( 41 )', 'david lee ( 13 )', 'chris duhon ( 5 )', 'madison square garden 17283', '24 - 32'], ['57', 'february 25', 'orlando', 'l 109 - 114 ( ot )', 'nate robinson ( 32 )', 'david lee ( 10 )', 'chris duhon ( 10 )', 'madison square garden 19763', '24 - 33'], ['58', 'february 27', 'philadelphia', 'l 103 - 108 ( ot )', 'larry hughes ( 25 )', 'david lee ( 11 )', 'chris duhon ( 6 )', 'madison square garden 19763', '24 - 34'], ['59', 'february 28', 'miami', 'l 115 - 120 ( ot )', 'nate robinson ( 29 )', 'david lee ( 13 )', 'chris duhon ( 9 )', 'american airlines arena 19600', '24 - 35']] |
1983 tampa bay buccaneers season | https://en.wikipedia.org/wiki/1983_Tampa_Bay_Buccaneers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11440693-2.html.csv | majority | the tampa bay buccaneers lost most of the games in its 1983 season . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'l', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'result', 'l'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , most of them fuzzily match to l .', 'tostr': 'most_eq { all_rows ; result ; l } = true'} | most_eq { all_rows ; result ; l } = true | for the result records of all rows , most of them fuzzily match to l . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 'l_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 'l_4': 'l'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 'l_4': [0]} | ['week', 'date', 'opponent', 'result', 'kickoff', 'game site', 'attendance', 'record'] | [['week', 'date', 'opponent', 'result', 'kickoff', 'game site', 'attendance', 'record'], ['1', 'september 4 , 1983', 'detroit lions', 'l 11 - 0', '1:00', 'tampa stadium', '62154', '0 - 1'], ['2', 'september 11 , 1983', 'chicago bears', 'l 17 - 10', '1:00', 'soldier field', '58156', '0 - 2'], ['3', 'september 18 , 1983', 'minnesota vikings', 'l 19 - 16 ot', '4:00', 'tampa stadium', '57567', '0 - 3'], ['4', 'september 25 , 1983', 'cincinnati bengals', 'l 23 - 17', '1:00', 'tampa stadium', '56023', '0 - 4'], ['5', 'october 2 , 1983', 'green bay packers', 'l 55 - 14', '1:00', 'lambeau field', '54272', '0 - 5'], ['6', 'october 9 , 1983', 'dallas cowboys', 'l 27 - 24 ot', '4:00', 'texas stadium', '63308', '0 - 6'], ['7', 'october 16 , 1983', 'st louis cardinals', 'l 34 - 27', '1:00', 'tampa stadium', '48224', '0 - 7'], ['8', 'october 23 , 1983', 'new orleans saints', 'l 24 - 21', '4:00', 'tampa stadium', '48242', '0 - 8'], ['9', 'october 30 , 1983', 'pittsburgh steelers', 'l 17 - 12', '1:00', 'three rivers stadium', '57648', '0 - 9'], ['10', 'november 6 , 1983', 'minnesota vikings', 'w 17 - 12', '1:00', 'hubert h humphrey metrodome', '59239', '1 - 9'], ['11', 'november 13 , 1983', 'cleveland browns', 'l 20 - 0', '1:00', 'cleveland stadium', '56091', '1 - 10'], ['12', 'november 20 , 1983', 'chicago bears', 'l 27 - 0', '1:00', 'tampa stadium', '36816', '1 - 11'], ['13', 'november 27 , 1983', 'houston oilers', 'w 33 - 24', '1:00', 'tampa stadium', '38625', '2 - 11'], ['14', 'december 4 , 1983', 'san francisco 49ers', 'l 35 - 21', '4:00', 'candlestick park', '49773', '2 - 12'], ['15', 'december 12 , 1983', 'green bay packers', 'l 12 - 9 ot', '9:00', 'tampa stadium', '50763', '2 - 13'], ['16', 'december 18 , 1983', 'detroit lions', 'l 23 - 20', '4:00', 'pontiac silverdome', '78392', '2 - 14']] |
united states presidential election in connecticut , 2004 | https://en.wikipedia.org/wiki/United_States_presidential_election_in_Connecticut%2C_2004 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1756284-1.html.csv | superlative | for the state of connecticut , in the 2004 presidential election , the highest number of votes for bush came from fairfield county . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '7', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'bush'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; bush }'}, 'county'], 'result': 'fairfield', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; bush } ; county }'}, 'fairfield'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; bush } ; county } ; fairfield } = true', 'tointer': 'select the row whose bush record of all rows is maximum . the county record of this row is fairfield .'} | eq { hop { argmax { all_rows ; bush } ; county } ; fairfield } = true | select the row whose bush record of all rows is maximum . the county record of this row is fairfield . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'bush_5': 5, 'county_6': 6, 'fairfield_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'bush_5': 'bush', 'county_6': 'county', 'fairfield_7': 'fairfield'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'bush_5': [0], 'county_6': [1], 'fairfield_7': [2]} | ['county', 'kerry %', 'kerry', 'bush %', 'bush', 'others %', 'others', '2000 result'] | [['hartford', '58.7 %', '229902', '39.5 %', '154919', '1.8 %', '6987', '1.5'], ['middlesex', '56.3 %', '47292', '42.0 %', '35252', '1.7 %', '1440', '+ 1.4'], ['new london', '55.8 %', '66062', '42.2 %', '49931', '2.0 %', '2367', '+ 0.4'], ['tolland', '54.6 %', '39146', '43.6 %', '31245', '1.9 %', '1338', '+ 1.6'], ['new haven', '54.3 %', '199060', '43.8 %', '160390', '1.9 %', '6942', '- 3.7'], ['windham', '52.1 %', '25477', '45.7 %', '22324', '2.2 %', '1060', '+ 2.5'], ['fairfield', '51.4 %', '205902', '47.3 %', '189605', '1.4 %', '5460', '- 0.9']] |
2009 thailand national games | https://en.wikipedia.org/wiki/2009_Thailand_National_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18615220-1.html.csv | ordinal | chonburi province won the second highest amount of silver medals in the 2009 thailand national games . | {'row': '4', 'col': '4', 'order': '2', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'silver', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; silver ; 2 }'}, 'province'], 'result': 'chonburi', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; silver ; 2 } ; province }'}, 'chonburi'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; silver ; 2 } ; province } ; chonburi } = true', 'tointer': 'select the row whose silver record of all rows is 2nd maximum . the province record of this row is chonburi .'} | eq { hop { nth_argmax { all_rows ; silver ; 2 } ; province } ; chonburi } = true | select the row whose silver record of all rows is 2nd maximum . the province record of this row is chonburi . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'silver_5': 5, '2_6': 6, 'province_7': 7, 'chonburi_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'silver_5': 'silver', '2_6': '2', 'province_7': 'province', 'chonburi_8': 'chonburi'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'silver_5': [0], '2_6': [0], 'province_7': [1], 'chonburi_8': [2]} | ['rank', 'province', 'gold', 'silver', 'bronze', 'total'] | [['1', 'bangkok', '129', '114', '80', '323'], ['2', 'suphan buri', '39', '24', '30', '93'], ['3', 'trang', '36', '15', '28', '79'], ['4', 'chonburi', '30', '40', '32', '102'], ['5', 'nakhon ratchasima', '17', '22', '29', '68'], ['6', 'chiang mai', '16', '25', '38', '79'], ['7', 'nonthaburi', '13', '13', '21', '47'], ['8', 'si sa ket', '13', '5', '14', '32'], ['9', 'ubon ratchathani', '12', '8', '25', '45'], ['10', 'samut sakhon', '10', '9', '11', '30']] |
pol espargar \ xc3 \ xb3 | https://en.wikipedia.org/wiki/Pol_Espargar%C3%B3 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16546257-1.html.csv | count | pol espargaró recorded 0 pole positions in a total of four different seasons . | {'scope': 'all', 'criterion': 'equal', 'value': '0', 'result': '4', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'pole', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose pole record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; pole ; 0 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; pole ; 0 } }', 'tointer': 'select the rows whose pole record is equal to 0 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; pole ; 0 } } ; 4 } = true', 'tointer': 'select the rows whose pole record is equal to 0 . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; pole ; 0 } } ; 4 } = true | select the rows whose pole record is equal to 0 . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'pole_5': 5, '0_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'pole_5': 'pole', '0_6': '0', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'pole_5': [0], '0_6': [0], '4_7': [2]} | ['season', 'race', 'podium', 'pole', 'flap'] | [['2006', '7', '0', '0', '0'], ['2007', '17', '1', '0', '0'], ['2008', '14', '3', '2', '1'], ['2009', '16', '5', '1', '1'], ['2010', '17', '12', '0', '3'], ['2011', '17', '2', '0', '1'], ['2012', '17', '11', '8', '5'], ['2013', '16', '10', '5', '4'], ['total', '121', '44', '16', '15']] |
zambia national under - 20 football team | https://en.wikipedia.org/wiki/Zambia_national_under-20_football_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18177477-1.html.csv | count | during the 2009 african youth championship ( qualifiers ) , zambia was the home team 2 times . | {'scope': 'subset', 'criterion': 'equal', 'value': 'zambia', 'result': '2', 'col': '4', 'subset': {'col': '2', 'criterion': 'equal', 'value': '2009 african youth championship ( qualifiers )'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', '2009 african youth championship ( qualifiers )'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; tournament ; 2009 african youth championship ( qualifiers ) }', 'tointer': 'select the rows whose tournament record fuzzily matches to 2009 african youth championship ( qualifiers ) .'}, 'home team', 'zambia'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose tournament record fuzzily matches to 2009 african youth championship ( qualifiers ) . among these rows , select the rows whose home team record fuzzily matches to zambia .', 'tostr': 'filter_eq { filter_eq { all_rows ; tournament ; 2009 african youth championship ( qualifiers ) } ; home team ; zambia }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; tournament ; 2009 african youth championship ( qualifiers ) } ; home team ; zambia } }', 'tointer': 'select the rows whose tournament record fuzzily matches to 2009 african youth championship ( qualifiers ) . among these rows , select the rows whose home team record fuzzily matches to zambia . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; tournament ; 2009 african youth championship ( qualifiers ) } ; home team ; zambia } } ; 2 } = true', 'tointer': 'select the rows whose tournament record fuzzily matches to 2009 african youth championship ( qualifiers ) . among these rows , select the rows whose home team record fuzzily matches to zambia . the number of such rows is 2 .'} | eq { count { filter_eq { filter_eq { all_rows ; tournament ; 2009 african youth championship ( qualifiers ) } ; home team ; zambia } } ; 2 } = true | select the rows whose tournament record fuzzily matches to 2009 african youth championship ( qualifiers ) . among these rows , select the rows whose home team record fuzzily matches to zambia . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'tournament_6': 6, '2009 african youth championship (qualifiers)_7': 7, 'home team_8': 8, 'zambia_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'tournament_6': 'tournament', '2009 african youth championship (qualifiers)_7': '2009 african youth championship ( qualifiers )', 'home team_8': 'home team', 'zambia_9': 'zambia', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'tournament_6': [0], '2009 african youth championship (qualifiers)_7': [0], 'home team_8': [1], 'zambia_9': [1], '2_10': [3]} | ['date', 'tournament', 'location', 'home team', 'away team'] | [['28 may 2008', 'international friendly', 'king abdul aziz makkah', 'saudi arabia', 'zambia'], ['27 june 2008', '2009 african youth championship ( qualifiers )', 'woodlands stadium ndola', 'zambia', 'zambia'], ['29 june 2008', '2009 african youth championship ( qualifiers )', 'woodlands stadium ndola', 'zambia', 'mauritius'], ['13 july 2008', '2009 african youth championship ( qualifiers )', 'stade germain comarmond bambous', 'mauritius', 'zambia'], ['28 september 2008', '2009 african youth championship ( qualifiers )', 'alexandria', 'egypt', 'zambia']] |
1978 green bay packers season | https://en.wikipedia.org/wiki/1978_Green_Bay_Packers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14656160-1.html.csv | superlative | james lofton was the first of these players to be picked for the green bay packers . | {'scope': 'all', 'col_superlative': '2', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '3', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'pick'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; pick }'}, 'player'], 'result': 'james lofton', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; pick } ; player }'}, 'james lofton'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; pick } ; player } ; james lofton } = true', 'tointer': 'select the row whose pick record of all rows is minimum . the player record of this row is james lofton .'} | eq { hop { argmin { all_rows ; pick } ; player } ; james lofton } = true | select the row whose pick record of all rows is minimum . the player record of this row is james lofton . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'pick_5': 5, 'player_6': 6, 'james lofton_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'pick_5': 'pick', 'player_6': 'player', 'james lofton_7': 'james lofton'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'pick_5': [0], 'player_6': [1], 'james lofton_7': [2]} | ['round', 'pick', 'player', 'position', 'school'] | [['1', '6', 'james lofton', 'wide receiver', 'stanford'], ['2', '34', 'michael hunt', 'linebacker', 'minnesota'], ['3', '62', 'estus hood', 'defensive back', 'illinois state'], ['5', '116', 'mike douglass', 'linebacker', 'san diego state'], ['5', '128', 'willie wilder', 'running back', 'florida'], ['6', '144', 'leotis harris', 'guard', 'arkansas'], ['7', '172', 'george plasketes', 'linebacker', 'ole miss'], ['8', '200', 'dennis sproul', 'quarterback', 'arizona state'], ['9', '228', 'keith myers', 'quarterback', 'utah state'], ['10', '256', 'larry key', 'running back', 'florida state'], ['10', '259', 'mark totten', 'center', 'florida'], ['11', '284', 'terry jones', 'defensive tackle', 'alabama'], ['12', '312', 'eason ramson', 'tight end', 'washington state']] |
athletics at the 1998 central american and caribbean games | https://en.wikipedia.org/wiki/Athletics_at_the_1998_Central_American_and_Caribbean_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10535131-3.html.csv | aggregation | in the category of athletics at the 1998 central american and caribbean games , the average number of gold medals won , was 3.29 . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '3.29', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'gold'], 'result': '3.29', 'ind': 0, 'tostr': 'avg { all_rows ; gold }'}, '3.29'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; gold } ; 3.29 } = true', 'tointer': 'the average of the gold record of all rows is 3.29 .'} | round_eq { avg { all_rows ; gold } ; 3.29 } = true | the average of the gold record of all rows is 3.29 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'gold_4': 4, '3.29_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'gold_4': 'gold', '3.29_5': '3.29'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'gold_4': [0], '3.29_5': [1]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'cuba', '19', '13', '12', '44'], ['2', 'mexico', '12', '9', '7', '28'], ['3', 'jamaica', '6', '9', '7', '22'], ['4', 'venezuela', '2', '4', '5', '11'], ['5', 'bahamas', '2', '2', '3', '7'], ['6', 'barbados', '1', '1', '1', '3'], ['7', 'dominican republic', '1', '0', '1', '2'], ['7', 'puerto rico', '1', '0', '1', '2'], ['9', 'us virgin islands', '1', '0', '0', '1'], ['9', 'suriname', '1', '0', '0', '1'], ['11', 'colombia', '0', '4', '6', '10'], ['12', 'trinidad and tobago', '0', '3', '0', '3'], ['13', 'guatemala', '0', '1', '2', '3'], ['14', 'el salvador', '0', '0', '1', '1']] |
list of highest - grossing bollywood films | https://en.wikipedia.org/wiki/List_of_highest-grossing_Bollywood_films | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11872185-4.html.csv | superlative | among the highest-grossing bollywood movies , the earliest one has a lifetime indian distributor share of 99,02,00000 . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '3', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '5', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'year'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; year }'}, 'lifetime india distributor share'], 'result': '99 , 02 , 00000', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; year } ; lifetime india distributor share }'}, '99 , 02 , 00000'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; year } ; lifetime india distributor share } ; 99 , 02 , 00000 } = true', 'tointer': 'select the row whose year record of all rows is minimum . the lifetime india distributor share record of this row is 99 , 02 , 00000 .'} | eq { hop { argmin { all_rows ; year } ; lifetime india distributor share } ; 99 , 02 , 00000 } = true | select the row whose year record of all rows is minimum . the lifetime india distributor share record of this row is 99 , 02 , 00000 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'year_5': 5, 'lifetime india distributor share_6': 6, '99 , 02 , 00000_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'year_5': 'year', 'lifetime india distributor share_6': 'lifetime india distributor share', '99 , 02 , 00000_7': '99 , 02 , 00000'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'year_5': [0], 'lifetime india distributor share_6': [1], '99 , 02 , 00000_7': [2]} | ['rank', 'movie', 'year', 'studio ( s )', 'lifetime india distributor share'] | [['1', 'chennai express', '2013', 'red chillies entertainment', '114 , 25 , 00000'], ['2', 'ek tha tiger', '2012', 'yash raj films', '106 , 00 , 00000'], ['3', '3 idiots', '2009', 'vinod chopra productions', '99 , 02 , 00000'], ['4', 'yeh jawaani hai deewani', '2013', 'dharma productions', '91 , 00 , 00000'], ['5', 'dabangg 2', '2012', 'arbaaz khan productions', '84 , 00 , 00000'], ['6', 'bodyguard', '2011', 'reliance entertainment', '79 , 49 , 00000'], ['7', 'dabangg', '2010', 'arbaaz khan productions', '76 , 84 , 00000'], ['8', 'rowdy rathore', '2012', 'utv motion pictures', '74 , 00 , 00000'], ['9', 'agneepath', '2012', 'dharma productions', '65 , 53 , 00000'], ['10', 'ready', '2011', 't - series', '64 , 58 , 00000']] |
approach and landing tests | https://en.wikipedia.org/wiki/Approach_and_Landing_Tests | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16237630-3.html.csv | count | 8 of the flights did not have a crew operating them . | {'scope': 'all', 'criterion': 'equal', 'value': 'none', 'result': '8', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'crew', 'none'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose crew record fuzzily matches to none .', 'tostr': 'filter_eq { all_rows ; crew ; none }'}], 'result': '8', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; crew ; none } }', 'tointer': 'select the rows whose crew record fuzzily matches to none . the number of such rows is 8 .'}, '8'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; crew ; none } } ; 8 } = true', 'tointer': 'select the rows whose crew record fuzzily matches to none . the number of such rows is 8 .'} | eq { count { filter_eq { all_rows ; crew ; none } } ; 8 } = true | select the rows whose crew record fuzzily matches to none . the number of such rows is 8 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'crew_5': 5, 'none_6': 6, '8_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'crew_5': 'crew', 'none_6': 'none', '8_7': '8'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'crew_5': [0], 'none_6': [0], '8_7': [2]} | ['test flight', 'date', 'speed', 'altitude', 'crew', 'duration', 'comment'] | [['taxi test 1', 'february 15 , 1977', 'mph ( km / h )', 'taxi', 'none', 'taxi', 'concrete runway , tailcone on'], ['taxi test 2', 'february 15 , 1977', 'mph ( km / h )', 'taxi', 'none', 'taxi', 'concrete runway , tailcone on'], ['taxi test 3', 'february 15 , 1977', 'mph ( km / h )', 'taxi', 'none', 'taxi', 'concrete runway , tailcone on'], ['captive - inert flight 1', 'february 18 , 1977', 'mph ( km / h )', '16000ft 4877 m', 'none', '2 h 5 min', 'tailcone on , landed with 747'], ['captive - inert flight 2', 'february 22 , 1977', 'mph ( km / h )', '22600ft 6888 m', 'none', '3 h 13 min', 'tailcone on , landed with 747'], ['captive - inert flight 3', 'february 25 , 1977', 'mph ( km / h )', '26600ft 8108 m', 'none', '2 h 28 min', 'tailcone on , landed with 747'], ['captive - inert flight 4', 'february 28 , 1977', 'mph ( km / h )', '28565ft 8707 m', 'none', '2 h 11 min', 'tailcone on , landed with 747'], ['captive - inert flight 5', 'march 2 , 1977', 'mph ( km / h )', '30000ft 9144 m', 'none', '1 h 39 min', 'tailcone on , landed with 747'], ['captive - active flight 1', 'june 18 , 1977', 'mph ( km / h )', '14970ft 4563 m', 'haise , fullerton', '55 min 46 s', 'tailcone on , landed with 747'], ['captive - active flight 2', 'june 28 , 1977', 'mph ( km / h )', '22030ft 6715 m', 'engle , truly', '62 min 0 s', 'tailcone on , landed with 747'], ['captive - active flight 3', 'july 26 , 1977', 'mph ( km / h )', '30292ft 9233 m', 'haise , fullerton', '59 min 53 s', 'tailcone on , landed with 747'], ['free flight 1', 'august 12 , 1977', 'mph ( km / h )', '24100ft 7346 m', 'haise , fullerton', '5 min 21 s', 'tailcone on , lakebed landing'], ['free flight 2', 'september 13 , 1977', 'mph ( km / h )', '26000ft 7925 m', 'engle , truly', '5 min 28 s', 'tailcone on , lakebed landing'], ['free flight 3', 'september 23 , 1977', 'mph ( km / h )', '24700ft 7529 m', 'haise , fullerton', '5 min 34 s', 'tailcone on , lakebed landing'], ['free flight 4', 'october 12 , 1977', 'mph ( km / h )', '22400ft 6828 m', 'engle , truly', '2 min 34 s', 'tailcone off , lakebed landing'], ['free flight 5', 'october 26 , 1977', 'mph ( km / h )', '19000ft 5791 m', 'haise , fullerton', '2 min 1 s', 'tailcone off , runway landing']] |
1996 masters tournament | https://en.wikipedia.org/wiki/1996_Masters_Tournament | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16514480-5.html.csv | count | in the 1996 masters tournament , seven of the players were from the united states . | {'scope': 'all', 'criterion': 'equal', 'value': 'united states', 'result': '7', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to united states .', 'tostr': 'filter_eq { all_rows ; country ; united states }'}], 'result': '7', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; country ; united states } }', 'tointer': 'select the rows whose country record fuzzily matches to united states . the number of such rows is 7 .'}, '7'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; country ; united states } } ; 7 } = true', 'tointer': 'select the rows whose country record fuzzily matches to united states . the number of such rows is 7 .'} | eq { count { filter_eq { all_rows ; country ; united states } } ; 7 } = true | select the rows whose country record fuzzily matches to united states . the number of such rows is 7 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'country_5': 5, 'united states_6': 6, '7_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'country_5': 'country', 'united states_6': 'united states', '7_7': '7'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'country_5': [0], 'united states_6': [0], '7_7': [2]} | ['place', 'player', 'country', 'score', 'to par', 'money'] | [['1', 'nick faldo', 'england', '69 + 67 + 73 + 67 = 276', '- 12', '450000'], ['2', 'greg norman', 'australia', '63 + 69 + 71 + 78 = 281', '- 7', '270000'], ['3', 'phil mickelson', 'united states', '65 + 73 + 72 + 72 = 282', '- 6', '170000'], ['4', 'frank nobilo', 'new zealand', '71 + 71 + 72 + 69 = 283', '- 5', '120000'], ['t5', 'scott hoch', 'united states', '67 + 73 + 73 + 71 = 284', '- 4', '95000'], ['t5', 'duffy waldorf', 'united states', '72 + 71 + 69 + 72 = 284', '- 4', '95000'], ['t7', 'davis love iii', 'united states', '72 + 71 + 74 + 68 = 285', '- 3', '77933'], ['t7', 'jeff maggert', 'united states', '71 + 73 + 72 + 69 = 285', '- 3', '77933'], ['t7', 'corey pavin', 'united states', '75 + 66 + 73 + 71 = 285', '- 3', '77933'], ['t10', 'david frost', 'south africa', '70 + 68 + 74 + 74 = 286', '- 2', '65000'], ['t10', 'scott mccarron', 'united states', '70 + 70 + 72 + 74 = 286', '- 2', '65000']] |
1919 - 20 ottawa senators season | https://en.wikipedia.org/wiki/1919%E2%80%9320_Ottawa_Senators_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11446913-3.html.csv | comparative | during the 1919 - 20 season , the ottawa senators scored more goals in their game on april 1 than they did in their game on march 30 . | {'row_1': '5', 'row_2': '4', 'col': '5', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'yes', 'diff_result': None} | {'func': 'and', 'args': [{'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'april 1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to april 1 .', 'tostr': 'filter_eq { all_rows ; date ; april 1 }'}, 'record'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; april 1 } ; record }', 'tointer': 'select the rows whose date record fuzzily matches to april 1 . take the record record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'march 30'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to march 30 .', 'tostr': 'filter_eq { all_rows ; date ; march 30 }'}, 'record'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; march 30 } ; record }', 'tointer': 'select the rows whose date record fuzzily matches to march 30 . take the record record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; date ; april 1 } ; record } ; hop { filter_eq { all_rows ; date ; march 30 } ; record } }', 'tointer': 'select the rows whose date record fuzzily matches to april 1 . take the record record of this row . select the rows whose date record fuzzily matches to march 30 . take the record record of this row . the first record is greater than the second record .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'april 1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to april 1 .', 'tostr': 'filter_eq { all_rows ; date ; april 1 }'}, 'record'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; april 1 } ; record }', 'tointer': 'select the rows whose date record fuzzily matches to april 1 . take the record record of this row .'}, '3 - 2'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; date ; april 1 } ; record } ; 3 - 2 }', 'tointer': 'the record record of the first row is 3 - 2 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'march 30'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to march 30 .', 'tostr': 'filter_eq { all_rows ; date ; march 30 }'}, 'record'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; march 30 } ; record }', 'tointer': 'select the rows whose date record fuzzily matches to march 30 . take the record record of this row .'}, '2 - 2'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; date ; march 30 } ; record } ; 2 - 2 }', 'tointer': 'the record record of the second row is 2 - 2 .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; date ; april 1 } ; record } ; 3 - 2 } ; eq { hop { filter_eq { all_rows ; date ; march 30 } ; record } ; 2 - 2 } }', 'tointer': 'the record record of the first row is 3 - 2 . the record record of the second row is 2 - 2 .'}], 'result': True, 'ind': 8, 'tostr': 'and { greater { hop { filter_eq { all_rows ; date ; april 1 } ; record } ; hop { filter_eq { all_rows ; date ; march 30 } ; record } } ; and { eq { hop { filter_eq { all_rows ; date ; april 1 } ; record } ; 3 - 2 } ; eq { hop { filter_eq { all_rows ; date ; march 30 } ; record } ; 2 - 2 } } } = true', 'tointer': 'select the rows whose date record fuzzily matches to april 1 . take the record record of this row . select the rows whose date record fuzzily matches to march 30 . take the record record of this row . the first record is greater than the second record . the record record of the first row is 3 - 2 . the record record of the second row is 2 - 2 .'} | and { greater { hop { filter_eq { all_rows ; date ; april 1 } ; record } ; hop { filter_eq { all_rows ; date ; march 30 } ; record } } ; and { eq { hop { filter_eq { all_rows ; date ; april 1 } ; record } ; 3 - 2 } ; eq { hop { filter_eq { all_rows ; date ; march 30 } ; record } ; 2 - 2 } } } = true | select the rows whose date record fuzzily matches to april 1 . take the record record of this row . select the rows whose date record fuzzily matches to march 30 . take the record record of this row . the first record is greater than the second record . the record record of the first row is 3 - 2 . the record record of the second row is 2 - 2 . | 13 | 9 | {'and_8': 8, 'result_9': 9, 'greater_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'date_11': 11, 'april 1_12': 12, 'record_13': 13, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'date_15': 15, 'march 30_16': 16, 'record_17': 17, 'and_7': 7, 'str_eq_5': 5, '3 - 2_18': 18, 'str_eq_6': 6, '2 - 2_19': 19} | {'and_8': 'and', 'result_9': 'true', 'greater_4': 'greater', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'date_11': 'date', 'april 1_12': 'april 1', 'record_13': 'record', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'date_15': 'date', 'march 30_16': 'march 30', 'record_17': 'record', 'and_7': 'and', 'str_eq_5': 'str_eq', '3 - 2_18': '3 - 2', 'str_eq_6': 'str_eq', '2 - 2_19': '2 - 2'} | {'and_8': [9], 'result_9': [], 'greater_4': [8], 'str_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'date_11': [0], 'april 1_12': [0], 'record_13': [2], 'str_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'date_15': [1], 'march 30_16': [1], 'record_17': [3], 'and_7': [8], 'str_eq_5': [7], '3 - 2_18': [5], 'str_eq_6': [7], '2 - 2_19': [6]} | ['date', 'visitor', 'score', 'home', 'record'] | [['march 22', 'seattle metropolitans', '2 - 3', 'ottawa senators', '1 - 0'], ['march 24', 'seattle metropolitans', '0 - 3', 'ottawa senators', '2 - 0'], ['march 27', 'seattle metropolitans', '3 - 1', 'ottawa senators', '2 - 1'], ['march 30', 'seattle metropolitans', '5 - 2', 'ottawa senators', '2 - 2'], ['april 1', 'seattle metropolitans', '1 - 6', 'ottawa senators', '3 - 2']] |
2005 texas rangers season | https://en.wikipedia.org/wiki/2005_Texas_Rangers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-12125069-2.html.csv | unique | the game played on june 24 was the only game that was won by houston . | {'scope': 'all', 'row': '4', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'houston', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winning team', 'houston'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winning team record fuzzily matches to houston .', 'tostr': 'filter_eq { all_rows ; winning team ; houston }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; winning team ; houston } }', 'tointer': 'select the rows whose winning team record fuzzily matches to houston . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winning team', 'houston'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winning team record fuzzily matches to houston .', 'tostr': 'filter_eq { all_rows ; winning team ; houston }'}, 'date'], 'result': 'june 24', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; winning team ; houston } ; date }'}, 'june 24'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; winning team ; houston } ; date } ; june 24 }', 'tointer': 'the date record of this unqiue row is june 24 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; winning team ; houston } } ; eq { hop { filter_eq { all_rows ; winning team ; houston } ; date } ; june 24 } } = true', 'tointer': 'select the rows whose winning team record fuzzily matches to houston . there is only one such row in the table . the date record of this unqiue row is june 24 .'} | and { only { filter_eq { all_rows ; winning team ; houston } } ; eq { hop { filter_eq { all_rows ; winning team ; houston } ; date } ; june 24 } } = true | select the rows whose winning team record fuzzily matches to houston . there is only one such row in the table . the date record of this unqiue row is june 24 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'winning team_7': 7, 'houston_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, 'june 24_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'winning team_7': 'winning team', 'houston_8': 'houston', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', 'june 24_10': 'june 24'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'winning team_7': [0], 'houston_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], 'june 24_10': [3]} | ['date', 'winning team', 'score', 'winning pitcher', 'losing pitcher', 'attendance', 'location'] | [['may 20', 'texas', '7 - 3', 'kenny rogers', 'brandon backe', '38109', 'arlington'], ['may 21', 'texas', '18 - 3', 'chris young', 'ezequiel astacio', '35781', 'arlington'], ['may 22', 'texas', '2 - 0', 'chan ho park', 'roy oswalt', '40583', 'arlington'], ['june 24', 'houston', '5 - 2', 'roy oswalt', 'ricardo rodríguez', '36199', 'houston'], ['june 25', 'texas', '6 - 5', 'chris young', 'brandon backe', '41868', 'houston']] |
arab maghreb union | https://en.wikipedia.org/wiki/Arab_Maghreb_Union | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2155836-1.html.csv | majority | in the arab maghreb union most of countries with area more than 1000000 km square has population of more than 6 millions in 2011 . | {'scope': 'subset', 'col': '3', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '6', 'subset': {'col': '2', 'criterion': 'greater_than', 'value': '1000000'}} | {'func': 'most_greater', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'area ( km square )', '1000000'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; area ( km square ) ; 1000000 }', 'tointer': 'select the rows whose area ( km square ) record is greater than 1000000 .'}, 'population ( millions , 2011 )', '6'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose area ( km square ) record is greater than 1000000 . for the population ( millions , 2011 ) records of these rows , most of them are greater than 6 .', 'tostr': 'most_greater { filter_greater { all_rows ; area ( km square ) ; 1000000 } ; population ( millions , 2011 ) ; 6 } = true'} | most_greater { filter_greater { all_rows ; area ( km square ) ; 1000000 } ; population ( millions , 2011 ) ; 6 } = true | select the rows whose area ( km square ) record is greater than 1000000 . for the population ( millions , 2011 ) records of these rows , most of them are greater than 6 . | 2 | 2 | {'most_greater_1': 1, 'result_2': 2, 'filter_greater_0': 0, 'all_rows_3': 3, 'area (km square)_4': 4, '1000000_5': 5, 'population (millions , 2011)_6': 6, '6_7': 7} | {'most_greater_1': 'most_greater', 'result_2': 'true', 'filter_greater_0': 'filter_greater', 'all_rows_3': 'all_rows', 'area (km square)_4': 'area ( km square )', '1000000_5': '1000000', 'population (millions , 2011)_6': 'population ( millions , 2011 )', '6_7': '6'} | {'most_greater_1': [2], 'result_2': [], 'filter_greater_0': [1], 'all_rows_3': [0], 'area (km square)_4': [0], '1000000_5': [0], 'population (millions , 2011)_6': [1], '6_7': [1]} | ['country', 'area ( km square )', 'population ( millions , 2011 )', 'gdp ( ppp ) ( usd , per capita )', 'gdp ( nominal ) ( billions usd )', 'hdi ( 2011 )'] | [['algeria', '2381741', '37.1', '7200', '183.4', '0.698 ( medium )'], ['libya', '1759540', '6.7', '14100', '177.9', '0.760 ( high )'], ['mauritania', '1025520', '3.4', '2200', '4', '0.453 ( low )'], ['morocco', '710 850', '32.3', '5100', '103.8', '0.582 ( medium )'], ['tunisia', '163610', '10.7', '9500', '48.9', '0.698 ( high )']] |
national technical university of athens | https://en.wikipedia.org/wiki/National_Technical_University_of_Athens | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1064216-1.html.csv | superlative | the school that has 7 lecturers has the lowest number of assistant professors . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '7', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'assistant professors'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; assistant professors }'}, 'lecturers'], 'result': '7', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; assistant professors } ; lecturers }'}, '7'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; assistant professors } ; lecturers } ; 7 } = true', 'tointer': 'select the row whose assistant professors record of all rows is minimum . the lecturers record of this row is 7 .'} | eq { hop { argmin { all_rows ; assistant professors } ; lecturers } ; 7 } = true | select the row whose assistant professors record of all rows is minimum . the lecturers record of this row is 7 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'assistant professors_5': 5, 'lecturers_6': 6, '7_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'assistant professors_5': 'assistant professors', 'lecturers_6': 'lecturers', '7_7': '7'} | {'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'assistant professors_5': [0], 'lecturers_6': [1], '7_7': [2]} | ['lecturers', 'associate professors', 'assistant professors', 'professors', 'total'] | [['5', '35', '27', '40', '120'], ['9', '10', '8', '58', '96'], ['12', '16', '17', '23', '81'], ['5', '12', '8', '20', '55'], ['18', '20', '9', '34', '119'], ['6', '13', '10', '48', '78'], ['7', '14', '5', '15', '49'], ['4', '10', '9', '14', '51'], ['2', '4', '8', '14', '28']] |
list of cities in the far east by population | https://en.wikipedia.org/wiki/List_of_cities_in_the_Far_East_by_population | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16478687-2.html.csv | superlative | mumbai ( bombay ) has the highest population density among cities in the far east . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '3', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'population density ( people / km square )'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; population density ( people / km square ) }'}, 'metropolitan area'], 'result': 'mumbai ( bombay )', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; population density ( people / km square ) } ; metropolitan area }'}, 'mumbai ( bombay )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; population density ( people / km square ) } ; metropolitan area } ; mumbai ( bombay ) } = true', 'tointer': 'select the row whose population density ( people / km square ) record of all rows is maximum . the metropolitan area record of this row is mumbai ( bombay ) .'} | eq { hop { argmax { all_rows ; population density ( people / km square ) } ; metropolitan area } ; mumbai ( bombay ) } = true | select the row whose population density ( people / km square ) record of all rows is maximum . the metropolitan area record of this row is mumbai ( bombay ) . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'population density (people / km square)_5': 5, 'metropolitan area_6': 6, 'mumbai (bombay)_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'population density (people / km square)_5': 'population density ( people / km square )', 'metropolitan area_6': 'metropolitan area', 'mumbai (bombay)_7': 'mumbai ( bombay )'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'population density (people / km square)_5': [0], 'metropolitan area_6': [1], 'mumbai (bombay)_7': [2]} | ['rank', 'metropolitan area', 'country', 'population', 'area ( km square )', 'population density ( people / km square )'] | [['1', 'tokyo', 'japan', '32450000', '8014', '4049'], ['2', 'seoul', 'south korea', '20550000', '5076', '4048'], ['3', 'mumbai ( bombay )', 'india', '20900000', '8100', '7706'], ['4', 'jakarta', 'indonesia', '18900000', '5100', '3706'], ['5', 'shanghai', 'china', '16650000', '5177', '3216'], ['7', 'hong kong - shenzhen', 'hong kong china', '15800000', '3051', '5179'], ['8', 'beijing', 'china', '12500000', '6562', '1905']] |
list of schools in the hawke 's bay region | https://en.wikipedia.org/wiki/List_of_schools_in_the_Hawke%27s_Bay_Region | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12195931-4.html.csv | unique | only the elsthorpe school in the hawke 's bay region has a decile of 9 . | {'scope': 'all', 'row': '3', 'col': '6', 'col_other': '1', 'criterion': 'equal', 'value': '9', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'decile', '9'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose decile record is equal to 9 .', 'tostr': 'filter_eq { all_rows ; decile ; 9 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; decile ; 9 } }', 'tointer': 'select the rows whose decile record is equal to 9 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'decile', '9'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose decile record is equal to 9 .', 'tostr': 'filter_eq { all_rows ; decile ; 9 }'}, 'name'], 'result': 'elsthorpe school', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; decile ; 9 } ; name }'}, 'elsthorpe school'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; decile ; 9 } ; name } ; elsthorpe school }', 'tointer': 'the name record of this unqiue row is elsthorpe school .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; decile ; 9 } } ; eq { hop { filter_eq { all_rows ; decile ; 9 } ; name } ; elsthorpe school } } = true', 'tointer': 'select the rows whose decile record is equal to 9 . there is only one such row in the table . the name record of this unqiue row is elsthorpe school .'} | and { only { filter_eq { all_rows ; decile ; 9 } } ; eq { hop { filter_eq { all_rows ; decile ; 9 } ; name } ; elsthorpe school } } = true | select the rows whose decile record is equal to 9 . there is only one such row in the table . the name record of this unqiue row is elsthorpe school . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'decile_7': 7, '9_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'elsthorpe school_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'decile_7': 'decile', '9_8': '9', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'elsthorpe school_10': 'elsthorpe school'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'decile_7': [0], '9_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'elsthorpe school_10': [3]} | ['name', 'years', 'gender', 'area', 'authority', 'decile', 'roll'] | [['argyll east school', '1 - 8', 'coed', 'otane', 'state', '4', '51'], ["central hawke 's bay college", '9 - 15', 'coed', 'waipukurau', 'state', '4', '557'], ['elsthorpe school', '1 - 8', 'coed', 'elsthorpe', 'state', '9', '42'], ['flemington school', '1 - 8', 'coed', 'waipukurau', 'state', '8', '71'], ['mangaorapa school', '1 - 8', 'coed', 'porangahau', 'state', '3', '19'], ['omakere school', '1 - 8', 'coed', 'waipawa', 'state', '8', '30'], ['ongaonga school', '1 - 8', 'coed', 'ongaonga', 'state', '6', '111'], ['otane school', '1 - 8', 'coed', 'otane', 'state', '3', '43'], ['porangahau school', '1 - 8', 'coed', 'porangahau', 'state', '4', '31'], ['pukehou school', '1 - 8', 'coed', 'pukehou', 'state', '5', '108'], ['sherwood school', '1 - 8', 'coed', 'takapau', 'state', '6', '30'], ["st joseph 's school", '1 - 8', 'coed', 'waipukurau', 'state integrated', '5', '95'], ['takapau school', '1 - 8', 'coed', 'takapau', 'state', '5', '141'], ['te aute college', '9 - 15', 'boys', 'pukehou', 'state integrated', '3', '86'], ['the terrace school', '1 - 8', 'coed', 'waipukurau', 'state', '2', '214'], ['tikokino school', '1 - 8', 'coed', 'waipawa', 'state', '7', '42'], ['tkkm o takapau', '1 - 8', 'coed', 'takapau', 'state', '3', '37'], ['waipawa school', '1 - 8', 'coed', 'waipawa', 'state', '3', '139'], ['waipukurau school', '1 - 8', 'coed', 'waipukurau', 'state', '3', '243']] |
huntington area rapid transit | https://en.wikipedia.org/wiki/Huntington_Area_Rapid_Transit | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14858130-4.html.csv | majority | all models of the huntington area rapid transit retired fleet had a width of 96 " . | {'scope': 'all', 'col': '4', 'most_or_all': 'all', 'criterion': 'equal', 'value': '96', 'subset': None} | {'func': 'all_eq', 'args': ['all_rows', 'width', '96'], 'result': True, 'ind': 0, 'tointer': 'for the width records of all rows , all of them are equal to 96 .', 'tostr': 'all_eq { all_rows ; width ; 96 } = true'} | all_eq { all_rows ; width ; 96 } = true | for the width records of all rows , all of them are equal to 96 . | 1 | 1 | {'all_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'width_3': 3, '96_4': 4} | {'all_eq_0': 'all_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'width_3': 'width', '96_4': '96'} | {'all_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'width_3': [0], '96_4': [0]} | ['year', 'model', 'length', 'width', 'fleet number'] | [['1970', 'gmc t6h4521a', "35 '", '96', '101 - 103'], ['1977', 'flxible 35096 - 6 - 1', "31 '", '96', '201 - 211'], ['1984', 'orion 01.507', "36 ' 8", '96', '301 - 303'], ['19xx', 'chance rt52', "25 ' 11", '96', "400 's"], ['1993', 'gillig phantom 3096tb', "30 '", '96', '501 - 506'], ['1998', 'gillig phantom 3096tb', "30 '", '96', '603'], ['1999', 'chance rt52', "25 ' 11", '96', '701 - 705']] |
list of zune applications | https://en.wikipedia.org/wiki/List_of_Zune_applications | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18138132-2.html.csv | majority | the majority of applications for the zune were in the utilities category . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'utilities', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'category', 'utilities'], 'result': True, 'ind': 0, 'tointer': 'for the category records of all rows , most of them fuzzily match to utilities .', 'tostr': 'most_eq { all_rows ; category ; utilities } = true'} | most_eq { all_rows ; category ; utilities } = true | for the category records of all rows , most of them fuzzily match to utilities . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'category_3': 3, 'utilities_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'category_3': 'category', 'utilities_4': 'utilities'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'category_3': [0], 'utilities_4': [0]} | ['title', 'developer', 'category', 'function', 'release date', 'version'] | [['calendar', 'matchbox', 'utilities', 'virtual calendar and scheduler', '2011 - 07 - 29', '1.0.0.3'], ['chord finder', 'microsoft', 'utilities', 'virtual guitar used to find s power chord', '2010 - 11 - 17', '1.0'], ['drum machine hd', 'dino games', 'utilities', 'virtual drum kit', '2010 - 10 - 20', '1.0'], ['facebook', 'matchbox', 'social networking', 'facebook application', '2010 - 12 - 16', '1.4'], ['metronome', 'dino games', 'utilities', 'virtual metronome', '2010 - 09 - 09', '1.0'], ['notes', 'microsoft', 'utilities', 'virtual notepad', '2011 - 06 - 23', '1.0'], ['piano', 'microsoft', 'entertainment', 'virtual musical keyboard', '2009 - 11 - 01', '1.0'], ['twitter', 'matchbox', 'social networking', 'twitter application', '2010 - 12 - 16', '1.6'], ['windows live messenger', 'microsoft', 'social networking', 'messenger application', '2010 - 11 - 17', '1.4']] |
high - speed rail in europe | https://en.wikipedia.org/wiki/High-speed_rail_in_Europe | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14227171-10.html.csv | ordinal | the second shortest speed rail in europe is the sofia - dragoman line . | {'row': '6', 'col': '3', 'order': '2', 'col_other': '1', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'length', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; length ; 2 }'}, 'line'], 'result': 'sofia - dragoman', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; length ; 2 } ; line }'}, 'sofia - dragoman'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; length ; 2 } ; line } ; sofia - dragoman } = true', 'tointer': 'select the row whose length record of all rows is 2nd minimum . the line record of this row is sofia - dragoman .'} | eq { hop { nth_argmin { all_rows ; length ; 2 } ; line } ; sofia - dragoman } = true | select the row whose length record of all rows is 2nd minimum . the line record of this row is sofia - dragoman . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'length_5': 5, '2_6': 6, 'line_7': 7, 'sofia - dragoman_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'length_5': 'length', '2_6': '2', 'line_7': 'line', 'sofia - dragoman_8': 'sofia - dragoman'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'length_5': [0], '2_6': [0], 'line_7': [1], 'sofia - dragoman_8': [2]} | ['line', 'speed', 'length', 'construction begun', 'expected start of revenue services'] | [['svilengrad - turkish border', '200 km / h', '19 km', '2010', '2012'], ['dimitrovgrad - svilengrad', '200 km / h', '70 km', '2012', '2013'], ['plovdiv - burgas', '200 km / h', '291 km', '2010', '2013'], ['sofia - plovdiv', '200 km / h', '156 km', '2010', '2015'], ['sofia - radomir', '200 km / h', '53 km', '2014', '2017'], ['sofia - dragoman', '200 km / h', '44 km', '2014', '2017'], ['vidin - sofia', '200 km / h', '222 km', 'unknown', '2020']] |
united states house of representatives elections , 1804 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1804 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2668387-18.html.csv | comparative | matthew clay was first elected before john w. epps was first elected . | {'row_1': '10', 'row_2': '12', 'col': '4', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'matthew clay'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose incumbent record fuzzily matches to matthew clay .', 'tostr': 'filter_eq { all_rows ; incumbent ; matthew clay }'}, 'first elected'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; matthew clay } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to matthew clay . take the first elected record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'john w eppes'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose incumbent record fuzzily matches to john w eppes .', 'tostr': 'filter_eq { all_rows ; incumbent ; john w eppes }'}, 'first elected'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; john w eppes } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to john w eppes . take the first elected record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; incumbent ; matthew clay } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; john w eppes } ; first elected } } = true', 'tointer': 'select the rows whose incumbent record fuzzily matches to matthew clay . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to john w eppes . take the first elected record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; incumbent ; matthew clay } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; john w eppes } ; first elected } } = true | select the rows whose incumbent record fuzzily matches to matthew clay . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to john w eppes . take the first elected record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'incumbent_7': 7, 'matthew clay_8': 8, 'first elected_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'incumbent_11': 11, 'john w eppes_12': 12, 'first elected_13': 13} | {'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'incumbent_7': 'incumbent', 'matthew clay_8': 'matthew clay', 'first elected_9': 'first elected', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'incumbent_11': 'incumbent', 'john w eppes_12': 'john w eppes', 'first elected_13': 'first elected'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'incumbent_7': [0], 'matthew clay_8': [0], 'first elected_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'incumbent_11': [1], 'john w eppes_12': [1], 'first elected_13': [3]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['virginia 1', 'john g jackson', 'democratic - republican', '1803', 're - elected', 'john g jackson ( dr ) 57.2 % thomas wilson ( f ) 42.8 %'], ['virginia 2', 'james stephenson', 'federalist', '1803', 'lost re - election democratic - republican gain', 'john morrow ( dr ) james stephenson ( f )'], ['virginia 3', 'john smith', 'democratic - republican', '1801', 're - elected', 'john smith ( dr )'], ['virginia 4', 'david holmes', 'democratic - republican', '1797', 're - elected', 'david holmes ( dr )'], ['virginia 6', 'abram trigg', 'democratic - republican', '1797', 're - elected', 'abram trigg ( dr )'], ['virginia 8', 'walter jones', 'democratic - republican', '1803', 're - elected', 'walter jones ( dr ) 99.0 % henry lee ( f ) 1.0 %'], ['virginia 9', 'philip r thompson', 'democratic - republican', '1793', 're - elected', 'philip r thompson ( dr )'], ['virginia 10', 'john dawson', 'democratic - republican', '1797', 're - elected', 'john dawson ( dr ) 66.2 % james barbour ( quid ) 33.8 %'], ['virginia 13', 'christopher h clark', 'democratic - republican', '1804 ( special )', 're - elected', 'christopher h clark ( dr )'], ['virginia 14', 'matthew clay', 'democratic - republican', '1797', 're - elected', 'matthew clay ( dr ) 88.9 % william lewis ( f ) 11.1 %'], ['virginia 15', 'john randolph', 'democratic - republican', '1799', 're - elected', 'john randolph ( dr )'], ['virginia 16', 'john w eppes', 'democratic - republican', '1803', 're - elected', 'john w eppes ( dr )'], ['virginia 18', 'peterson goodwyn', 'democratic - republican', '1803', 're - elected', 'peterson goodwyn ( dr )'], ['virginia 19', 'edwin gray', 'democratic - republican', '1799', 're - elected', 'edwin gray ( dr )'], ['virginia 20', 'thomas newton , jr', 'democratic - republican', '1799', 're - elected', 'thomas newton , jr ( dr ) 100 %']] |
lee janzen | https://en.wikipedia.org/wiki/Lee_Janzen | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1507431-4.html.csv | count | there were two tournaments where lee janzen had zero top five appearances . | {'scope': 'all', 'criterion': 'equal', 'value': '0', 'result': '2', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'top - 5', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose top - 5 record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; top - 5 ; 0 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; top - 5 ; 0 } }', 'tointer': 'select the rows whose top - 5 record is equal to 0 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; top - 5 ; 0 } } ; 2 } = true', 'tointer': 'select the rows whose top - 5 record is equal to 0 . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; top - 5 ; 0 } } ; 2 } = true | select the rows whose top - 5 record is equal to 0 . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'top - 5_5': 5, '0_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'top - 5_5': 'top - 5', '0_6': '0', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'top - 5_5': [0], '0_6': [0], '2_7': [2]} | ['tournament', 'wins', 'top - 5', 'top - 10', 'top - 25', 'events', 'cuts made'] | [['masters tournament', '0', '0', '0', '3', '12', '9'], ['us open', '2', '2', '3', '6', '19', '11'], ['the open championship', '0', '0', '0', '4', '11', '7'], ['pga championship', '0', '1', '2', '6', '13', '9'], ['totals', '2', '2', '5', '19', '55', '36']] |
three cheers for sweet revenge | https://en.wikipedia.org/wiki/Three_Cheers_for_Sweet_Revenge | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1148083-5.html.csv | unique | with three cheers for sweet revenge , when the region is united states , the only time the format was cd was on june 8 , 2004 . | {'scope': 'subset', 'row': '6', 'col': '4', 'col_other': '1,2', 'criterion': 'equal', 'value': 'cd', 'subset': {'col': '1', 'criterion': 'equal', 'value': 'united states'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'region', 'united states'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; region ; united states }', 'tointer': 'select the rows whose region record fuzzily matches to united states .'}, 'format', 'cd'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose region record fuzzily matches to united states . among these rows , select the rows whose format record fuzzily matches to cd .', 'tostr': 'filter_eq { filter_eq { all_rows ; region ; united states } ; format ; cd }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; region ; united states } ; format ; cd } }', 'tointer': 'select the rows whose region record fuzzily matches to united states . among these rows , select the rows whose format record fuzzily matches to cd . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'region', 'united states'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; region ; united states }', 'tointer': 'select the rows whose region record fuzzily matches to united states .'}, 'format', 'cd'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose region record fuzzily matches to united states . among these rows , select the rows whose format record fuzzily matches to cd .', 'tostr': 'filter_eq { filter_eq { all_rows ; region ; united states } ; format ; cd }'}, 'date'], 'result': 'june 8 , 2004', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; region ; united states } ; format ; cd } ; date }'}, 'june 8 , 2004'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; region ; united states } ; format ; cd } ; date } ; june 8 , 2004 }', 'tointer': 'the date record of this unqiue row is june 8 , 2004 .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; region ; united states } ; format ; cd } } ; eq { hop { filter_eq { filter_eq { all_rows ; region ; united states } ; format ; cd } ; date } ; june 8 , 2004 } } = true', 'tointer': 'select the rows whose region record fuzzily matches to united states . among these rows , select the rows whose format record fuzzily matches to cd . there is only one such row in the table . the date record of this unqiue row is june 8 , 2004 .'} | and { only { filter_eq { filter_eq { all_rows ; region ; united states } ; format ; cd } } ; eq { hop { filter_eq { filter_eq { all_rows ; region ; united states } ; format ; cd } ; date } ; june 8 , 2004 } } = true | select the rows whose region record fuzzily matches to united states . among these rows , select the rows whose format record fuzzily matches to cd . there is only one such row in the table . the date record of this unqiue row is june 8 , 2004 . | 8 | 6 | {'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'region_8': 8, 'united states_9': 9, 'format_10': 10, 'cd_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'date_12': 12, 'june 8 , 2004_13': 13} | {'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'region_8': 'region', 'united states_9': 'united states', 'format_10': 'format', 'cd_11': 'cd', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'date_12': 'date', 'june 8 , 2004_13': 'june 8 , 2004'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'region_8': [0], 'united states_9': [0], 'format_10': [1], 'cd_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'date_12': [3], 'june 8 , 2004_13': [4]} | ['region', 'date', 'label', 'format', 'catalogue'] | [['australia', 'april 11 , 2005', 'reprise', 'cd', '9362486152'], ['japan', 'july 22 , 2004', 'reprise', 'cd', 'wpcr11890'], ['japan', 'january 26 , 2005', 'reprise', 'cd + dvd', 'wpzr30075'], ['japan', 'june 24 , 2009', 'reprise', 'cd', 'wpcr13504'], ['united kingdom', 'september 3 , 2004', 'reprise', 'cd', '9362486152'], ['united states', 'june 8 , 2004', 'reprise', 'cd', '486152'], ['united states', 'december 16 , 2008', 'reprise', '12 vinyl', '148615']] |
list of microcars by country of origin : c | https://en.wikipedia.org/wiki/List_of_microcars_by_country_of_origin%3A_C | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15659438-1.html.csv | majority | most of the microcars were from the former country czechoslovakia . | {'scope': 'all', 'col': '1', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'czechoslovakia', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'country', 'czechoslovakia'], 'result': True, 'ind': 0, 'tointer': 'for the country records of all rows , most of them fuzzily match to czechoslovakia .', 'tostr': 'most_eq { all_rows ; country ; czechoslovakia } = true'} | most_eq { all_rows ; country ; czechoslovakia } = true | for the country records of all rows , most of them fuzzily match to czechoslovakia . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'country_3': 3, 'czechoslovakia_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'country_3': 'country', 'czechoslovakia_4': 'czechoslovakia'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'country_3': [0], 'czechoslovakia_4': [0]} | ['country', 'automobile name', 'manufacturer', 'engine make / capacity', 'year'] | [['china', 'shuanghuan noble', 'shijiazhuang shuanghuan automobile co', '1.1 litre gasoline', '2006 -'], ['croatia', 'dok - ing xd', 'dok - ing , zagreb', '45 kw electric motor ( x2 )', '2010 -'], ['czechoslovakia', 'aero minor', 'letecke zavody np , jinonice', 'jawa 615cc', '1946 - 1952'], ['czechoslovakia', 'avia', 'avia , warsaw', 'jawa 350cc', '1956 - 1958'], ['czechoslovakia', 'oskar 54', 'moto - velo - sport', 'jawa 249cc', '1945 - 1956'], ['czechoslovakia', 'oskar 16 / 250', 'velo , hradec králové', 'jawa 249cc', '1956 - 1963'], ['czechoslovakia', 'velorex 16 / 175', 'velo , hradec králové', 'čz 171cc', '1963 - 1971'], ['czechoslovakia', 'velorex 16 / 350', 'velo , hradec králové', 'jawa 344cc', '1963 - 1971'], ['czechoslovakia', 'velorex 435', 'velo , hradec králové', 'jawa 344cc', '1971 - 1973']] |
2008 - 09 kuwaiti premier league | https://en.wikipedia.org/wiki/2008%E2%80%9309_Kuwaiti_Premier_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18111138-1.html.csv | unique | arabi is the only kuwaiti premier league team from mansuriyah . | {'scope': 'all', 'row': '6', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': 'mansuriyah', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'city', 'mansuriyah'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose city record fuzzily matches to mansuriyah .', 'tostr': 'filter_eq { all_rows ; city ; mansuriyah }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; city ; mansuriyah } }', 'tointer': 'select the rows whose city record fuzzily matches to mansuriyah . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'city', 'mansuriyah'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose city record fuzzily matches to mansuriyah .', 'tostr': 'filter_eq { all_rows ; city ; mansuriyah }'}, 'club'], 'result': 'arabi', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; city ; mansuriyah } ; club }'}, 'arabi'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; city ; mansuriyah } ; club } ; arabi }', 'tointer': 'the club record of this unqiue row is arabi .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; city ; mansuriyah } } ; eq { hop { filter_eq { all_rows ; city ; mansuriyah } ; club } ; arabi } } = true', 'tointer': 'select the rows whose city record fuzzily matches to mansuriyah . there is only one such row in the table . the club record of this unqiue row is arabi .'} | and { only { filter_eq { all_rows ; city ; mansuriyah } } ; eq { hop { filter_eq { all_rows ; city ; mansuriyah } ; club } ; arabi } } = true | select the rows whose city record fuzzily matches to mansuriyah . there is only one such row in the table . the club record of this unqiue row is arabi . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'city_7': 7, 'mansuriyah_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'club_9': 9, 'arabi_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'city_7': 'city', 'mansuriyah_8': 'mansuriyah', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'club_9': 'club', 'arabi_10': 'arabi'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'city_7': [0], 'mansuriyah_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'club_9': [2], 'arabi_10': [3]} | ['club', 'coach', 'city', 'stadium', '2007 - 2008 season'] | [['kazma sporting club', 'marinko koljanin', 'kuwait city', 'al - sadaqua walsalam stadium', '5th'], ['qadsia', 'mohammed ibrahem', 'kuwait city', 'mohammed al - hamad stadium', '2nd'], ['kuwait', 'dragan', 'kuwait city', 'al kuwait sports club stadium', 'champions'], ['salmiya', 'mihai stoichiţă', 'al salmiya', 'thamir stadium', '3rd'], ['al tadamon', 'rashid budaj', 'farwaniya', 'farwaniya stadium', '6th'], ['arabi', 'ahmed khalaf', 'mansuriyah', 'sabah al salem stadium', '4th'], ['al shabab', 'goran', 'al - ahmadi', 'al - ahmadi stadium', 'promoted'], ['al naser', 'maher al - shemari', 'al farwaniyah', 'ali al salem al subah', '7th']] |
1979 - 80 philadelphia flyers season | https://en.wikipedia.org/wiki/1979%E2%80%9380_Philadelphia_Flyers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14208862-5.html.csv | majority | all games of the philadelphia flyers ' in the 1979 - 80 season were scheduled for the month of january . | {'scope': 'all', 'col': '1', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'january', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'date', 'january'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , all of them fuzzily match to january .', 'tostr': 'all_eq { all_rows ; date ; january } = true'} | all_eq { all_rows ; date ; january } = true | for the date records of all rows , all of them fuzzily match to january . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, 'january_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', 'january_4': 'january'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], 'january_4': [0]} | ['date', 'visitor', 'score', 'home', 'decision', 'attendance', 'record'] | [['january 4', 'philadelphia', '5 - 3', 'ny rangers', 'myre', '17398', '25 - 1 - 10'], ['january 6', 'philadelphia', '4 - 2', 'buffalo', 'peeters', '16433', '26 - 1 - 10'], ['january 7', 'philadelphia', '1 - 7', 'minnesota', 'myre', '15962', '26 - 2 - 10'], ['january 10', 'winnipeg', '4 - 5', 'philadelphia', 'peeters', '17077', '27 - 2 - 10'], ['january 12', 'philadelphia', '3 - 4', 'montreal', 'myre', '18091', '27 - 3 - 10'], ['january 13', 'st louis', '1 - 1', 'philadelphia', 'peeters', '17077', '27 - 3 - 11'], ['january 15', 'washington', '4 - 7', 'philadelphia', 'myre', '17077', '28 - 3 - 11'], ['january 17', 'chicago', '1 - 5', 'philadelphia', 'peeters', '17077', '29 - 3 - 11'], ['january 19', 'philadelphia', '4 - 4', 'washington', 'myre', '18130', '29 - 3 - 12'], ['january 22', 'philadelphia', '3 - 1', 'st louis', 'peeters', '17453', '30 - 3 - 12'], ['january 23', 'philadelphia', '4 - 1', 'chicago', 'myre', '17160', '31 - 3 - 12'], ['january 25', 'philadelphia', '5 - 4', 'winnipeg', 'peeters', '15122', '32 - 3 - 12'], ['january 27', 'philadelphia', '5 - 3', 'edmonton', 'peeters', '15423', '33 - 3 - 12'], ['january 31', 'minnesota', '2 - 4', 'philadelphia', 'st croix', '17077', '34 - 3 - 12']] |
list of football clubs in italy | https://en.wikipedia.org/wiki/List_of_football_clubs_in_Italy | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1083851-4.html.csv | majority | for football clubs in italy , stadiums with a capacity of less than 10000 are mostly in division b. | {'scope': 'subset', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'divisione b', 'subset': {'col': '4', 'criterion': 'less_than', 'value': '10000'}} | {'func': 'most_str_eq', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'capacity', '10000'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; capacity ; 10000 }', 'tointer': 'select the rows whose capacity record is less than 10000 .'}, '201112 season', 'divisione b'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose capacity record is less than 10000 . for the 201112 season records of these rows , most of them fuzzily match to divisione b .', 'tostr': 'most_eq { filter_less { all_rows ; capacity ; 10000 } ; 201112 season ; divisione b } = true'} | most_eq { filter_less { all_rows ; capacity ; 10000 } ; 201112 season ; divisione b } = true | select the rows whose capacity record is less than 10000 . for the 201112 season records of these rows , most of them fuzzily match to divisione b . | 2 | 2 | {'most_str_eq_1': 1, 'result_2': 2, 'filter_less_0': 0, 'all_rows_3': 3, 'capacity_4': 4, '10000_5': 5, '201112 season_6': 6, 'divisione b_7': 7} | {'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_less_0': 'filter_less', 'all_rows_3': 'all_rows', 'capacity_4': 'capacity', '10000_5': '10000', '201112 season_6': '201112 season', 'divisione b_7': 'divisione b'} | {'most_str_eq_1': [2], 'result_2': [], 'filter_less_0': [1], 'all_rows_3': [0], 'capacity_4': [0], '10000_5': [0], '201112 season_6': [1], 'divisione b_7': [1]} | ['club', 'city', 'stadium', 'capacity', '201112 season'] | [['andriabat', 'andria', 'degli ulivi', '9140', '12th in lega pro prima divisione b'], ['avellino', 'avellino', 'partenio - lombardi', '7450', '5th in lega pro prima divisione a'], ['barletta', 'barletta', 'cosimo puttilli', '4018', '6th in lega pro prima divisione b'], ['benevento', 'benevento', 'ciro vigorito', '12847', '6th in lega pro prima divisione a'], ['carrarese', 'carrara', 'dei marmi', '9500', '8th in lega pro prima divisione b'], ['catanzaro', 'catanzaro', 'nicola ceravolo', '14650', '2nd in lega pro seconda divisione b'], ['frosinone', 'frosinone', 'matusa', '9680', '9th in lega pro prima divisione b'], ['gubbio', 'gubbio', 'pietro barbetti', '5300', '21st in serie b'], ['latina', 'latina', 'domenico francioni', '6850', '16th in lega pro prima divisione b'], ['nocerina', 'nocera inferiore', 'san francesco', '7632', '20th in serie b'], ['paganese', 'pagani', 'marcello torre', '5900', '6th in lega pro seconda divisione b'], ['perugia', 'perugia', 'renato curi', '28000', '1st in lega pro seconda divisione b'], ['pisa', 'pisa', 'arena garibaldi', '14869', '7th in lega pro prima divisione a'], ['prato', 'prato', 'lungobisenzio', '6750', '14th in lega pro prima divisione b'], ['sorrento', "sorrento ( playing in cava de ' tirreni )", 'simonetta lamberti', '5200', '4th in lega pro prima divisione a'], ['viareggio', 'viareggio', 'torquato bresciani', '7000', '14th in lega pro prima divisione a']] |
andy linden ( racing driver ) | https://en.wikipedia.org/wiki/Andy_Linden_%28racing_driver%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1236025-1.html.csv | unique | andy linden 's race in 1953 was the only time he drove less than 5 laps . | {'scope': 'all', 'row': '3', 'col': '6', 'col_other': '1', 'criterion': 'less_than', 'value': '5', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'laps', '5'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose laps record is less than 5 .', 'tostr': 'filter_less { all_rows ; laps ; 5 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_less { all_rows ; laps ; 5 } }', 'tointer': 'select the rows whose laps record is less than 5 . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'laps', '5'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose laps record is less than 5 .', 'tostr': 'filter_less { all_rows ; laps ; 5 }'}, 'year'], 'result': '1953', 'ind': 2, 'tostr': 'hop { filter_less { all_rows ; laps ; 5 } ; year }'}, '1953'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_less { all_rows ; laps ; 5 } ; year } ; 1953 }', 'tointer': 'the year record of this unqiue row is 1953 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_less { all_rows ; laps ; 5 } } ; eq { hop { filter_less { all_rows ; laps ; 5 } ; year } ; 1953 } } = true', 'tointer': 'select the rows whose laps record is less than 5 . there is only one such row in the table . the year record of this unqiue row is 1953 .'} | and { only { filter_less { all_rows ; laps ; 5 } } ; eq { hop { filter_less { all_rows ; laps ; 5 } ; year } ; 1953 } } = true | select the rows whose laps record is less than 5 . there is only one such row in the table . the year record of this unqiue row is 1953 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_less_0': 0, 'all_rows_6': 6, 'laps_7': 7, '5_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '1953_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_less_0': 'filter_less', 'all_rows_6': 'all_rows', 'laps_7': 'laps', '5_8': '5', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '1953_10': '1953'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_less_0': [1, 2], 'all_rows_6': [0], 'laps_7': [0], '5_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '1953_10': [3]} | ['year', 'start', 'qual', 'rank', 'finish', 'laps'] | [['1951', '31', '132.226', '26', '4', '200'], ['1952', '2', '137.002', '4', '33', '20'], ['1953', '5', '136.060', '19', '33', '3'], ['1954', '23', '137.820', '28', '25', '165'], ['1955', '8', '139.098', '22', '6', '200'], ['1956', '9', '143.056', '11', '27', '90'], ['1957', '12', '143.244', '5', '5', '200']] |
list of malmö ff records and statistics | https://en.wikipedia.org/wiki/List_of_Malm%C3%B6_FF_records_and_statistics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-29701419-2.html.csv | count | according to the list of malmö ff records and statistics , among the football players that scored over 250 total goals , 2 of them had less than 400 total appearances . | {'scope': 'subset', 'criterion': 'less_than', 'value': '400', 'result': '2', 'col': '6', 'subset': {'col': '7', 'criterion': 'greater_than', 'value': '250'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_less', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'total goals', '250'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; total goals ; 250 }', 'tointer': 'select the rows whose total goals record is greater than 250 .'}, 'total appearances', '400'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose total goals record is greater than 250 . among these rows , select the rows whose total appearances record is less than 400 .', 'tostr': 'filter_less { filter_greater { all_rows ; total goals ; 250 } ; total appearances ; 400 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_less { filter_greater { all_rows ; total goals ; 250 } ; total appearances ; 400 } }', 'tointer': 'select the rows whose total goals record is greater than 250 . among these rows , select the rows whose total appearances record is less than 400 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_less { filter_greater { all_rows ; total goals ; 250 } ; total appearances ; 400 } } ; 2 } = true', 'tointer': 'select the rows whose total goals record is greater than 250 . among these rows , select the rows whose total appearances record is less than 400 . the number of such rows is 2 .'} | eq { count { filter_less { filter_greater { all_rows ; total goals ; 250 } ; total appearances ; 400 } } ; 2 } = true | select the rows whose total goals record is greater than 250 . among these rows , select the rows whose total appearances record is less than 400 . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_less_1': 1, 'filter_greater_0': 0, 'all_rows_5': 5, 'total goals_6': 6, '250_7': 7, 'total appearances_8': 8, '400_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_less_1': 'filter_less', 'filter_greater_0': 'filter_greater', 'all_rows_5': 'all_rows', 'total goals_6': 'total goals', '250_7': '250', 'total appearances_8': 'total appearances', '400_9': '400', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_less_1': [2], 'filter_greater_0': [1], 'all_rows_5': [0], 'total goals_6': [0], '250_7': [0], 'total appearances_8': [1], '400_9': [1], '2_10': [3]} | ['name', 'nationality', 'malmö ff career', 'league appearances', 'league goals', 'total appearances', 'total goals'] | [['hans håkansson', 'sweden', '1927 - 1938', '192', '163', '350', '341'], ['bo larsson', 'sweden', '1962 - 1966 1969 - 1979', '302', '119', '546', '289'], ['egon jönsson', 'sweden', '1943 - 1955', '200', '99', '405', '269'], ['börje tapper', 'sweden', '1939 - 1951', '191', '91', '371', '298'], ['thomas sjöberg', 'sweden', '1974 - 1976 1977 - 1978 1979 - 1982', '180', '80', '334', '157'], ['ivar roslund', 'sweden', '1925 - 1937', '169', '71', '311', '179'], ['ingvar rydell', 'sweden', '1948 - 1953', '106', '68', '210', '162'], ['stellan nilsson', 'sweden', '1940 - 1950', '179', '68', '336', '166'], ['gustaf nilsson', 'sweden', '1940 - 1950', '132', '65', '265', '205']] |
list of true jackson , vp episodes | https://en.wikipedia.org/wiki/List_of_True_Jackson%2C_VP_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-20046379-3.html.csv | superlative | of all list of true jackson vp episodes , first episode directed by roger christiansen was aired on january 9 , 2010 . | {'scope': 'subset', 'col_superlative': '6', 'row_superlative': '3', 'value_mentioned': 'yes', 'max_or_min': 'min', 'other_col': '4', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'roger christiansen'}} | {'func': 'eq', 'args': [{'func': 'min', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'directed by', 'roger christiansen'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; directed by ; roger christiansen }', 'tointer': 'select the rows whose directed by record fuzzily matches to roger christiansen .'}, 'original air date'], 'result': 'january 9 , 2010', 'ind': 1, 'tostr': 'min { filter_eq { all_rows ; directed by ; roger christiansen } ; original air date }', 'tointer': 'select the rows whose directed by record fuzzily matches to roger christiansen . the minimum original air date record of these rows is january 9 , 2010 .'}, 'january 9 , 2010'], 'result': True, 'ind': 2, 'tostr': 'eq { min { filter_eq { all_rows ; directed by ; roger christiansen } ; original air date } ; january 9 , 2010 } = true', 'tointer': 'select the rows whose directed by record fuzzily matches to roger christiansen . the minimum original air date record of these rows is january 9 , 2010 .'} | eq { min { filter_eq { all_rows ; directed by ; roger christiansen } ; original air date } ; january 9 , 2010 } = true | select the rows whose directed by record fuzzily matches to roger christiansen . the minimum original air date record of these rows is january 9 , 2010 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'min_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'directed by_5': 5, 'roger christiansen_6': 6, 'original air date_7': 7, 'january 9 , 2010_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'min_1': 'min', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'directed by_5': 'directed by', 'roger christiansen_6': 'roger christiansen', 'original air date_7': 'original air date', 'january 9 , 2010_8': 'january 9 , 2010'} | {'eq_2': [3], 'result_3': [], 'min_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'directed by_5': [0], 'roger christiansen_6': [0], 'original air date_7': [1], 'january 9 , 2010_8': [2]} | ['no in series', 'no in season', 'title', 'directed by', 'written by', 'original air date', 'production code', 'us viewers ( millions )'] | [['27', '1', 'true concert', 'gary halvorson', 'dan kopelman', 'november 14 , 2009', '204', '3.8'], ['30', '4', 'true parade', 'gary halvorson', 'andy gordon', 'december 12 , 2009', '210', 'n / a'], ['31', '5', 'true drama', 'roger christiansen', 'steve joe', 'january 9 , 2010', '211', '3.3'], ['32', '6', 'my boss ate my homework', 'roger christiansen', 'diana sproveri', 'january 16 , 2010', '203', 'n / a'], ['33', '7', 'little buddies', 'adam weissman', 'sib ventress', 'january 30 , 2010', '208', 'n / a'], ['34', '8', 'true valentine', 'gary halvorson', 'sebastian jones', 'february 6 , 2010', '206', 'n / a'], ['35', '9', 'true date', 'dennie gordon', 'steve joe', 'february 20 , 2010', '212', 'n / a'], ['36', '10', 'the hunky librarian', 'roger christiansen', 'sarah jane cunningham & suzie v freeman', 'march 13 , 2010', '209', 'n / a'], ['37', '11', 'saving snackleberry', 'roger christiansen', 'stacey cantwell', 'march 20 , 2010', '213', 'n / a'], ['38', '12', 'pajama party', 'gary halvorson', 'steve joe', 'april 3 , 2010', '207', 'n / a'], ['39', '13', 'the gift', 'roger christiansen', 'andy gordon', 'april 17 , 2010', '205', 'n / a'], ['40', '14', 'true royal', 'roger christiansen', 'sarah jane cunningham & suzie v freeman', 'may 1 , 2010', '214', '3.7'], ['41', '15', 'true fear', 'gary halvorson', 'sib ventress', 'may 8 , 2010', '216', 'n / a'], ['42', '16', 'the reject room', 'gary halvorson', 'dan kopelman', 'may 15 , 2010', '215', 'n / a'], ['43 - 44', '17 - 18', 'mission gone bad trapped in paris', 'gary halvorson', 'andy gordon', 'may 22 , 2010', '218 - 219', '3.4'], ['45', '19', 'heatwave', 'gregg heschong', 'steve joe', 'june 26 , 2010', '217', 'n / a']] |
2008 - 09 football league one | https://en.wikipedia.org/wiki/2008%E2%80%9309_Football_League_One | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-18788823-5.html.csv | count | there were 6 managers whose manner of departure was ' contract terminated ' . | {'scope': 'all', 'criterion': 'equal', 'value': 'contract terminated', 'result': '6', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'manner of departure', 'contract terminated'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose manner of departure record fuzzily matches to contract terminated .', 'tostr': 'filter_eq { all_rows ; manner of departure ; contract terminated }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; manner of departure ; contract terminated } }', 'tointer': 'select the rows whose manner of departure record fuzzily matches to contract terminated . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; manner of departure ; contract terminated } } ; 6 } = true', 'tointer': 'select the rows whose manner of departure record fuzzily matches to contract terminated . the number of such rows is 6 .'} | eq { count { filter_eq { all_rows ; manner of departure ; contract terminated } } ; 6 } = true | select the rows whose manner of departure record fuzzily matches to contract terminated . the number of such rows is 6 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'manner of departure_5': 5, 'contract terminated_6': 6, '6_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'manner of departure_5': 'manner of departure', 'contract terminated_6': 'contract terminated', '6_7': '6'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'manner of departure_5': [0], 'contract terminated_6': [0], '6_7': [2]} | ['team', 'outgoing manager', 'manner of departure', 'date of vacancy', 'replaced by', 'date of appointment', 'position in table'] | [['milton keynes dons', 'paul ince', 'signed by blackburn rovers ( mutual consent )', '22 june 2008', 'roberto di matteo', '2 july 2008', 'pre - season'], ['cheltenham town', 'keith downing', 'mutual consent', '13 september 2008', 'martin allen', '15 september 2008', '24th'], ['colchester united', 'geraint williams', 'mutual consent', '22 september 2008', 'paul lambert', '24 september 2008', '23rd'], ['carlisle united', 'john ward', 'mutual consent', '3 november 2008', 'greg abbott', '5 december 2008', '20th'], ['huddersfield town', 'stan ternent', 'mutual consent', '4 november 2008', 'lee clark', '11 december 2008', '16th'], ['swindon town', 'maurice malpas', 'mutual consent', '14 november 2008', 'danny wilson', '26 december 2008', '16th'], ['crewe alexandra', 'steve holland', 'contract terminated', '18 november 2008', 'guðjón þórðarson', '24 december 2008', '24th'], ['hartlepool united', 'danny wilson', 'contract terminated', '15 december 2008', 'chris turner', '15 december 2008', '13th'], ['leeds united', 'gary mcallister', 'contract terminated', '21 december 2008', 'simon grayson', '23 december 2008', '9th'], ['walsall', 'jimmy mullen', 'contract terminated', '10 january 2009', 'chris hutchings', '20 january 2009', '12th'], ['leyton orient', 'martin ling', 'mutual consent', '18 january 2009', 'geraint williams', '5 february 2009', '21st'], ['yeovil town', 'russell slade', 'contract terminated', '16 february 2009', 'terry skiverton', '18 february 2009', '16th'], ['brighton & hove albion', 'micky adams', 'contract terminated', '21 february 2009', 'russell slade', '6 march 2009', '21st']] |
my love : essential collection | https://en.wikipedia.org/wiki/My_Love%3A_Essential_Collection | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18969843-5.html.csv | comparative | my love was released on columbia records before it was released on legacy recordings . | {'row_1': '5', 'row_2': '6', 'col': '2', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'label', 'columbia'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose label record fuzzily matches to columbia .', 'tostr': 'filter_eq { all_rows ; label ; columbia }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; label ; columbia } ; date }', 'tointer': 'select the rows whose label record fuzzily matches to columbia . take the date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'label', 'legacy recordings'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose label record fuzzily matches to legacy recordings .', 'tostr': 'filter_eq { all_rows ; label ; legacy recordings }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; label ; legacy recordings } ; date }', 'tointer': 'select the rows whose label record fuzzily matches to legacy recordings . take the date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; label ; columbia } ; date } ; hop { filter_eq { all_rows ; label ; legacy recordings } ; date } } = true', 'tointer': 'select the rows whose label record fuzzily matches to columbia . take the date record of this row . select the rows whose label record fuzzily matches to legacy recordings . take the date record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; label ; columbia } ; date } ; hop { filter_eq { all_rows ; label ; legacy recordings } ; date } } = true | select the rows whose label record fuzzily matches to columbia . take the date record of this row . select the rows whose label record fuzzily matches to legacy recordings . take the date record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'label_7': 7, 'columbia_8': 8, 'date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'label_11': 11, 'legacy recordings_12': 12, 'date_13': 13} | {'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'label_7': 'label', 'columbia_8': 'columbia', 'date_9': 'date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'label_11': 'label', 'legacy recordings_12': 'legacy recordings', 'date_13': 'date'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'label_7': [0], 'columbia_8': [0], 'date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'label_11': [1], 'legacy recordings_12': [1], 'date_13': [3]} | ['region', 'date', 'label', 'format', 'catalog'] | [['europe', 'october 24 , 2008', 'columbia', 'cd', '88697400492'], ['europe', 'october 24 , 2008', 'columbia', '2cd', '88697400502'], ['australia', 'october 27 , 2008', 'columbia', '2cd', '88697374522'], ['north america', 'october 28 , 2008', 'columbia', 'cd', '88697411432'], ['north america', 'october 28 , 2008', 'columbia', '2cd', '88697374522'], ['australia', 'july 11 , 2011', 'legacy recordings', '2cd', '88697936772'], ['europe', 'july 15 , 2011', 'legacy recordings', '2cd', '88697936772'], ['north america', 'august 29 , 2011', 'legacy recordings', '3cd', '886979487321'], ['north america', 'september 13 , 2011', 'legacy recordings', '2cd', '886979487222']] |
bwf super series masters finals | https://en.wikipedia.org/wiki/BWF_Super_Series_Masters_Finals | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-20361783-1.html.csv | majority | lee chong wei played 3 out of the 5 games in the bwf super series masters finals men 's singles . | {'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'lee chong wei', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'mens singles', 'lee chong wei'], 'result': True, 'ind': 0, 'tointer': 'for the mens singles records of all rows , most of them fuzzily match to lee chong wei .', 'tostr': 'most_eq { all_rows ; mens singles ; lee chong wei } = true'} | most_eq { all_rows ; mens singles ; lee chong wei } = true | for the mens singles records of all rows , most of them fuzzily match to lee chong wei . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'mens singles_3': 3, 'lee chong wei_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'mens singles_3': 'mens singles', 'lee chong wei_4': 'lee chong wei'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'mens singles_3': [0], 'lee chong wei_4': [0]} | ['year', 'mens singles', 'womens singles', 'mens doubles', 'womens doubles', 'mixed doubles'] | [['2012', 'chen long', 'li xuerui', 'mathias boe carsten mogensen', 'wang xiaoli yu yang', 'joachim fischer nielsen christinna pedersen'], ['2011', 'lin dan', 'wang yihan', 'mathias boe carsten mogensen', 'wang xiaoli yu yang', 'zhang nan zhao yunlei'], ['2010', 'lee chong wei', 'wang shixian', 'mathias boe carsten mogensen', 'wang xiaoli yu yang', 'zhang nan zhao yunlei'], ['2009', 'lee chong wei', 'wong mew choo', 'jung jae - sung lee yong - dae', 'wong pei tty chin eei hui', 'joachim fischer nielsen christinna pedersen'], ['2008', 'lee chong wei', 'zhou mi', 'koo kien keat tan boon heong', 'wong pei tty chin eei hui', 'thomas laybourn kamilla rytter juhl']] |
1990 - 91 dundee united f.c. season | https://en.wikipedia.org/wiki/1990%E2%80%9391_Dundee_United_F.C._season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15255963-3.html.csv | superlative | the game that dundee united f.c. played against motherwell drew the highest attendance in the 1990-91 season . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '6', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'attendance'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; attendance }'}, 'opponent'], 'result': 'motherwell', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; attendance } ; opponent }'}, 'motherwell'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; opponent } ; motherwell } = true', 'tointer': 'select the row whose attendance record of all rows is maximum . the opponent record of this row is motherwell .'} | eq { hop { argmax { all_rows ; attendance } ; opponent } ; motherwell } = true | select the row whose attendance record of all rows is maximum . the opponent record of this row is motherwell . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, 'opponent_6': 6, 'motherwell_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'attendance_5': 'attendance', 'opponent_6': 'opponent', 'motherwell_7': 'motherwell'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], 'opponent_6': [1], 'motherwell_7': [2]} | ['date', 'opponent', 'venue', 'result', 'attendance', 'scorers'] | [['26 january 1990', 'east fife', 'a', '1 - 1', '4947', 'connolly'], ['29 january 1990', 'east fife', 'h', '2 - 1', '7190', 'clark , ferguson'], ['23 february 1990', 'airdrieonians', 'h', '2 - 0', '8648', 'french ( 2 )'], ['13 march 1990', 'dundee', 'h', '3 - 1', '16228', 'mckinnon , jackson , ferguson'], ['6 april 1991', 'st johnstone', 'n', '0 - 0', '16560', 'clark , ferguson'], ['18 may 1991', 'motherwell', 'n', '3 - 4', '57319', "bowman , j o'neil , jackson"]] |
2010 fedex cup playoffs | https://en.wikipedia.org/wiki/2010_FedEx_Cup_Playoffs | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-28498999-3.html.csv | superlative | matt kuchar had the highest placement in the 2010 fedex cup playoffs . | {'scope': 'all', 'col_superlative': '7', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'after'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; after }'}, 'player'], 'result': 'matt kuchar', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; after } ; player }'}, 'matt kuchar'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; after } ; player } ; matt kuchar } = true', 'tointer': 'select the row whose after record of all rows is minimum . the player record of this row is matt kuchar .'} | eq { hop { argmin { all_rows ; after } ; player } ; matt kuchar } = true | select the row whose after record of all rows is minimum . the player record of this row is matt kuchar . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'after_5': 5, 'player_6': 6, 'matt kuchar_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'after_5': 'after', 'player_6': 'player', 'matt kuchar_7': 'matt kuchar'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'after_5': [0], 'player_6': [1], 'matt kuchar_7': [2]} | ['', 'player', 'country', 'score', 'to par', 'winnings', 'after', 'before'] | [['1', 'matt kuchar', 'united states', '68 + 69 + 69 + 66 = 272', '- 12', '1350000', '1', '9'], ['2', 'martin laird', 'scotland', '69 + 67 + 65 + 71 = 272', '- 12', '810000', '3', '95'], ['t3', 'kevin streelman', 'united states', '72 + 63 + 71 + 68 = 274', '- 10', '435000', '18', '102'], ['t3', 'steve stricker', 'united states', '70 + 70 + 68 + 66 = 274', '- 10', '435000', '2', '2'], ['t5', 'jason day', 'australia', '67 + 67 + 70 + 71 = 275', '- 9', '263438', '14', '28'], ['t5', 'ryan palmer', 'united states', '66 + 74 + 66 + 69 = 275', '- 9', '263438', '13', '23'], ['t5', 'rory sabbatini', 'south africa', '68 + 74 + 69 + 64 = 275', '- 9', '263438', '33', '60'], ['t5', 'vaughn taylor', 'united states', '65 + 70 + 71 + 69 = 275', '- 9', '263438', '21', '38'], ['t9', 'dustin johnson', 'united states', '71 + 69 + 64 + 72 = 276', '- 8', '202500', '6', '11'], ['t9', 'adam scott', 'australia', '66 + 71 + 68 + 71 = 276', '- 8', '202500', '19', '32']] |
list of mexican municipalities | https://en.wikipedia.org/wiki/List_of_Mexican_municipalities | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12453743-1.html.csv | comparative | tecate is a bigger mexican municipality by way of squared kilometers than tijuana is . | {'row_1': '3', 'row_2': '4', 'col': '5', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'municipality', 'tecate'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose municipality record fuzzily matches to tecate .', 'tostr': 'filter_eq { all_rows ; municipality ; tecate }'}, 'area ( km2 )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; municipality ; tecate } ; area ( km2 ) }', 'tointer': 'select the rows whose municipality record fuzzily matches to tecate . take the area ( km2 ) record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'municipality', 'tijuana'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose municipality record fuzzily matches to tijuana .', 'tostr': 'filter_eq { all_rows ; municipality ; tijuana }'}, 'area ( km2 )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; municipality ; tijuana } ; area ( km2 ) }', 'tointer': 'select the rows whose municipality record fuzzily matches to tijuana . take the area ( km2 ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; municipality ; tecate } ; area ( km2 ) } ; hop { filter_eq { all_rows ; municipality ; tijuana } ; area ( km2 ) } } = true', 'tointer': 'select the rows whose municipality record fuzzily matches to tecate . take the area ( km2 ) record of this row . select the rows whose municipality record fuzzily matches to tijuana . take the area ( km2 ) record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; municipality ; tecate } ; area ( km2 ) } ; hop { filter_eq { all_rows ; municipality ; tijuana } ; area ( km2 ) } } = true | select the rows whose municipality record fuzzily matches to tecate . take the area ( km2 ) record of this row . select the rows whose municipality record fuzzily matches to tijuana . take the area ( km2 ) record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'municipality_7': 7, 'tecate_8': 8, 'area (km2)_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'municipality_11': 11, 'tijuana_12': 12, 'area (km2)_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'municipality_7': 'municipality', 'tecate_8': 'tecate', 'area (km2)_9': 'area ( km2 )', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'municipality_11': 'municipality', 'tijuana_12': 'tijuana', 'area (km2)_13': 'area ( km2 )'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'municipality_7': [0], 'tecate_8': [0], 'area (km2)_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'municipality_11': [1], 'tijuana_12': [1], 'area (km2)_13': [3]} | ['inegi code', 'municipality', 'municipal seat', 'population ( 2010 )', 'area ( km2 )'] | [['001', 'ensenada', 'ensenada', '466814', '52482.4'], ['002', 'mexicali', 'mexicali', '956826', '13700'], ['003', 'tecate', 'tecate', '101079', '3079'], ['004', 'tijuana', 'tijuana', '1559683', '879.2'], ['005', 'playas de rosarito', 'rosarito', '90668', '513.32']] |
list of vancouver canucks draft picks | https://en.wikipedia.org/wiki/List_of_Vancouver_Canucks_draft_picks | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11636955-5.html.csv | count | vancouver had 2 picks in the 9th round . | {'scope': 'all', 'criterion': 'equal', 'value': '2', 'result': '1', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'reg gp', '2'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose reg gp record is equal to 2 .', 'tostr': 'filter_eq { all_rows ; reg gp ; 2 }'}], 'result': '1', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; reg gp ; 2 } }', 'tointer': 'select the rows whose reg gp record is equal to 2 . the number of such rows is 1 .'}, '1'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; reg gp ; 2 } } ; 1 } = true', 'tointer': 'select the rows whose reg gp record is equal to 2 . the number of such rows is 1 .'} | eq { count { filter_eq { all_rows ; reg gp ; 2 } } ; 1 } = true | select the rows whose reg gp record is equal to 2 . the number of such rows is 1 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'reg gp_5': 5, '2_6': 6, '1_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'reg gp_5': 'reg gp', '2_6': '2', '1_7': '1'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'reg gp_5': [0], '2_6': [0], '1_7': [2]} | ['rd', 'pick', 'player', 'reg gp', 'pl gp'] | [['1', '3', 'dennis ververgaert', '409', '3'], ['1', '9', 'bob dailey', '257', '7'], ['2', '19', 'paulin bordeleau', '183', '5'], ['3', '35', 'paul sheard', '0', '0'], ['4', '51', 'keith mackie', '0', '0'], ['5', '67', "paul o'neil", '5', '0'], ['6', '83', 'jim cowell', '0', '0'], ['7', '99', 'clay hebenton', '0', '0'], ['8', '115', 'john senkpiel', '0', '0'], ['9', '131', 'peter folco', '2', '0'], ['9', '146', 'terry mcdougall', '0', '0']] |
1981 new york yankees season | https://en.wikipedia.org/wiki/1981_New_York_Yankees_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11487949-8.html.csv | aggregation | in the 1981 nyy season games listed , the average attendance was 56346 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '56346', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '56346', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '56346'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 56346 } = true', 'tointer': 'the average of the attendance record of all rows is 56346 .'} | round_eq { avg { all_rows ; attendance } ; 56346 } = true | the average of the attendance record of all rows is 56346 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '56346_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '56346_5': '56346'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '56346_5': [1]} | ['game', 'score', 'date', 'location', 'attendance', 'time of game'] | [['1', 'dodgers - 3 , yankees - 5', 'october 20', 'yankee stadium ( new york )', '56470', '2:32'], ['2', 'dodgers - 0 , yankees - 3', 'october 21', 'yankee stadium ( new york )', '56505', '2:29'], ['3', 'yankees - 4 , dodgers - 5', 'october 23', 'dodger stadium ( los angeles )', '56236', '3:04'], ['4', 'yankees - 7 , dodgers - 8', 'october 24', 'dodger stadium ( los angeles )', '56242', '3:32'], ['5', 'yankees - 1 , dodgers - 2', 'october 25', 'dodger stadium ( los angeles )', '56115', '2:19'], ['6', 'dodgers - 9 , yankees - 2', 'october 28', 'yankee stadium ( new york )', '56513', '3:09']] |
washington redskins draft history | https://en.wikipedia.org/wiki/Washington_Redskins_draft_history | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17100961-5.html.csv | ordinal | in the washington redskins draft history , the 2nd to last player picked was wayne millner . | {'row': '8', 'col': '3', 'order': '2', 'col_other': '4', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'overall', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; overall ; 2 }'}, 'name'], 'result': 'wayne millner', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; overall ; 2 } ; name }'}, 'wayne millner'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; overall ; 2 } ; name } ; wayne millner } = true', 'tointer': 'select the row whose overall record of all rows is 2nd maximum . the name record of this row is wayne millner .'} | eq { hop { nth_argmax { all_rows ; overall ; 2 } ; name } ; wayne millner } = true | select the row whose overall record of all rows is 2nd maximum . the name record of this row is wayne millner . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'overall_5': 5, '2_6': 6, 'name_7': 7, 'wayne millner_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'overall_5': 'overall', '2_6': '2', 'name_7': 'name', 'wayne millner_8': 'wayne millner'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'overall_5': [0], '2_6': [0], 'name_7': [1], 'wayne millner_8': [2]} | ['round', 'pick', 'overall', 'name', 'position', 'college'] | [['1', '2', '2', 'riley smith', 'fb', 'alabama'], ['2', '2', '11', 'keith topping', 'e', 'stanford'], ['3', '2', '20', 'ed smith', 'fb', 'new york'], ['4', '2', '29', 'paul tangora', 'g', 'northwestern'], ['5', '2', '38', 'wilson groseclose', 'ot', 'texas christian'], ['6', '2', '47', 'larry lutz', 'ot', 'california'], ['7', '2', '56', 'don irwin', 'fb', 'colgate'], ['8', '2', '65', 'wayne millner', 'e', 'notre dame'], ['9', '2', '74', 'marcel saunders', 'g', 'loyola']] |
united states presidential election in nevada , 2008 | https://en.wikipedia.org/wiki/United_States_presidential_election_in_Nevada%2C_2008 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-20424014-1.html.csv | count | mccain won only one county by exactly 75.7 % . | {'scope': 'all', 'criterion': 'equal', 'value': '75.7 %', 'result': '1', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'mccain %', '75.7 %'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose mccain % record fuzzily matches to 75.7 % .', 'tostr': 'filter_eq { all_rows ; mccain % ; 75.7 % }'}], 'result': '1', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; mccain % ; 75.7 % } }', 'tointer': 'select the rows whose mccain % record fuzzily matches to 75.7 % . the number of such rows is 1 .'}, '1'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; mccain % ; 75.7 % } } ; 1 } = true', 'tointer': 'select the rows whose mccain % record fuzzily matches to 75.7 % . the number of such rows is 1 .'} | eq { count { filter_eq { all_rows ; mccain % ; 75.7 % } } ; 1 } = true | select the rows whose mccain % record fuzzily matches to 75.7 % . the number of such rows is 1 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'mccain %_5': 5, '75.7%_6': 6, '1_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'mccain %_5': 'mccain %', '75.7%_6': '75.7 %', '1_7': '1'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'mccain %_5': [0], '75.7%_6': [0], '1_7': [2]} | ['county', 'mccain', 'mccain %', 'obama', 'obama %'] | [['carson city', '11419', '48.2 %', '11623', '49.1 %'], ['churchill', '6832', '64.4 %', '3494', '33.0 %'], ['clark', '257078', '39.5 %', '380765', '58.5 %'], ['douglas', '14648', '56.6 %', '10672', '41.2 %'], ['elko', '10969', '68.5 %', '4541', '28.4 %'], ['esmeralda', '303', '69.0 %', '104', '23.7 %'], ['eureka', '564', '75.7 %', '144', '19.3 %'], ['humboldt', '3586', '63.3 %', '1909', '33.7 %'], ['lander', '1466', '69.7 %', '577', '27.5 %'], ['lincoln', '1498', '71.1 %', '518', '24.6 %'], ['lyon', '12154', '57.6 %', '8405', '39.8 %'], ['mineral', '1131', '49.0 %', '1082', '46.9 %'], ['nye', '9537', '54.5 %', '7226', '41.3 %'], ['pershing', '1075', '58.6 %', '673', '36.7 %'], ['storey', '1247', '51.6 %', '1102', '45.6 %'], ['washoe', '76880', '42.6 %', '99671', '55.3 %']] |
2004 in paraguayan football | https://en.wikipedia.org/wiki/2004_in_Paraguayan_football | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14889048-1.html.csv | majority | the majority of clubs in the 2004 paraguayan football league had less than 7 wins . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'less_than', 'value': '7', 'subset': None} | {'func': 'most_less', 'args': ['all_rows', 'wins', '7'], 'result': True, 'ind': 0, 'tointer': 'for the wins records of all rows , most of them are less than 7 .', 'tostr': 'most_less { all_rows ; wins ; 7 } = true'} | most_less { all_rows ; wins ; 7 } = true | for the wins records of all rows , most of them are less than 7 . | 1 | 1 | {'most_less_0': 0, 'result_1': 1, 'all_rows_2': 2, 'wins_3': 3, '7_4': 4} | {'most_less_0': 'most_less', 'result_1': 'true', 'all_rows_2': 'all_rows', 'wins_3': 'wins', '7_4': '7'} | {'most_less_0': [1], 'result_1': [], 'all_rows_2': [0], 'wins_3': [0], '7_4': [0]} | ['position', 'team', 'played', 'wins', 'draws', 'losses', 'scored', 'conceded', 'points'] | [['1', 'cerro porteño', '18', '12', '5', '1', '31', '13', '41'], ['2', 'libertad', '18', '11', '5', '2', '44', '13', '38'], ['3', 'tacuary', '18', '8', '4', '6', '25', '13', '28'], ['4', 'guaraní', '18', '8', '4', '6', '20', '25', '28'], ['5', 'olimpia', '18', '6', '5', '7', '21', '28', '23'], ['6', 'nacional', '18', '5', '5', '8', '19', '24', '20'], ['7', 'sol de américa', '18', '5', '4', '9', '14', '24', '19'], ['8', '12 de octubre', '18', '5', '3', '10', '18', '28', '18'], ['9', 'sportivo luqueño', '18', '3', '8', '7', '19', '30', '17']] |
1977 kentucky wildcats football team | https://en.wikipedia.org/wiki/1977_Kentucky_Wildcats_football_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21063459-1.html.csv | comparative | kentucky wildcats won against mississippi state before winning against vanderbilt . | {'row_1': '5', 'row_2': '9', 'col': '2', 'col_other': '3', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'mississippi state'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose opponent record fuzzily matches to mississippi state .', 'tostr': 'filter_eq { all_rows ; opponent ; mississippi state }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; opponent ; mississippi state } ; date }', 'tointer': 'select the rows whose opponent record fuzzily matches to mississippi state . take the date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'vanderbilt'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to vanderbilt .', 'tostr': 'filter_eq { all_rows ; opponent ; vanderbilt }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; opponent ; vanderbilt } ; date }', 'tointer': 'select the rows whose opponent record fuzzily matches to vanderbilt . take the date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; opponent ; mississippi state } ; date } ; hop { filter_eq { all_rows ; opponent ; vanderbilt } ; date } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to mississippi state . take the date record of this row . select the rows whose opponent record fuzzily matches to vanderbilt . take the date record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; opponent ; mississippi state } ; date } ; hop { filter_eq { all_rows ; opponent ; vanderbilt } ; date } } = true | select the rows whose opponent record fuzzily matches to mississippi state . take the date record of this row . select the rows whose opponent record fuzzily matches to vanderbilt . take the date record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'opponent_7': 7, 'mississippi state_8': 8, 'date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'opponent_11': 11, 'vanderbilt_12': 12, 'date_13': 13} | {'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'opponent_7': 'opponent', 'mississippi state_8': 'mississippi state', 'date_9': 'date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'opponent_11': 'opponent', 'vanderbilt_12': 'vanderbilt', 'date_13': 'date'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'opponent_7': [0], 'mississippi state_8': [0], 'date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'opponent_11': [1], 'vanderbilt_12': [1], 'date_13': [3]} | ['game', 'date', 'opponent', 'result', 'wildcats points', 'opponents', 'record'] | [['1', 'sept 10', 'north carolina', 'win', '10', '7', '1 - 0'], ['2', 'sept 17', 'baylor', 'loss', '6', '21', '1 - 1'], ['3', 'sept 24', '17 west virginia', 'win', '28', '13', '2 - 1'], ['4', 'oct 1', '4 penn state', 'win', '24', '20', '3 - 1 , 16'], ['5', 'oct 8', 'mississippi state', 'win', '23', '7', '4 - 1 , 12'], ['6', 'oct 15', '16 louisiana state', 'win', '33', '13', '5 - 1 , 8'], ['7', 'oct 22', 'georgia', 'win', '33', '0', '6 - 1 , 7'], ['8', 'oct 29', 'virginia tech', 'win', '32', '0', '7 - 1 , 7'], ['9', 'nov 5', 'vanderbilt', 'win', '28', '6', '8 - 1 , 7'], ['10', 'nov 12', 'florida', 'win', '14', '7', '9 - 1 , 7']] |
2010 big east conference football season | https://en.wikipedia.org/wiki/2010_Big_East_Conference_football_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-28298589-4.html.csv | count | three of the games for the sept 11 date were scheduled for 12:00 pm . | {'scope': 'all', 'criterion': 'equal', 'value': '12:00 pm', 'result': '3', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'time', '12:00 pm'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose time record fuzzily matches to 12:00 pm .', 'tostr': 'filter_eq { all_rows ; time ; 12:00 pm }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; time ; 12:00 pm } }', 'tointer': 'select the rows whose time record fuzzily matches to 12:00 pm . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; time ; 12:00 pm } } ; 3 } = true', 'tointer': 'select the rows whose time record fuzzily matches to 12:00 pm . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; time ; 12:00 pm } } ; 3 } = true | select the rows whose time record fuzzily matches to 12:00 pm . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'time_5': 5, '12:00 pm_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'time_5': 'time', '12:00 pm_6': '12:00 pm', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'time_5': [0], '12:00 pm_6': [0], '3_7': [2]} | ['date', 'time', 'visiting team', 'home team', 'site', 'broadcast', 'result', 'attendance'] | [['september 10', '7:00 pm', 'no 23 west virginia', 'marshall', 'joan c edwards stadium huntington , wv', 'espn2', 'w 24 - 21 ot', '41382'], ['september 11', '12:00 pm', 'south florida', 'no 8 florida', 'ben hill griffin stadium gainesville , fl', 'big east network', 'l 14 - 38', '90612'], ['september 11', '12:00 pm', 'indiana state', 'cincinnati', 'nippert stadium cincinnati , oh', 'fsohio', 'w 40 - 7', '30807'], ['september 11', '12:00 pm', 'texas southern', 'connecticut', 'rentschler field east hartford , ct', 'big east network', 'w 62 - 3', '37359'], ['september 11', '1:00 pm', 'new hampshire', 'pittsburgh', 'heinz field pittsburgh , pa', 'espn3.com', 'w 38 - 16', '50120'], ['september 11', '3:30 pm', 'eastern kentucky', 'louisville', "papa john 's cardinal stadium louisville , ky", 'big east network', 'w 23 - 13', '51427'], ['september 11', '7:00 pm', 'syracuse', 'washington', 'husky stadium seattle , wa', 'fsn northwest', 'l 20 - 41', '62418']] |
south asian canadian | https://en.wikipedia.org/wiki/South_Asian_Canadian | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1717824-1.html.csv | comparative | in 2001 there were more south asians in alberta than quebec . | {'row_1': '3', 'row_2': '4', 'col': '4', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'province', 'alberta'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose province record fuzzily matches to alberta .', 'tostr': 'filter_eq { all_rows ; province ; alberta }'}, 'south asians 2011'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; province ; alberta } ; south asians 2011 }', 'tointer': 'select the rows whose province record fuzzily matches to alberta . take the south asians 2011 record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'province', 'quebec'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose province record fuzzily matches to quebec .', 'tostr': 'filter_eq { all_rows ; province ; quebec }'}, 'south asians 2011'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; province ; quebec } ; south asians 2011 }', 'tointer': 'select the rows whose province record fuzzily matches to quebec . take the south asians 2011 record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; province ; alberta } ; south asians 2011 } ; hop { filter_eq { all_rows ; province ; quebec } ; south asians 2011 } } = true', 'tointer': 'select the rows whose province record fuzzily matches to alberta . take the south asians 2011 record of this row . select the rows whose province record fuzzily matches to quebec . take the south asians 2011 record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; province ; alberta } ; south asians 2011 } ; hop { filter_eq { all_rows ; province ; quebec } ; south asians 2011 } } = true | select the rows whose province record fuzzily matches to alberta . take the south asians 2011 record of this row . select the rows whose province record fuzzily matches to quebec . take the south asians 2011 record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'province_7': 7, 'alberta_8': 8, 'south asians 2011_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'province_11': 11, 'quebec_12': 12, 'south asians 2011_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'province_7': 'province', 'alberta_8': 'alberta', 'south asians 2011_9': 'south asians 2011', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'province_11': 'province', 'quebec_12': 'quebec', 'south asians 2011_13': 'south asians 2011'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'province_7': [0], 'alberta_8': [0], 'south asians 2011_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'province_11': [1], 'quebec_12': [1], 'south asians 2011_13': [3]} | ['province', 'south asians 2001', '% 2001', 'south asians 2011', '% 2011'] | [['ontario', '554870', '4.9 %', '1003180', '7.9 %'], ['british columbia', '210295', '5.4 %', '311265', '7.2 %'], ['alberta', '69580', '2.4 %', '159055', '4.4 %'], ['quebec', '59510', '0.8 %', '91400', '1.2 %'], ['manitoba', '12875', '1.2 %', '26220', '2.2 %'], ['saskatchewan', '4090', '0.4 %', '12620', '1.3 %'], ['nova scotia', '2895', '0.3 %', '5935', '0.7 %'], ['new brunswick', '1415', '0.2 %', '3090', '0.4 %'], ['newfoundland and labrador', '1010', '0.2 %', '2005', '0.4 %'], ['prince edward island', '115', '0.1 %', '500', '0.4 %'], ['yukon', '205', '0.7 %', '340', '1.0 %'], ['northwest territories', '190', '0.5 %', '200', '0.5 %'], ['nunavut', '30', '0.1 %', '115', '0.4 %']] |
prr locomotive classification | https://en.wikipedia.org/wiki/PRR_locomotive_classification | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1140522-5.html.csv | majority | the majority of prr class locomotives were used for the purpose of freight service . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'freight', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'service', 'freight'], 'result': True, 'ind': 0, 'tointer': 'for the service records of all rows , most of them fuzzily match to freight .', 'tostr': 'most_eq { all_rows ; service ; freight } = true'} | most_eq { all_rows ; service ; freight } = true | for the service records of all rows , most of them fuzzily match to freight . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'service_3': 3, 'freight_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'service_3': 'service', 'freight_4': 'freight'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'service_3': [0], 'freight_4': [0]} | ['prr class', 'builders model', 'build date', 'total produced', 'wheel arrangement', 'service', 'power output'] | [['fs10', 'h10 - 44', '1948 - 1949', '55', 'b - b', 'switcher', ''], ['fs12', 'h12 - 44', '1952 - 1954', '16', 'b - b', 'switcher', ''], ['ff20', 't erie buil', '1947 - 1948', '36', 'a1a - a1a', 'freight', ''], ['ff20', 'erie built', '1947 - 1948', '12', 'a1a - a1a', 'freight', ''], ['ff16', 'cf - 16 - 4', '1950', '16', 'b - b', 'freight', ''], ['ff16', 'cf - 16 - 4', '1950', '8', 'b - b', 'freight', ''], ['frs - 16', 'h16 - 44', '1952', '10', 'b - b', 'freight', ''], ['frs - 20', 'h20 - 44', '1948 - 1951', '38', 'b - b', 'freight', ''], ['frs - 24', 'h24 - 66', '1948 - 1951', '38', 'c - c', 'freight', '']] |
india at the commonwealth games | https://en.wikipedia.org/wiki/India_at_the_Commonwealth_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16792440-1.html.csv | count | there were four years in which india did not participate in the commonwealth games . | {'scope': 'all', 'criterion': 'equal', 'value': 'did not participate', 'result': '4', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'gold', 'did not participate'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose gold record fuzzily matches to did not participate .', 'tostr': 'filter_eq { all_rows ; gold ; did not participate }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; gold ; did not participate } }', 'tointer': 'select the rows whose gold record fuzzily matches to did not participate . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; gold ; did not participate } } ; 4 } = true', 'tointer': 'select the rows whose gold record fuzzily matches to did not participate . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; gold ; did not participate } } ; 4 } = true | select the rows whose gold record fuzzily matches to did not participate . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'gold_5': 5, 'did not participate_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'gold_5': 'gold', 'did not participate_6': 'did not participate', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'gold_5': [0], 'did not participate_6': [0], '4_7': [2]} | ['year', 'gold', 'silver', 'bronze', 'total'] | [['2010', '38', '27', '36', '101'], ['2006', '22', '17', '10', '49'], ['2002', '30', '22', '13', '69'], ['1998', '7', '10', '8', '25'], ['1994', '6', '11', '7', '24'], ['1990', '13', '8', '11', '32'], ['1986', 'did not participate', 'did not participate', 'did not participate', 'did not participate'], ['1982', '5', '8', '3', '16'], ['1978', '5', '5', '5', '15'], ['1974', '4', '8', '3', '15'], ['1970', '5', '3', '4', '12'], ['1966', '3', '4', '3', '10'], ['1962', 'did not participate', 'did not participate', 'did not participate', 'did not participate'], ['1958', '2', '1', '0', '3'], ['1954', '0', '0', '0', '0'], ['1950', 'did not participate', 'did not participate', 'did not participate', 'did not participate'], ['1938', '0', '0', '0', '0'], ['1934', '0', '0', '1', '1'], ['1930', 'did not participate', 'did not participate', 'did not participate', 'did not participate'], ['total', '140', '124', '104', '372']] |
inside business | https://en.wikipedia.org/wiki/Inside_Business | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10888144-1.html.csv | aggregation | the six seasons of inside business featured an average of around 35-36 episodes . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '38.167', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'episodes'], 'result': '38.167', 'ind': 0, 'tostr': 'avg { all_rows ; episodes }'}, '38.167'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; episodes } ; 38.167 } = true', 'tointer': 'the average of the episodes record of all rows is 38.167 .'} | round_eq { avg { all_rows ; episodes } ; 38.167 } = true | the average of the episodes record of all rows is 38.167 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'episodes_4': 4, '38.167_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'episodes_4': 'episodes', '38.167_5': '38.167'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'episodes_4': [0], '38.167_5': [1]} | ['season no', 'season start', 'season end', 'episodes', 'host'] | [['1', '4 august 2002', '8 december 2002', '19', 'alan kohler'], ['2', '9 february 2003', '30 november 2003', '41', 'alan kohler'], ['3', '15 february 2004', '5 december 2004', '41', 'alan kohler'], ['4', '13 february 2005', '4 december 2005', '42', 'alan kohler'], ['5', '12 february 2006', '10 december 2006', '43', 'alan kohler'], ['6', '11 february 2007', '9 december 2007', '43', 'alan kohler']] |
estonia in the eurovision song contest 2002 | https://en.wikipedia.org/wiki/Estonia_in_the_Eurovision_Song_Contest_2002 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12676284-1.html.csv | aggregation | for estonians in the eurovision song contest in 2002 , the average number of votes is 46.4 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '46.4', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'votes'], 'result': '46.4', 'ind': 0, 'tostr': 'avg { all_rows ; votes }'}, '46.4'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; votes } ; 46.4 } = true', 'tointer': 'the average of the votes record of all rows is 46.4 .'} | round_eq { avg { all_rows ; votes } ; 46.4 } = true | the average of the votes record of all rows is 46.4 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'votes_4': 4, '46.4_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'votes_4': 'votes', '46.4_5': '46.4'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'votes_4': [0], '46.4_5': [1]} | ['draw', 'artist', 'song', 'votes', 'place'] | [['1', 'jaanika vilipo', "i 'm falling", '49', '5'], ['2', 'yvetta kadakas & ivo linna', 'computer love', '14', '10'], ['3', 'maarja kivi', 'a dream', '38', '7'], ['4', 'lea liitmaa & jaagup kreem', 'what if i fell', '31', '9'], ['5', 'gerli padar', 'need a little nothing', '60', '3'], ['6', 'hatuna & riina riistop', 'this is ( what luv can do )', '32', '8'], ['7', 'maarja tãµkke', "i 'll never forget", '51', '4'], ['8', 'nightlight duo & cowboys', 'another country song', '65', '2'], ['9', 'sahlene', 'runaway', '85', '1'], ['10', 'julia hillens', "u ca n't", '39', '6']] |
1958 san francisco 49ers season | https://en.wikipedia.org/wiki/1958_San_Francisco_49ers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18589208-1.html.csv | ordinal | the san francisco 49ers ' game against the pittsburgh steelers was the earliest in the 1958 season . | {'row': '1', 'col': '2', 'order': '1', 'col_other': '3', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'date', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; date ; 1 }'}, 'opponent'], 'result': 'pittsburgh steelers', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; date ; 1 } ; opponent }'}, 'pittsburgh steelers'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; date ; 1 } ; opponent } ; pittsburgh steelers } = true', 'tointer': 'select the row whose date record of all rows is 1st minimum . the opponent record of this row is pittsburgh steelers .'} | eq { hop { nth_argmin { all_rows ; date ; 1 } ; opponent } ; pittsburgh steelers } = true | select the row whose date record of all rows is 1st minimum . the opponent record of this row is pittsburgh steelers . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'date_5': 5, '1_6': 6, 'opponent_7': 7, 'pittsburgh steelers_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'date_5': 'date', '1_6': '1', 'opponent_7': 'opponent', 'pittsburgh steelers_8': 'pittsburgh steelers'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'date_5': [0], '1_6': [0], 'opponent_7': [1], 'pittsburgh steelers_8': [2]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 28 , 1958', 'pittsburgh steelers', 'w 23 - 20', '51856'], ['2', 'october 5 , 1958', 'los angeles rams', 'l 33 - 3', '59826'], ['3', 'october 12 , 1958', 'chicago bears', 'l 28 - 6', '45310'], ['4', 'october 19 , 1958', 'philadelphia eagles', 'w 30 - 24', '33110'], ['5', 'october 26 , 1958', 'chicago bears', 'l 27 - 14', '59441'], ['6', 'november 2 , 1958', 'detroit lions', 'w 24 - 21', '59350'], ['7', 'november 9 , 1958', 'los angeles rams', 'l 56 - 7', '95082'], ['8', 'november 16 , 1958', 'detroit lions', 'l 35 - 21', '54523'], ['9', 'november 23 , 1958', 'green bay packers', 'w 33 - 12', '43819'], ['10', 'november 30 , 1958', 'baltimore colts', 'l 35 - 27', '57557'], ['11', 'december 7 , 1958', 'green bay packers', 'w 48 - 21', '50793'], ['12', 'december 14 , 1958', 'baltimore colts', 'w 21 - 12', '58334']] |
1959 formula one season | https://en.wikipedia.org/wiki/1959_Formula_One_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1140108-6.html.csv | unique | in the 1959 formula one season , when the constructor was cooper-climax , the only time the circuit was goodwood was on march 30 . | {'scope': 'subset', 'row': '1', 'col': '2', 'col_other': '3', 'criterion': 'equal', 'value': 'goodwood', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'cooper - climax'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'constructor', 'cooper - climax'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; constructor ; cooper - climax }', 'tointer': 'select the rows whose constructor record fuzzily matches to cooper - climax .'}, 'circuit', 'goodwood'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose constructor record fuzzily matches to cooper - climax . among these rows , select the rows whose circuit record fuzzily matches to goodwood .', 'tostr': 'filter_eq { filter_eq { all_rows ; constructor ; cooper - climax } ; circuit ; goodwood }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; constructor ; cooper - climax } ; circuit ; goodwood } }', 'tointer': 'select the rows whose constructor record fuzzily matches to cooper - climax . among these rows , select the rows whose circuit record fuzzily matches to goodwood . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'constructor', 'cooper - climax'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; constructor ; cooper - climax }', 'tointer': 'select the rows whose constructor record fuzzily matches to cooper - climax .'}, 'circuit', 'goodwood'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose constructor record fuzzily matches to cooper - climax . among these rows , select the rows whose circuit record fuzzily matches to goodwood .', 'tostr': 'filter_eq { filter_eq { all_rows ; constructor ; cooper - climax } ; circuit ; goodwood }'}, 'date'], 'result': '30 march', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; constructor ; cooper - climax } ; circuit ; goodwood } ; date }'}, '30 march'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; constructor ; cooper - climax } ; circuit ; goodwood } ; date } ; 30 march }', 'tointer': 'the date record of this unqiue row is 30 march .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; constructor ; cooper - climax } ; circuit ; goodwood } } ; eq { hop { filter_eq { filter_eq { all_rows ; constructor ; cooper - climax } ; circuit ; goodwood } ; date } ; 30 march } } = true', 'tointer': 'select the rows whose constructor record fuzzily matches to cooper - climax . among these rows , select the rows whose circuit record fuzzily matches to goodwood . there is only one such row in the table . the date record of this unqiue row is 30 march .'} | and { only { filter_eq { filter_eq { all_rows ; constructor ; cooper - climax } ; circuit ; goodwood } } ; eq { hop { filter_eq { filter_eq { all_rows ; constructor ; cooper - climax } ; circuit ; goodwood } ; date } ; 30 march } } = true | select the rows whose constructor record fuzzily matches to cooper - climax . among these rows , select the rows whose circuit record fuzzily matches to goodwood . there is only one such row in the table . the date record of this unqiue row is 30 march . | 8 | 6 | {'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'constructor_8': 8, 'cooper - climax_9': 9, 'circuit_10': 10, 'goodwood_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'date_12': 12, '30 march_13': 13} | {'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'constructor_8': 'constructor', 'cooper - climax_9': 'cooper - climax', 'circuit_10': 'circuit', 'goodwood_11': 'goodwood', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'date_12': 'date', '30 march_13': '30 march'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'constructor_8': [0], 'cooper - climax_9': [0], 'circuit_10': [1], 'goodwood_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'date_12': [3], '30 march_13': [4]} | ['race name', 'circuit', 'date', 'winning driver', 'constructor', 'report'] | [['vii glover trophy', 'goodwood', '30 march', 'stirling moss', 'cooper - climax', 'report'], ['xiv barc aintree 200', 'aintree', '18 april', 'jean behra', 'ferrari', 'report'], ['xi brdc international trophy', 'silverstone', '2 may', 'jack brabham', 'cooper - climax', 'report'], ['vi international gold cup', 'oulton park', '26 september', 'stirling moss', 'cooper - climax', 'report'], ['iv silver city trophy', 'snetterton', '10 october', 'ron flockhart', 'brm', 'report']] |
midland railway - butterley | https://en.wikipedia.org/wiki/Midland_Railway_%E2%80%93_Butterley | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1167462-1.html.csv | ordinal | the no46233 duchess of sutherland was the third earliest built locomotive in the midland railway - butterley . | {'row': '6', 'col': '5', 'order': '3', 'col_other': '1', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'date', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; date ; 3 }'}, 'number & name'], 'result': 'no46233 duchess of sutherland', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; date ; 3 } ; number & name }'}, 'no46233 duchess of sutherland'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; date ; 3 } ; number & name } ; no46233 duchess of sutherland } = true', 'tointer': 'select the row whose date record of all rows is 3rd minimum . the number & name record of this row is no46233 duchess of sutherland .'} | eq { hop { nth_argmin { all_rows ; date ; 3 } ; number & name } ; no46233 duchess of sutherland } = true | select the row whose date record of all rows is 3rd minimum . the number & name record of this row is no46233 duchess of sutherland . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'date_5': 5, '3_6': 6, 'number & name_7': 7, 'no46233 duchess of sutherland_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'date_5': 'date', '3_6': '3', 'number & name_7': 'number & name', 'no46233 duchess of sutherland_8': 'no46233 duchess of sutherland'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'date_5': [0], '3_6': [0], 'number & name_7': [1], 'no46233 duchess of sutherland_8': [2]} | ['number & name', 'description', 'livery', 'owner ( s )', 'date'] | [['no 1163 whitehead', 'peckett 0 - 4 - 0st', 'green', 'private owner', '1908'], ['no 47327 / 23', 'lms fowler class 3f 0 - 6 - 0t', 's & djr prussian blue', 'derby city council', '1926'], ['castle donington power station no 1', 'rsh 0 - 4 - 0st', 'dark blue', 'midland railway trust', '1954'], ['no 80080', 'br 2 - 6 - 4t class 4 mt', 'br lined black with early crest', 'princess royal class locomotive trust', '1954'], ['no 73129', 'br 4 - 6 - 0 class 5 mt', 'br unlined black with the early crest', 'derby city council', '1956'], ['no46233 duchess of sutherland', 'lms coronation class', 'br green with early crest', 'princess royal class locomotive trust', '1938']] |
jeremy ( song ) | https://en.wikipedia.org/wiki/Jeremy_%28song%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1728643-2.html.csv | aggregation | the average rank for jeremy ( song ) throughout all accolades is about 38 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '38', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'rank'], 'result': '38', 'ind': 0, 'tostr': 'avg { all_rows ; rank }'}, '38'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; rank } ; 38 } = true', 'tointer': 'the average of the rank record of all rows is 38 .'} | round_eq { avg { all_rows ; rank } ; 38 } = true | the average of the rank record of all rows is 38 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'rank_4': 4, '38_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'rank_4': 'rank', '38_5': '38'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'rank_4': [0], '38_5': [1]} | ['publication', 'country', 'accolade', 'year', 'rank'] | [['mtv', 'united states', '100 greatest videos ever made', '1999', '19'], ['rolling stone', 'united states', 'the 100 top music videos', '1993', '36'], ['rolling stone', 'united states', 'the 100 greatest pop songs since the beatles', '2000', '48'], ['vh1', 'united states', '100 best songs of the past 25 years', '2003', '32'], ['vh1', 'united states', "100 greatest songs of the '90s", '2007', '11'], ['kerrang !', 'united kingdom', '100 greatest singles of all time', '2002', '85']] |
2007 - 08 english premiership ( rugby union ) | https://en.wikipedia.org/wiki/2007%E2%80%9308_English_Premiership_%28rugby_union%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10234157-2.html.csv | superlative | charlie hodgson had the highest number of drops among top scorers in the 2007 - 08 english premiership ( rugby union ) . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '2', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'drop'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; drop }'}, 'name'], 'result': 'charlie hodgson', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; drop } ; name }'}, 'charlie hodgson'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; drop } ; name } ; charlie hodgson } = true', 'tointer': 'select the row whose drop record of all rows is maximum . the name record of this row is charlie hodgson .'} | eq { hop { argmax { all_rows ; drop } ; name } ; charlie hodgson } = true | select the row whose drop record of all rows is maximum . the name record of this row is charlie hodgson . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'drop_5': 5, 'name_6': 6, 'charlie hodgson_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'drop_5': 'drop', 'name_6': 'name', 'charlie hodgson_7': 'charlie hodgson'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'drop_5': [0], 'name_6': [1], 'charlie hodgson_7': [2]} | ['points', 'name', 'club', 'tries', 'drop'] | [['207', 'andy goode', 'leicester tigers', '1', '4'], ['201', 'charlie hodgson', 'sale sharks', '0', '9'], ['192', 'danny cipriani', 'london wasps', '6', '0'], ['179', 'glen jackson', 'saracens', '2', '2'], ['178', 'olly barkley', 'bath rugby', '3', '0'], ['152', 'ryan lamb', 'gloucester rugby', '4', '1'], ['127', 'alberto di bernardo', 'leeds carnegie', '0', '5'], ['118', 'shane drahm', 'worcester warriors', '1', '1'], ['115', 'adrian jarvis', 'harlequins', '0', '0'], ['107', 'chris malone', 'harlequins', '2', '2']] |
yakushiji ryōko no kaiki jikenbo | https://en.wikipedia.org/wiki/Yakushiji_Ry%C5%8Dko_no_Kaiki_Jikenbo | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18443854-1.html.csv | superlative | demon skyscraper was the earliest of these books to be published . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'year'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; year }'}, 'english title'], 'result': 'demon skyscraper', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; year } ; english title }'}, 'demon skyscraper'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; year } ; english title } ; demon skyscraper } = true', 'tointer': 'select the row whose year record of all rows is minimum . the english title record of this row is demon skyscraper .'} | eq { hop { argmin { all_rows ; year } ; english title } ; demon skyscraper } = true | select the row whose year record of all rows is minimum . the english title record of this row is demon skyscraper . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'year_5': 5, 'english title_6': 6, 'demon skyscraper_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'year_5': 'year', 'english title_6': 'english title', 'demon skyscraper_7': 'demon skyscraper'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'year_5': [0], 'english title_6': [1], 'demon skyscraper_7': [2]} | ['japanese title', 'english title', 'year', 'first publisher', 'isbn'] | [['魔天楼 ( matenrō )', 'demon skyscraper', '1996', 'kodansha bunko', 'isbn 4 - 06 - 263346 - 9'], ['東京ナイトメア ( tokyo nightmare )', 'tokyo nightmare', '1999', 'kodansha novels', 'isbn 4 - 06 - 182042 - 7'], ['巴里 ・ 妖都変 ( paris yōto - hen )', 'paris , the strange attractive capital', '2000', 'kobunsha kappa novels', 'isbn 4 - 334 - 07371 - 9'], ['クレオパトラの葬送 ( cleopatra no sōsō )', 'funeral of the cleopatra', '2001', 'kodansha novels', 'isbn 4 - 06 - 182197 - 0'], ['黒蜘蛛島 ( black spider island )', 'black spider island', '2003', 'kobunsha kappa novels', 'isbn 4 - 334 - 07541 - x'], ['夜光曲 ( yakōkyoku )', 'luminous song', '2005', 'shodensha non - novels', 'isbn 4 - 396 - 20793 - x'], ['霧の訪問者 ( kiri no hōmonsha )', "visitor 's fog", '2006', 'kodansha novels', 'isbn 4 - 06 - 182499 - 6'], ['水妖日にご用心 ( suiyō - bi ni goyōjin )', 'be careful on wednesday', '2007', 'shodensha non - novels', 'isbn 4 - 396 - 20840 - 5']] |
1995 wta tour | https://en.wikipedia.org/wiki/1995_WTA_Tour | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15866312-10.html.csv | majority | the majority of the matches in the 1995 wta tour were tier ii matches . | {'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'tier ii', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'tier', 'tier ii'], 'result': True, 'ind': 0, 'tointer': 'for the tier records of all rows , most of them fuzzily match to tier ii .', 'tostr': 'most_eq { all_rows ; tier ; tier ii } = true'} | most_eq { all_rows ; tier ; tier ii } = true | for the tier records of all rows , most of them fuzzily match to tier ii . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'tier_3': 3, 'tier ii_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'tier_3': 'tier', 'tier ii_4': 'tier ii'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'tier_3': [0], 'tier ii_4': [0]} | ['week of', 'tier', 'winner', 'runner - up', 'semi finalists'] | [['2 october', 'tier iv', 'shi - ting wang 6 - 1 , 6 - 1', 'jing - qian yi', 'tina križan annabel ellwood'], ['2 october', 'tier iv', 'petra kamstra tina križan 2 - 6 , 6 - 4 , 6 - 1', 'nana miyagi stephanie reece', 'tina križan annabel ellwood'], ['2 october', 'tier i', 'iva majoli 6 - 4 , 6 - 4', 'mary pierce', 'chanda rubin mariaan de swardt'], ['2 october', 'tier i', 'nicole arendt manon bollegraf 6 - 4 , 6 - 7 , 6 - 4', 'chanda rubin caroline vis', 'chanda rubin mariaan de swardt'], ['9 october', 'tier ii', 'iva majoli 6 - 4 , 7 - 6', 'gabriela sabatini', 'anke huber chanda rubin'], ['9 october', 'tier ii', 'gigi fernández natalia zvereva 5 - 7 , 6 - 1 , 6 - 4', 'meredith mcgrath larisa savchenko', 'anke huber chanda rubin'], ['17 october', 'tier ii', 'mary joe fernández 6 - 4 , 7 - 5', 'amanda coetzer', 'kristie boogert magdalena maleeva'], ['17 october', 'tier ii', 'meredith mcgrath larisa savchenko 7 - 5 , 6 - 1', 'lori mcneil helena suková', 'kristie boogert magdalena maleeva'], ['30 october', 'tier iii', 'brenda schultz - mccarthy 7 - 6 , 6 - 2', 'dominique monami', 'lindsay lee rennae stubbs'], ['30 october', 'tier iii', 'nicole arendt manon bollegraf 7 - 6 , 4 - 6 , 6 - 2', 'lisa raymond rennae stubbs', 'lindsay lee rennae stubbs'], ['30 october', 'tier ii', 'magdalena maleeva 6 - 3 , 6 - 4', 'ai sugiyama', 'lindsay davenport mary joe fernández'], ['30 october', 'tier ii', 'lori mcneil helena suková 3 - 6 , 6 - 4 , 6 - 3', 'katrina adams zina garrison - jackson', 'lindsay davenport mary joe fernández']] |
1980 denver broncos season | https://en.wikipedia.org/wiki/1980_Denver_Broncos_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17928023-1.html.csv | superlative | in the 1980 denver broncos season the highest attendance at the mile high satdium was 74970 on september 21st . | {'scope': 'subset', 'col_superlative': '6', 'row_superlative': '3', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '2,5', 'subset': {'col': '5', 'criterion': 'equal', 'value': 'mile high stadium'}} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'max', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'game site', 'mile high stadium'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; game site ; mile high stadium }', 'tointer': 'select the rows whose game site record fuzzily matches to mile high stadium .'}, 'attendance'], 'result': '74970', 'ind': 1, 'tostr': 'max { filter_eq { all_rows ; game site ; mile high stadium } ; attendance }', 'tointer': 'select the rows whose game site record fuzzily matches to mile high stadium . the maximum attendance record of these rows is 74970 .'}, '74970'], 'result': True, 'ind': 2, 'tostr': 'eq { max { filter_eq { all_rows ; game site ; mile high stadium } ; attendance } ; 74970 }', 'tointer': 'select the rows whose game site record fuzzily matches to mile high stadium . the maximum attendance record of these rows is 74970 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'game site', 'mile high stadium'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; game site ; mile high stadium }', 'tointer': 'select the rows whose game site record fuzzily matches to mile high stadium .'}, 'attendance'], 'result': None, 'ind': 3, 'tostr': 'argmax { filter_eq { all_rows ; game site ; mile high stadium } ; attendance }'}, 'date'], 'result': 'sep 21', 'ind': 4, 'tostr': 'hop { argmax { filter_eq { all_rows ; game site ; mile high stadium } ; attendance } ; date }'}, 'sep 21'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { argmax { filter_eq { all_rows ; game site ; mile high stadium } ; attendance } ; date } ; sep 21 }', 'tointer': 'the date record of the row with superlative attendance record is sep 21 .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { max { filter_eq { all_rows ; game site ; mile high stadium } ; attendance } ; 74970 } ; eq { hop { argmax { filter_eq { all_rows ; game site ; mile high stadium } ; attendance } ; date } ; sep 21 } } = true', 'tointer': 'select the rows whose game site record fuzzily matches to mile high stadium . the maximum attendance record of these rows is 74970 . the date record of the row with superlative attendance record is sep 21 .'} | and { eq { max { filter_eq { all_rows ; game site ; mile high stadium } ; attendance } ; 74970 } ; eq { hop { argmax { filter_eq { all_rows ; game site ; mile high stadium } ; attendance } ; date } ; sep 21 } } = true | select the rows whose game site record fuzzily matches to mile high stadium . the maximum attendance record of these rows is 74970 . the date record of the row with superlative attendance record is sep 21 . | 8 | 7 | {'and_6': 6, 'result_7': 7, 'eq_2': 2, 'max_1': 1, 'filter_str_eq_0': 0, 'all_rows_8': 8, 'game site_9': 9, 'mile high stadium_10': 10, 'attendance_11': 11, '74970_12': 12, 'str_eq_5': 5, 'str_hop_4': 4, 'argmax_3': 3, 'attendance_13': 13, 'date_14': 14, 'sep 21_15': 15} | {'and_6': 'and', 'result_7': 'true', 'eq_2': 'eq', 'max_1': 'max', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_8': 'all_rows', 'game site_9': 'game site', 'mile high stadium_10': 'mile high stadium', 'attendance_11': 'attendance', '74970_12': '74970', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'argmax_3': 'argmax', 'attendance_13': 'attendance', 'date_14': 'date', 'sep 21_15': 'sep 21'} | {'and_6': [7], 'result_7': [], 'eq_2': [6], 'max_1': [2], 'filter_str_eq_0': [1, 3], 'all_rows_8': [0], 'game site_9': [0], 'mile high stadium_10': [0], 'attendance_11': [1], '74970_12': [2], 'str_eq_5': [6], 'str_hop_4': [5], 'argmax_3': [4], 'attendance_13': [3], 'date_14': [4], 'sep 21_15': [5]} | ['week', 'date', 'opponent', 'result', 'game site', 'attendance', 'record'] | [['1', 'sep 7', 'philadelphia eagles', 'l 6 - 27', "veteran 's stadium", '70307', '0 - 1'], ['2', 'sep 14', 'dallas cowboys', 'w 41 - 20', 'mile high stadium', '74919', '1 - 1'], ['3', 'sep 21', 'san diego chargers', 'l 13 - 30', 'mile high stadium', '74970', '1 - 2'], ['4', 'sep 29', 'new england patriots', 'l 14 - 23', 'schaefer stadium', '60153', '1 - 3'], ['5', 'oct 5', 'cleveland browns', 'w 19 - 16', 'municipal stadium', '81065', '2 - 3'], ['6', 'oct 13', 'washington redskins', 'w 20 - 17', 'mile high stadium', '74657', '3 - 3'], ['7', 'oct 19', 'kansas city chiefs', 'l 17 - 23', 'mile high stadium', '74459', '3 - 4'], ['8', 'oct 26', 'new york giants', 'w 14 - 9', 'giants stadium', '67598', '4 - 4'], ['9', 'nov 2', 'houston oilers', 'l 16 - 20', 'mile high stadium', '74717', '4 - 5'], ['10', 'nov 9', 'san diego chargers', 'w 20 - 13', 'san diego stadium', '51435', '5 - 5'], ['11', 'nov 16', 'new york jets', 'w 31 - 24', 'mile high stadium', '72114', '6 - 5'], ['12', 'nov 23', 'seattle seahawks', 'w 36 - 20', 'mile high stadium', '73274', '7 - 5'], ['13', 'dec 1', 'oakland raiders', 'l 3 - 9', 'oakland - alameda county coliseum', '51593', '7 - 6'], ['14', 'dec 7', 'kansas city chiefs', 'l 14 - 31', 'arrowhead stadium', '40237', '7 - 7'], ['15', 'dec 14', 'oakland raiders', 'l 21 - 24', 'mile high stadium', '73974', '7 - 8']] |
anwar robinson | https://en.wikipedia.org/wiki/Anwar_Robinson | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1620672-1.html.csv | comparative | anwar robinson 's top 10 week performance was the only time that he had a bottom 2 result . | {'row_1': '6', 'row_2': '6', 'col': '5', 'col_other': '1', 'relation': 'equal', 'record_mentioned': 'yes', 'diff_result': None} | {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'week', 'top 10'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose week record fuzzily matches to top 10 .', 'tostr': 'filter_eq { all_rows ; week ; top 10 }'}, 'result'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; week ; top 10 } ; result }', 'tointer': 'select the rows whose week record fuzzily matches to top 10 . take the result record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'week', 'top 10'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose week record fuzzily matches to top 10 .', 'tostr': 'filter_eq { all_rows ; week ; top 10 }'}, 'result'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; week ; top 10 } ; result }', 'tointer': 'select the rows whose week record fuzzily matches to top 10 . take the result record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { all_rows ; week ; top 10 } ; result } ; hop { filter_eq { all_rows ; week ; top 10 } ; result } }', 'tointer': 'select the rows whose week record fuzzily matches to top 10 . take the result record of this row . select the rows whose week record fuzzily matches to top 10 . take the result record of this row . the first record fuzzily matches to the second record .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'week', 'top 10'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose week record fuzzily matches to top 10 .', 'tostr': 'filter_eq { all_rows ; week ; top 10 }'}, 'result'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; week ; top 10 } ; result }', 'tointer': 'select the rows whose week record fuzzily matches to top 10 . take the result record of this row .'}, 'bottom 2'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; week ; top 10 } ; result } ; bottom 2 }', 'tointer': 'the result record of the first row is bottom 2 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'week', 'top 10'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose week record fuzzily matches to top 10 .', 'tostr': 'filter_eq { all_rows ; week ; top 10 }'}, 'result'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; week ; top 10 } ; result }', 'tointer': 'select the rows whose week record fuzzily matches to top 10 . take the result record of this row .'}, 'bottom 2'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; week ; top 10 } ; result } ; bottom 2 }', 'tointer': 'the result record of the second row is bottom 2 .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; week ; top 10 } ; result } ; bottom 2 } ; eq { hop { filter_eq { all_rows ; week ; top 10 } ; result } ; bottom 2 } }', 'tointer': 'the result record of the first row is bottom 2 . the result record of the second row is bottom 2 .'}], 'result': True, 'ind': 8, 'tostr': 'and { eq { hop { filter_eq { all_rows ; week ; top 10 } ; result } ; hop { filter_eq { all_rows ; week ; top 10 } ; result } } ; and { eq { hop { filter_eq { all_rows ; week ; top 10 } ; result } ; bottom 2 } ; eq { hop { filter_eq { all_rows ; week ; top 10 } ; result } ; bottom 2 } } } = true', 'tointer': 'select the rows whose week record fuzzily matches to top 10 . take the result record of this row . select the rows whose week record fuzzily matches to top 10 . take the result record of this row . the first record fuzzily matches to the second record . the result record of the first row is bottom 2 . the result record of the second row is bottom 2 .'} | and { eq { hop { filter_eq { all_rows ; week ; top 10 } ; result } ; hop { filter_eq { all_rows ; week ; top 10 } ; result } } ; and { eq { hop { filter_eq { all_rows ; week ; top 10 } ; result } ; bottom 2 } ; eq { hop { filter_eq { all_rows ; week ; top 10 } ; result } ; bottom 2 } } } = true | select the rows whose week record fuzzily matches to top 10 . take the result record of this row . select the rows whose week record fuzzily matches to top 10 . take the result record of this row . the first record fuzzily matches to the second record . the result record of the first row is bottom 2 . the result record of the second row is bottom 2 . | 13 | 9 | {'and_8': 8, 'result_9': 9, 'str_eq_4': 4, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'week_11': 11, 'top 10_12': 12, 'result_13': 13, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'week_15': 15, 'top 10_16': 16, 'result_17': 17, 'and_7': 7, 'str_eq_5': 5, 'bottom 2_18': 18, 'str_eq_6': 6, 'bottom 2_19': 19} | {'and_8': 'and', 'result_9': 'true', 'str_eq_4': 'str_eq', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'week_11': 'week', 'top 10_12': 'top 10', 'result_13': 'result', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'week_15': 'week', 'top 10_16': 'top 10', 'result_17': 'result', 'and_7': 'and', 'str_eq_5': 'str_eq', 'bottom 2_18': 'bottom 2', 'str_eq_6': 'str_eq', 'bottom 2_19': 'bottom 2'} | {'and_8': [9], 'result_9': [], 'str_eq_4': [8], 'str_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'week_11': [0], 'top 10_12': [0], 'result_13': [2], 'str_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'week_15': [1], 'top 10_16': [1], 'result_17': [3], 'and_7': [8], 'str_eq_5': [7], 'bottom 2_18': [5], 'str_eq_6': [7], 'bottom 2_19': [6]} | ['week', 'theme', 'song choice', 'original artist', 'result'] | [['top 24 ( 12 men )', "contestant 's choice", 'moon river', 'andy williams', 'safe'], ['top 20 ( 10 men )', "contestant 's choice", "what 's going on", 'marvin gaye', 'safe'], ['top 16 ( 8 men )', "contestant 's choice", 'what a wonderful world', 'louis armstrong', 'safe'], ['top 12', '1960s', 'a house is not a home', 'dionne warwick', 'safe'], ['top 11', 'billboard number ones', "ai n't nobody", 'chaka khan', 'safe'], ['top 10', '1990s', 'i believe i can fly', 'r kelly', 'bottom 2'], ['top 9', 'classic broadway', 'if ever i would leave you', 'from camelot', 'safe'], ['top 8', 'songs from birth year', "i 'll never love this way again", 'dionne warwick', 'safe'], ['top 7', '1970s dance music', 'september', 'earth , wind & fire', 'eliminated']] |
athletics at the 1998 central american and caribbean games | https://en.wikipedia.org/wiki/Athletics_at_the_1998_Central_American_and_Caribbean_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10535131-3.html.csv | superlative | the highest number of gold medals in athletics at the 1998 central american and caribbean games , was won by cuba . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'gold'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; gold }'}, 'nation'], 'result': 'cuba', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; gold } ; nation }'}, 'cuba'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; gold } ; nation } ; cuba } = true', 'tointer': 'select the row whose gold record of all rows is maximum . the nation record of this row is cuba .'} | eq { hop { argmax { all_rows ; gold } ; nation } ; cuba } = true | select the row whose gold record of all rows is maximum . the nation record of this row is cuba . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'gold_5': 5, 'nation_6': 6, 'cuba_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'gold_5': 'gold', 'nation_6': 'nation', 'cuba_7': 'cuba'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'gold_5': [0], 'nation_6': [1], 'cuba_7': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'cuba', '19', '13', '12', '44'], ['2', 'mexico', '12', '9', '7', '28'], ['3', 'jamaica', '6', '9', '7', '22'], ['4', 'venezuela', '2', '4', '5', '11'], ['5', 'bahamas', '2', '2', '3', '7'], ['6', 'barbados', '1', '1', '1', '3'], ['7', 'dominican republic', '1', '0', '1', '2'], ['7', 'puerto rico', '1', '0', '1', '2'], ['9', 'us virgin islands', '1', '0', '0', '1'], ['9', 'suriname', '1', '0', '0', '1'], ['11', 'colombia', '0', '4', '6', '10'], ['12', 'trinidad and tobago', '0', '3', '0', '3'], ['13', 'guatemala', '0', '1', '2', '3'], ['14', 'el salvador', '0', '0', '1', '1']] |
polona hercog | https://en.wikipedia.org/wiki/Polona_Hercog | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17717526-11.html.csv | ordinal | the 4th match polona hercog played was against an opponent from georgia . | {'row': '4', 'col': '1', 'order': '4', 'col_other': '7', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'year', '4'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; year ; 4 }'}, 'against'], 'result': 'georgia', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; year ; 4 } ; against }'}, 'georgia'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; year ; 4 } ; against } ; georgia } = true', 'tointer': 'select the row whose year record of all rows is 4th minimum . the against record of this row is georgia .'} | eq { hop { nth_argmin { all_rows ; year ; 4 } ; against } ; georgia } = true | select the row whose year record of all rows is 4th minimum . the against record of this row is georgia . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'year_5': 5, '4_6': 6, 'against_7': 7, 'georgia_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmin_0': 'nth_argmin', 'all_rows_4': 'all_rows', 'year_5': 'year', '4_6': '4', 'against_7': 'against', 'georgia_8': 'georgia'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'year_5': [0], '4_6': [0], 'against_7': [1], 'georgia_8': [2]} | ['year', 'edition', 'round', 'date', 'location', 'surface', 'against', 'opponent ( s )', 'score', 'result'] | [['2007', 'group i e / a', 'rr', '19 april', 'plovdiv', 'clay', 'estonia', 'anett kaasik margit rüütel', '1 - 6 , 6 - 1 , 6 - 4', '3 - 0'], ['2007', 'group i e / a', 'ppo', '21 april', 'plovdiv', 'clay', 'switzerland', 'patty schnyder', '3 - 6 , 6 - 3 , 2 - 6', '0 - 2'], ['2008', 'group i e / a', 'rr', '31 january', 'budapest', 'carpet', 'belarus', 'ima bohush tatiana poutchek', '1 - 6 , 3 - 6', '0 - 3'], ['2008', 'group i e / a', 'rr', '1 february', 'budapest', 'carpet', 'georgia', 'ekaterine gorgodze', '6 - 7 ( 3 - 7 ) , 7 - 6 ( 7 - 5 ) , 7 - 5', '2 - 0'], ['2010', 'group i e / a', 'rr', 'february 3', 'lisbon', 'hard', 'israël', "shahar pe'er", '1 - 6 , 4 - 6', '2 - 1'], ['2010', 'group i e / a', 'rr', 'february 3', 'lisbon', 'hard', 'israël', "julia glushko shahar pe'er", '6 - 1 , 7 - 6 ( 9 - 7 )', '2 - 1'], ['2010', 'group i e / a', 'rr', '4 february', 'lisbon', 'hard', 'netherlands', 'arantxa rus', '6 - 7 ( 5 - 7 ) , 7 - 5 , 6 - 2', '2 - 1'], ['2010', 'group i e / a', 'rr', '6 february', 'lisbon', 'hard', 'switzerland', 'sarah moundir', '6 - 4 , 6 - 1', '3 - 0'], ['2010', 'world group ii', 'po', '24 - 25 april', 'maribor', 'clay', 'japan', 'ayumi morita', '3 - 6 , 6 - 1 , 6 - 3', '4 - 1'], ['2010', 'world group ii', 'po', '24 - 25 april', 'maribor', 'clay', 'japan', 'kimiko date - krumm', '4 - 6 , 2 - 6', '4 - 1'], ['2011', 'world group ii', 'qf', '5 - 6 february', 'maribor', 'clay', 'germany', 'julia görges', '7 - 5 , 6 - 4', '1 - 4'], ['2011', 'world group ii', 'qf', '5 - 6 february', 'maribor', 'clay', 'germany', 'andrea petkovic', '1 - 6 , 2 - 6', '1 - 4'], ['2011', 'world group ii', 'qf', '5 - 6 february', 'maribor', 'clay', 'germany', 'anna - lena grönefeld tatjana malek', '6 - 7 ( 3 - 7 ) , 0 - 0 ret', '1 - 4'], ['2011', 'world group ii', 'po', '16 - 17 april', 'koper', 'clay', 'canada', 'eugenie bouchard', '6 - 0 , 6 - 4', '3 - 2'], ['2011', 'world group ii', 'po', '16 - 17 april', 'koper', 'clay', 'canada', 'rebecca marino', '5 - 7 , 6 - 2 , 8 - 6', '3 - 2'], ['2011', 'world group ii', 'po', '16 - 17 april', 'koper', 'clay', 'canada', 'sharon fichman rebecca marino', '7 - 6 ( 7 - 5 ) , 6 - 2', '3 - 2'], ['2012', 'world group ii', 'qf', '4 - 5 february', 'hyōgo', 'hard', 'japan', 'kimiko date - krumm', '6 -- 2 , 4 - 6 , 2 - 6', '0 - 5'], ['2012', 'world group ii', 'qf', '4 - 5 february', 'hyōgo', 'hard', 'japan', 'ayumi morita', '6 - 3 , 6 - 7 ( 6 - 8 ) , 1 - 6', '0 - 5']] |
list of major league baseball home run records | https://en.wikipedia.org/wiki/List_of_Major_League_Baseball_home_run_records | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13669614-14.html.csv | comparative | the cleveland indians major league baseball home run record was set at an earlier date than the chicago white sox major league baseball home run record . | {'row_1': '2', 'row_2': '6', 'col': '2', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'cleveland indians'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team record fuzzily matches to cleveland indians .', 'tostr': 'filter_eq { all_rows ; team ; cleveland indians }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; team ; cleveland indians } ; date }', 'tointer': 'select the rows whose team record fuzzily matches to cleveland indians . take the date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'chicago white sox'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose team record fuzzily matches to chicago white sox .', 'tostr': 'filter_eq { all_rows ; team ; chicago white sox }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; team ; chicago white sox } ; date }', 'tointer': 'select the rows whose team record fuzzily matches to chicago white sox . take the date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; team ; cleveland indians } ; date } ; hop { filter_eq { all_rows ; team ; chicago white sox } ; date } } = true', 'tointer': 'select the rows whose team record fuzzily matches to cleveland indians . take the date record of this row . select the rows whose team record fuzzily matches to chicago white sox . take the date record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; team ; cleveland indians } ; date } ; hop { filter_eq { all_rows ; team ; chicago white sox } ; date } } = true | select the rows whose team record fuzzily matches to cleveland indians . take the date record of this row . select the rows whose team record fuzzily matches to chicago white sox . take the date record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'team_7': 7, 'cleveland indians_8': 8, 'date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'team_11': 11, 'chicago white sox_12': 12, 'date_13': 13} | {'less_4': 'less', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'team_7': 'team', 'cleveland indians_8': 'cleveland indians', 'date_9': 'date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'team_11': 'team', 'chicago white sox_12': 'chicago white sox', 'date_13': 'date'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'team_7': [0], 'cleveland indians_8': [0], 'date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'team_11': [1], 'chicago white sox_12': [1], 'date_13': [3]} | ['team', 'date', 'opponent', 'pitcher', 'inn', 'venue'] | [['milwaukee braves', 'june 8 , 1961', 'cincinnati reds', 'jim maloney ( 2 ) marshall bridges', '7th', 'crosley field'], ['cleveland indians', 'july 31 , 1963', 'los angeles angels', 'paul foytack', '6th', 'cleveland stadium'], ['minnesota twins', 'may 2 , 1964', 'kansas city athletics', 'dan pfister ( 3 ) vern handrahan', '11th', 'municipal stadium'], ['los angeles dodgers', 'september 18 , 2006', 'san diego padres', 'jon adkins ( 2 ) trevor hoffman', '9th', 'dodger stadium'], ['boston red sox', 'april 22 , 2007', 'new york yankees', 'chase wright', '3rd', 'fenway park'], ['chicago white sox', 'august 14 , 2008', 'kansas city royals', 'joel peralta ( 3 ) robinson tejeda', '6th', 'us cellular field'], ['arizona diamondbacks', 'august 11 , 2010', 'milwaukee brewers', 'dave bush', '4th', 'miller park']] |
2001 denver broncos season | https://en.wikipedia.org/wiki/2001_Denver_Broncos_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16729083-1.html.csv | count | in the 2001 season , the denver broncos won 8 games . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'w', 'result': '8', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'w'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to w .', 'tostr': 'filter_eq { all_rows ; result ; w }'}], 'result': '8', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; result ; w } }', 'tointer': 'select the rows whose result record fuzzily matches to w . the number of such rows is 8 .'}, '8'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; result ; w } } ; 8 } = true', 'tointer': 'select the rows whose result record fuzzily matches to w . the number of such rows is 8 .'} | eq { count { filter_eq { all_rows ; result ; w } } ; 8 } = true | select the rows whose result record fuzzily matches to w . the number of such rows is 8 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'result_5': 5, 'w_6': 6, '8_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'result_5': 'result', 'w_6': 'w', '8_7': '8'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'result_5': [0], 'w_6': [0], '8_7': [2]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 10 , 2001', 'new york giants', 'w 31 - 20', '75735'], ['2', 'september 23 , 2001', 'arizona cardinals', 'w 38 - 17', '50913'], ['3', 'september 30 , 2001', 'baltimore ravens', 'l 20 - 13', '75082'], ['4', 'october 7 , 2001', 'kansas city chiefs', 'w 20 - 6', '75037'], ['5', 'october 14 , 2001', 'seattle seahawks', 'l 34 - 21', '61837'], ['6', 'october 21 , 2001', 'san diego chargers', 'l 27 - 10', '67521'], ['7', 'october 28 , 2001', 'new england patriots', 'w 31 - 20', '74750'], ['8', 'november 5 , 2001', 'oakland raiders', 'l 38 - 28', '62637'], ['9', 'november 11 , 2001', 'san diego chargers', 'w 26 - 16', '74951'], ['10', 'november 18 , 2001', 'washington redskins', 'l 17 - 10', '74622'], ['11', 'november 22 , 2001', 'dallas cowboys', 'w 26 - 24', '64104'], ['12', 'december 2 , 2001', 'miami dolphins', 'l 21 - 10', '73938'], ['13', 'december 9 , 2001', 'seattle seahawks', 'w 20 - 7', '74524'], ['14', 'december 16 , 2001', 'kansas city chiefs', 'l 26 - 23', '77778'], ['16', 'december 30 , 2001', 'oakland raiders', 'w 23 - 17', '75582'], ['17', 'january 6 , 2002', 'indianapolis colts', 'l 29 - 10', '56192']] |
real salt lake | https://en.wikipedia.org/wiki/Real_Salt_Lake | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1053453-8.html.csv | superlative | nic rimando is has played in the most number of games ( caps ) for real salt lake . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'caps'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; caps }'}, 'player'], 'result': 'nick rimando', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; caps } ; player }'}, 'nick rimando'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; caps } ; player } ; nick rimando } = true', 'tointer': 'select the row whose caps record of all rows is maximum . the player record of this row is nick rimando .'} | eq { hop { argmax { all_rows ; caps } ; player } ; nick rimando } = true | select the row whose caps record of all rows is maximum . the player record of this row is nick rimando . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'caps_5': 5, 'player_6': 6, 'nick rimando_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'caps_5': 'caps', 'player_6': 'player', 'nick rimando_7': 'nick rimando'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'caps_5': [0], 'player_6': [1], 'nick rimando_7': [2]} | ['rank', 'player', 'nation', 'caps', 'goals', 'years'] | [['1', 'nick rimando', 'usa', '201', '0', '2007 - present'], ['2', 'andy williams', 'jam', '189', '14', '2005 - 2011'], ['3', 'kyle beckerman', 'usa', '177', '21', '2007 - present'], ['4', 'chris wingert', 'usa', '174', '1', '2007 - present'], ['5', 'nat borchers', 'usa', '173', '9', '2008 - present'], ['6', 'javier morales', 'arg', '155', '28', '2007 - present'], ['7', 'tony beltran', 'usa', '135', '0', '2008 - present'], ['8', 'ned grabavoy', 'usa', '126', '8', '2009 - present'], ['9', 'fabián espíndola', 'arg', '125', '35', '2007 - 2012'], ['10', 'robbie findley', 'usa', '121', '35', '2007 - 2010 , 2013 - present']] |
1979 new orleans saints season | https://en.wikipedia.org/wiki/1979_New_Orleans_Saints_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18842968-2.html.csv | majority | in the 1979 new orleans saints season , when the game was in december , the saints lost most of the games . | {'scope': 'subset', 'col': '4', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'l', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'december'}} | {'func': 'most_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'december'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; december }', 'tointer': 'select the rows whose date record fuzzily matches to december .'}, 'result', 'l'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to december . for the result records of these rows , most of them fuzzily match to l .', 'tostr': 'most_eq { filter_eq { all_rows ; date ; december } ; result ; l } = true'} | most_eq { filter_eq { all_rows ; date ; december } ; result ; l } = true | select the rows whose date record fuzzily matches to december . for the result records of these rows , most of them fuzzily match to l . | 2 | 2 | {'most_str_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'date_4': 4, 'december_5': 5, 'result_6': 6, 'l_7': 7} | {'most_str_eq_1': 'most_str_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'date_4': 'date', 'december_5': 'december', 'result_6': 'result', 'l_7': 'l'} | {'most_str_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'date_4': [0], 'december_5': [0], 'result_6': [1], 'l_7': [1]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 2 , 1979', 'atlanta falcons', 'l 40 - 34', '70940'], ['2', 'september 9 , 1979', 'green bay packers', 'l 28 - 19', '53184'], ['3', 'september 16 , 1979', 'philadelphia eagles', 'l 26 - 14', '54212'], ['4', 'september 23 , 1979', 'san francisco 49ers', 'w 30 - 21', '39727'], ['5', 'september 30 , 1979', 'new york giants', 'w 24 - 14', '51543'], ['6', 'october 7 , 1979', 'los angeles rams', 'l 35 - 17', '68986'], ['7', 'october 14 , 1979', 'tampa bay buccaneers', 'w 42 - 14', '67640'], ['8', 'october 21 , 1979', 'detroit lions', 'w 17 - 7', '57428'], ['9', 'october 28 , 1979', 'washington redskins', 'w 14 - 10', '52133'], ['10', 'november 4 , 1979', 'denver broncos', 'l 10 - 3', '74482'], ['11', 'november 11 , 1979', 'san francisco 49ers', 'w 31 - 20', '65551'], ['12', 'november 18 , 1979', 'seattle seahawks', 'l 38 - 24', '60055'], ['13', 'november 25 , 1979', 'atlanta falcons', 'w 37 - 6', '42815'], ['14', 'december 3 , 1979', 'oakland raiders', 'l 42 - 35', '65541'], ['15', 'december 9 , 1979', 'san diego chargers', 'l 35 - 0', '61059'], ['16', 'december 16 , 1979', 'los angeles rams', 'w 29 - 14', '53879']] |
economy of south america | https://en.wikipedia.org/wiki/Economy_of_South_America | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1222653-10.html.csv | superlative | considering the economy of south america , the highest exchange rate for 1 dollar is found for the paraguayan guaraní . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '8', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', '1 usd ='], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; 1 usd = }'}, 'currency'], 'result': 'paraguayan guaraní ( pyg )', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; 1 usd = } ; currency }'}, 'paraguayan guaraní ( pyg )'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; 1 usd = } ; currency } ; paraguayan guaraní ( pyg ) } = true', 'tointer': 'select the row whose 1 usd = record of all rows is maximum . the currency record of this row is paraguayan guaraní ( pyg ) .'} | eq { hop { argmax { all_rows ; 1 usd = } ; currency } ; paraguayan guaraní ( pyg ) } = true | select the row whose 1 usd = record of all rows is maximum . the currency record of this row is paraguayan guaraní ( pyg ) . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, '1 usd =_5': 5, 'currency_6': 6, 'paraguayan guaraní (pyg)_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', '1 usd =_5': '1 usd =', 'currency_6': 'currency', 'paraguayan guaraní (pyg)_7': 'paraguayan guaraní ( pyg )'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], '1 usd =_5': [0], 'currency_6': [1], 'paraguayan guaraní (pyg)_7': [2]} | ['country', 'currency', '1 euro =', '1 usd =', 'central bank'] | [['argentina', 'argentine peso ( ars )', '5.72079', '4.34950', 'central bank of argentina'], ['bolivia', 'bolivian boliviano ( bob )', '9.02081', '6.86000', 'central bank of bolivia'], ['brazil', 'brazilian real ( brl )', '2.25592', '1.71577', 'central bank of brazil'], ['chile', 'chilean peso ( clp )', '635.134', '483.050', 'central bank of chile'], ['colombia', 'colombian peso ( cop )', '2353.40', '1790.00', 'bank of the republic'], ['ecuador', 'us dollar ( usd )', '1.46611', '1', 'federal reserve'], ['guyana', 'guyanese dollar ( gyd )', '264.192', '200.950', 'bank of guyana'], ['paraguay', 'paraguayan guaraní ( pyg )', '4500.00', '5916.27', 'central bank of paraguay'], ['peru', 'peruvian nuevo sol ( pen )', '3.53004', '2.68500', 'central reserve bank of peru'], ['suriname', 'surinamese dollar ( srd )', '4.27296', '3.25000', 'central bank of suriname'], ['uruguay', 'uruguayan peso ( uyu )', '25.3797', '19.3000', 'central bank of uruguay'], ['venezuela', 'venezuelan bolívar fuerte ( vef )', '5.65462', '4.30000', 'central bank of venezuela']] |
1933 vfl season | https://en.wikipedia.org/wiki/1933_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10790397-11.html.csv | majority | all games of the 1933 vfl season were played on the 8th of july . | {'scope': 'all', 'col': '7', 'most_or_all': 'all', 'criterion': 'equal', 'value': '8 july 1933', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'date', '8 july 1933'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , all of them fuzzily match to 8 july 1933 .', 'tostr': 'all_eq { all_rows ; date ; 8 july 1933 } = true'} | all_eq { all_rows ; date ; 8 july 1933 } = true | for the date records of all rows , all of them fuzzily match to 8 july 1933 . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, '8 july 1933_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', '8 july 1933_4': '8 july 1933'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], '8 july 1933_4': [0]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['north melbourne', '13.12 ( 90 )', 'south melbourne', '15.13 ( 103 )', 'arden street oval', '15000', '8 july 1933'], ['collingwood', '20.19 ( 139 )', 'essendon', '14.14 ( 98 )', 'victoria park', '8500', '8 july 1933'], ['carlton', '10.10 ( 70 )', 'richmond', '9.13 ( 67 )', 'princes park', '43000', '8 july 1933'], ['melbourne', '21.10 ( 136 )', 'hawthorn', '15.8 ( 98 )', 'mcg', '6877', '8 july 1933'], ['st kilda', '11.14 ( 80 )', 'geelong', '7.13 ( 55 )', 'junction oval', '10000', '8 july 1933'], ['footscray', '15.6 ( 96 )', 'fitzroy', '8.14 ( 62 )', 'western oval', '18000', '8 july 1933']] |
list of colombian submissions for the academy award for best foreign language film | https://en.wikipedia.org/wiki/List_of_Colombian_submissions_for_the_Academy_Award_for_Best_Foreign_Language_Film | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22102732-1.html.csv | ordinal | of the colombian submissions for the academy award for best foreign language film , the one that was submitted in the 3rd to last year was the wind journeys . | {'row': '15', 'col': '1', 'order': '3', 'col_other': '2', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'year ( ceremony )', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; year ( ceremony ) ; 3 }'}, 'english title'], 'result': 'the wind journeys', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; year ( ceremony ) ; 3 } ; english title }'}, 'the wind journeys'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; year ( ceremony ) ; 3 } ; english title } ; the wind journeys } = true', 'tointer': 'select the row whose year ( ceremony ) record of all rows is 3rd maximum . the english title record of this row is the wind journeys .'} | eq { hop { nth_argmax { all_rows ; year ( ceremony ) ; 3 } ; english title } ; the wind journeys } = true | select the row whose year ( ceremony ) record of all rows is 3rd maximum . the english title record of this row is the wind journeys . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'year (ceremony)_5': 5, '3_6': 6, 'english title_7': 7, 'the wind journeys_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'year (ceremony)_5': 'year ( ceremony )', '3_6': '3', 'english title_7': 'english title', 'the wind journeys_8': 'the wind journeys'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'year (ceremony)_5': [0], '3_6': [0], 'english title_7': [1], 'the wind journeys_8': [2]} | ['year ( ceremony )', 'english title', 'spanish title', 'director', 'result'] | [['1980 ( 53rd )', 'the latin immigrant', 'el inmigrante latino', 'gustavo nieto roa', 'not nominated'], ['1984 ( 57th )', 'a man of principle', 'cóndores no entierran todos los días', 'francisco norden', 'not nominated'], ['1986 ( 59th )', 'a time to die', 'tiempo de morir', 'jorge alí triana', 'not nominated'], ['1991 ( 64th )', 'confessing to laura', 'confesión a laura', 'jaime osorio gómez', 'not nominated'], ['1994 ( 67th )', 'the strategy of the snail', 'la estrategia del caracol', 'sergio cabrera', 'not nominated'], ['1996 ( 69th )', 'oedipus mayor', 'edipo alcalde', 'jorge alí triana', 'not nominated'], ['1997 ( 70th )', 'the debt', 'la deuda', 'manuel jose alvarez & nicolas buenaventura', 'not nominated'], ['1998 ( 71st )', 'the rose seller', 'la vendedora de rosas', 'victor gaviria', 'not nominated'], ['1999 ( 72nd )', 'time out', 'golpe de estadio', 'sergio cabrera', 'not nominated'], ['2001 ( 74th )', 'our lady of the assassins', 'la virgen de los sicarios', 'barbet schroeder', 'not nominated'], ['2005 ( 78th )', 'wandering shadows', 'la sombra del caminante', 'ciro guerra', 'not nominated'], ['2006 ( 79th )', 'a ton of luck', 'soñar no cuesta nada', 'rodrigo triana', 'not nominated'], ['2007 ( 80th )', 'satanás', 'satanás', 'andi baiz', 'not nominated'], ['2008 ( 81st )', 'dog eat dog', 'perro come perro', 'carlos moreno', 'not nominated'], ['2009 ( 82nd )', 'the wind journeys', 'los viajes del viento', 'ciro guerra', 'not nominated'], ['2010 ( 83rd )', 'crab trap', 'el vuelco del cangrejo', 'oscar ruiz navia', 'not nominated'], ['2012 ( 85th )', 'the snitch cartel', 'el cartel de los sapos', 'carlos moreno', 'not nominated']] |
formula student | https://en.wikipedia.org/wiki/Formula_Student | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1936678-1.html.csv | comparative | leeds university was named best uk team in a formula student competition before oxford brookes university was . | {'row_1': '4', 'row_2': '6', 'col': '1', 'col_other': '4', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'best uk team', 'leeds university'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose best uk team record fuzzily matches to leeds university .', 'tostr': 'filter_eq { all_rows ; best uk team ; leeds university }'}, 'year'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; best uk team ; leeds university } ; year }', 'tointer': 'select the rows whose best uk team record fuzzily matches to leeds university . take the year record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'best uk team', 'oxford brookes university'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose best uk team record fuzzily matches to oxford brookes university .', 'tostr': 'filter_eq { all_rows ; best uk team ; oxford brookes university }'}, 'year'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; best uk team ; oxford brookes university } ; year }', 'tointer': 'select the rows whose best uk team record fuzzily matches to oxford brookes university . take the year record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; best uk team ; leeds university } ; year } ; hop { filter_eq { all_rows ; best uk team ; oxford brookes university } ; year } } = true', 'tointer': 'select the rows whose best uk team record fuzzily matches to leeds university . take the year record of this row . select the rows whose best uk team record fuzzily matches to oxford brookes university . take the year record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; best uk team ; leeds university } ; year } ; hop { filter_eq { all_rows ; best uk team ; oxford brookes university } ; year } } = true | select the rows whose best uk team record fuzzily matches to leeds university . take the year record of this row . select the rows whose best uk team record fuzzily matches to oxford brookes university . take the year record of this row . the first record is less than the second record . | 5 | 5 | {'less_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'best uk team_7': 7, 'leeds university_8': 8, 'year_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'best uk team_11': 11, 'oxford brookes university_12': 12, 'year_13': 13} | {'less_4': 'less', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'best uk team_7': 'best uk team', 'leeds university_8': 'leeds university', 'year_9': 'year', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'best uk team_11': 'best uk team', 'oxford brookes university_12': 'oxford brookes university', 'year_13': 'year'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'best uk team_7': [0], 'leeds university_8': [0], 'year_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'best uk team_11': [1], 'oxford brookes university_12': [1], 'year_13': [3]} | ['year', 'location', 'class 1', 'best uk team', 'class 1 - 200', 'class 3', 'class 1a'] | [['1998', 'mira', 'university of texas at arlington', 'university of birmingham', 'n / a', 'n / a', 'n / a'], ['1999', 'nec birmingham', 'rochester institute of technology', 'leeds university', 'n / a', 'university of maribor', 'n / a'], ['2000', 'nec birmingham', 'csu pomona', 'university of hertfordshire', 'n / a', 'university of huddersfield', 'n / a'], ['2001', 'nec birmingham', 'georgia institute of technology', 'leeds university', 'university of birmingham', 'university of bath', 'n / a'], ['2002', 'bruntingthorpe', 'georgia institute of technology', 'brunel university', 'university of hertfordshire', 'university of florence', 'n / a'], ['2003', 'bruntingthorpe', 'university of toronto', 'oxford brookes university', 'helsinki polytechnic stadia', 'swansea university', 'n / a'], ['2004', 'bruntingthorpe', 'rmit university', 'oxford brookes university', 'uas stralsund', 'university of bath', 'n / a'], ['2005', 'bruntingthorpe', 'university of toronto', 'university of hertfordshire', 'swansea university', 'university of bath', 'n / a'], ['2006', 'bruntingthorpe', 'university of toronto', 'oxford brookes university', 'university of hertfordshire', 'instituto superior técnico', 'n / a'], ['2007', 'silverstone', 'rmit university', 'university of bath', 'university of hertfordshire', 'university of hertfordshire', 'n / a'], ['2008', 'silverstone', 'university of stuttgart', 'university of bath', 'university of hertfordshire', 'eindhoven university of technology', 'university of hertfordshire'], ['2009', 'silverstone', 'university of stuttgart', 'university of bath', 'tu munich', 'isfahan university of technology', 'university of hertfordshire'], ['2010', 'silverstone', 'tu munich', 'university of hertfordshire', 'n / a', 'instituto superior técnico', 'eth zurich'], ['year', 'location', 'class 1', 'best uk team', 'class 1a', 'class 2', 'class 2a'], ['2011', 'silverstone', 'university of stuttgart', 'university of hertfordshire', 'delft university of technology', 'university of bath', 'university of warwick'], ['year', 'location', 'class 1', 'best uk team', 'dynamic events winner', 'static events winner', 'class 2'], ['2012', 'silverstone', 'chalmers university of technology', 'oxford brookes university', 'tu munich', 'monash university', 'instituto superior técnico']] |
november nine | https://en.wikipedia.org/wiki/November_Nine | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23696862-6.html.csv | majority | the majority of poker players in the november nine had 0 wsop bracelets . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': '0', 'subset': None} | {'func': 'most_eq', 'args': ['all_rows', 'wsop bracelets', '0'], 'result': True, 'ind': 0, 'tointer': 'for the wsop bracelets records of all rows , most of them are equal to 0 .', 'tostr': 'most_eq { all_rows ; wsop bracelets ; 0 } = true'} | most_eq { all_rows ; wsop bracelets ; 0 } = true | for the wsop bracelets records of all rows , most of them are equal to 0 . | 1 | 1 | {'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'wsop bracelets_3': 3, '0_4': 4} | {'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'wsop bracelets_3': 'wsop bracelets', '0_4': '0'} | {'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'wsop bracelets_3': [0], '0_4': [0]} | ['name', 'starting chip count', 'wsop bracelets', 'wsop cashes', 'wsop earnings', 'final place', 'prize'] | [['jesse sylvia', '43875000', '0', '2', '36372', '2nd', '5295149'], ['andras koroknai', '29375000', '0', '2', '39371', '6th', '1640461'], ['greg merson', '28725000', '1', '5', '1253501', '1st', '8531853'], ['russell thomas', '24800000', '0', '3', '126796', '4th', '2850494'], ['steven gee', '16860000', '1', '4', '480822', '9th', '754798'], ['michael esposito', '16260000', '0', '3', '27311', '7th', '1257790'], ['robert salaburu', '15155000', '0', '0', '0', '8th', '971252'], ['jacob balsiger', '13115000', '0', '1', '3531', '3rd', '3797558']] |
novovoronezh nuclear power plant | https://en.wikipedia.org/wiki/Novovoronezh_Nuclear_Power_Plant | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12805568-1.html.csv | comparative | the novovoronezh - 2 unit was shut down later than the novovoronezh - 1 unit was . | {'row_1': '2', 'row_2': '1', 'col': '8', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'unit', 'novovoronezh - 2'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose unit record fuzzily matches to novovoronezh - 2 .', 'tostr': 'filter_eq { all_rows ; unit ; novovoronezh - 2 }'}, 'shutdown'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; unit ; novovoronezh - 2 } ; shutdown }', 'tointer': 'select the rows whose unit record fuzzily matches to novovoronezh - 2 . take the shutdown record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'unit', 'novovoronezh - 1'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose unit record fuzzily matches to novovoronezh - 1 .', 'tostr': 'filter_eq { all_rows ; unit ; novovoronezh - 1 }'}, 'shutdown'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; unit ; novovoronezh - 1 } ; shutdown }', 'tointer': 'select the rows whose unit record fuzzily matches to novovoronezh - 1 . take the shutdown record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; unit ; novovoronezh - 2 } ; shutdown } ; hop { filter_eq { all_rows ; unit ; novovoronezh - 1 } ; shutdown } } = true', 'tointer': 'select the rows whose unit record fuzzily matches to novovoronezh - 2 . take the shutdown record of this row . select the rows whose unit record fuzzily matches to novovoronezh - 1 . take the shutdown record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; unit ; novovoronezh - 2 } ; shutdown } ; hop { filter_eq { all_rows ; unit ; novovoronezh - 1 } ; shutdown } } = true | select the rows whose unit record fuzzily matches to novovoronezh - 2 . take the shutdown record of this row . select the rows whose unit record fuzzily matches to novovoronezh - 1 . take the shutdown record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'str_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'unit_7': 7, 'novovoronezh - 2_8': 8, 'shutdown_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'unit_11': 11, 'novovoronezh - 1_12': 12, 'shutdown_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'str_hop_2': 'str_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'unit_7': 'unit', 'novovoronezh - 2_8': 'novovoronezh - 2', 'shutdown_9': 'shutdown', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'unit_11': 'unit', 'novovoronezh - 1_12': 'novovoronezh - 1', 'shutdown_13': 'shutdown'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'unit_7': [0], 'novovoronezh - 2_8': [0], 'shutdown_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'unit_11': [1], 'novovoronezh - 1_12': [1], 'shutdown_13': [3]} | ['unit', 'reactortype', 'net capacity', 'gross capacity', 'construction started', 'electricity grid', 'commercial operation', 'shutdown'] | [['novovoronezh - 1', 'vver - 210 ( prototype )', '197 mw', '210 mw', '01.07.1957', '30.09.1964', '31.12.1964', '16.02.1988'], ['novovoronezh - 2', 'vver - 365 ( prototype )', '336 mw', '365 mw', '01.06.1964', '27.12.1969', '14.04.1970', '29.08.1990'], ['novovoronezh - 3', 'vver - 440 / 179', '385 mw', '417 mw', '01.07.1967', '27.12.1971', '29.06.1972', '2016 planned'], ['novovoronezh - 4', 'vver - 440 / 179', '385 mw', '417 mw', '01.07.1967', '28.12.1972', '24.03.1973', '2017 planned'], ['novovoronezh - 5', 'vver - 1000 / 187 ( prototype )', '950 mw', '1000 mw', '01.03.1974', '31.05.1980', '20.02.1981', '2035 planned']] |
mattia pasini | https://en.wikipedia.org/wiki/Mattia_Pasini | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13985563-1.html.csv | comparative | in mattia pasini 's career , he competed in more races in 2005 than in 2012 . | {'row_1': '2', 'row_2': '9', 'col': '2', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'season', '2005'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose season record fuzzily matches to 2005 .', 'tostr': 'filter_eq { all_rows ; season ; 2005 }'}, 'races'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; season ; 2005 } ; races }', 'tointer': 'select the rows whose season record fuzzily matches to 2005 . take the races record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'season', '2012'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose season record fuzzily matches to 2012 .', 'tostr': 'filter_eq { all_rows ; season ; 2012 }'}, 'races'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; season ; 2012 } ; races }', 'tointer': 'select the rows whose season record fuzzily matches to 2012 . take the races record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; season ; 2005 } ; races } ; hop { filter_eq { all_rows ; season ; 2012 } ; races } } = true', 'tointer': 'select the rows whose season record fuzzily matches to 2005 . take the races record of this row . select the rows whose season record fuzzily matches to 2012 . take the races record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; season ; 2005 } ; races } ; hop { filter_eq { all_rows ; season ; 2012 } ; races } } = true | select the rows whose season record fuzzily matches to 2005 . take the races record of this row . select the rows whose season record fuzzily matches to 2012 . take the races record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'season_7': 7, '2005_8': 8, 'races_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'season_11': 11, '2012_12': 12, 'races_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'season_7': 'season', '2005_8': '2005', 'races_9': 'races', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'season_11': 'season', '2012_12': '2012', 'races_13': 'races'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'season_7': [0], '2005_8': [0], 'races_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'season_11': [1], '2012_12': [1], 'races_13': [3]} | ['season', 'races', 'podiums', 'pole', 'flap'] | [['2004', '16', '0', '0', '0'], ['2005', '15', '6', '0', '0'], ['2006', '16', '6', '2', '2'], ['2007', '17', '5', '9', '2'], ['2008', '16', '4', '0', '0'], ['2009', '16', '5', '0', '0'], ['2010', '8', '0', '0', '0'], ['2011', '17', '0', '0', '0'], ['2012', '14', '0', '0', '0'], ['2012', '1', '0', '0', '0'], ['2013', '16', '0', '0', '0'], ['total', '152', '26', '11', '4']] |
jim rathmann | https://en.wikipedia.org/wiki/Jim_Rathmann | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1252109-1.html.csv | count | for jim rathmann , when the year was before 1960 , there were 5 occasions where it was 200 laps . | {'scope': 'subset', 'criterion': 'equal', 'value': '200', 'result': '5', 'col': '6', 'subset': {'col': '1', 'criterion': 'less_than', 'value': '1960'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'year', '1960'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; year ; 1960 }', 'tointer': 'select the rows whose year record is less than 1960 .'}, 'laps', '200'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record is less than 1960 . among these rows , select the rows whose laps record is equal to 200 .', 'tostr': 'filter_eq { filter_less { all_rows ; year ; 1960 } ; laps ; 200 }'}], 'result': '5', 'ind': 2, 'tostr': 'count { filter_eq { filter_less { all_rows ; year ; 1960 } ; laps ; 200 } }', 'tointer': 'select the rows whose year record is less than 1960 . among these rows , select the rows whose laps record is equal to 200 . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_less { all_rows ; year ; 1960 } ; laps ; 200 } } ; 5 } = true', 'tointer': 'select the rows whose year record is less than 1960 . among these rows , select the rows whose laps record is equal to 200 . the number of such rows is 5 .'} | eq { count { filter_eq { filter_less { all_rows ; year ; 1960 } ; laps ; 200 } } ; 5 } = true | select the rows whose year record is less than 1960 . among these rows , select the rows whose laps record is equal to 200 . the number of such rows is 5 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_eq_1': 1, 'filter_less_0': 0, 'all_rows_5': 5, 'year_6': 6, '1960_7': 7, 'laps_8': 8, '200_9': 9, '5_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_eq_1': 'filter_eq', 'filter_less_0': 'filter_less', 'all_rows_5': 'all_rows', 'year_6': 'year', '1960_7': '1960', 'laps_8': 'laps', '200_9': '200', '5_10': '5'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_eq_1': [2], 'filter_less_0': [1], 'all_rows_5': [0], 'year_6': [0], '1960_7': [0], 'laps_8': [1], '200_9': [1], '5_10': [3]} | ['year', 'start', 'qual', 'rank', 'finish', 'laps'] | [['1949', '21', '126.516', '29', '11', '175'], ['1950', '28', '129.959', '24', '24', '122'], ['1952', '10', '136.343', '7', '2', '200'], ['1953', '25', '135.666', '28', '7', '200'], ['1954', '28', '138.228', '21', '28', '110'], ['1955', '20', '138.707', '24', '14', '191'], ['1956', '2', '145.120', '3', '20', '175'], ['1957', '32', '139.806', '31', '2', '200'], ['1958', '20', '143.147', '15', '5', '200'], ['1959', '3', '144.433', '4', '2', '200'], ['1960', '2', '146.371', '4', '1', '200'], ['1961', '11', '145.413', '13', '30', '48'], ['1962', '23', '146.610', '21', '9', '200'], ['1963', '29', '147.838', '32', '24', '99']] |
2008 - 09 minnesota timberwolves season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Minnesota_Timberwolves_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17058226-10.html.csv | majority | kevin love had the majority of rebounds during this stretch of the minnesota season . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'kevin love', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'high rebounds', 'kevin love'], 'result': True, 'ind': 0, 'tointer': 'for the high rebounds records of all rows , most of them fuzzily match to kevin love .', 'tostr': 'most_eq { all_rows ; high rebounds ; kevin love } = true'} | most_eq { all_rows ; high rebounds ; kevin love } = true | for the high rebounds records of all rows , most of them fuzzily match to kevin love . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'high rebounds_3': 3, 'kevin love_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'high rebounds_3': 'high rebounds', 'kevin love_4': 'kevin love'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'high rebounds_3': [0], 'kevin love_4': [0]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['76', 'april 3', 'utah', 'w 103 - 102 ( ot )', 'ryan gomes , rodney carney ( 25 )', 'mike miller ( 9 )', 'mike miller ( 8 )', 'energysolutions arena 19911', '22 - 54'], ['77', 'april 5', 'denver', 'l 87 - 110 ( ot )', 'sebastian telfair ( 18 )', 'shelden williams ( 12 )', 'mike miller ( 6 )', 'target center 16839', '22 - 55'], ['78', 'april 7', 'la clippers', 'w 87 - 77 ( ot )', 'ryan gomes ( 24 )', 'kevin love ( 15 )', 'sebastian telfair , mike miller ( 6 )', 'staples center 16757', '23 - 55'], ['79', 'april 8', 'golden state', 'w 105 - 97 ( ot )', 'sebastian telfair ( 21 )', 'kevin love ( 12 )', 'mike miller ( 6 )', 'oracle arena 18808', '24 - 55'], ['80', 'april 11', 'phoenix', 'l 97 - 110 ( ot )', 'sebastian telfair ( 21 )', 'mike miller ( 9 )', 'mike miller ( 9 )', 'target center 18478', '24 - 56'], ['81', 'april 13', 'dallas', 'l 94 - 96 ( ot )', 'craig smith ( 24 )', 'kevin love ( 11 )', 'sebastian telfair ( 12 )', 'american airlines center 19900', '24 - 57']] |
operación triunfo ( spain ) | https://en.wikipedia.org/wiki/Operaci%C3%B3n_Triunfo_%28Spain%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1149495-1.html.csv | superlative | the earliest year for operación triunfo in spain was the year 2001 . | {'scope': 'all', 'col_superlative': '2', 'row_superlative': '1', 'value_mentioned': 'yes', 'max_or_min': 'min', 'other_col': 'n/a', 'subset': None} | {'func': 'eq', 'args': [{'func': 'min', 'args': ['all_rows', 'year'], 'result': '2001 - 2002', 'ind': 0, 'tostr': 'min { all_rows ; year }', 'tointer': 'the minimum year record of all rows is 2001 - 2002 .'}, '2001 - 2002'], 'result': True, 'ind': 1, 'tostr': 'eq { min { all_rows ; year } ; 2001 - 2002 } = true', 'tointer': 'the minimum year record of all rows is 2001 - 2002 .'} | eq { min { all_rows ; year } ; 2001 - 2002 } = true | the minimum year record of all rows is 2001 - 2002 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'min_0': 0, 'all_rows_3': 3, 'year_4': 4, '2001 - 2002_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'min_0': 'min', 'all_rows_3': 'all_rows', 'year_4': 'year', '2001 - 2002_5': '2001 - 2002'} | {'eq_1': [2], 'result_2': [], 'min_0': [1], 'all_rows_3': [0], 'year_4': [0], '2001 - 2002_5': [1]} | ['series', 'year', 'winner', 'runner - up', 'third place', 'fourth place', 'fifth place', 'sixth place', 'host'] | [['1', '2001 - 2002', 'rosa lópez', 'david bisbal', 'david bustamante', 'chenoa', 'manu tenorio', 'verónica romero', 'carlos lozano'], ['2', '2002 - 2003', 'ainhoa cantalapiedra', 'manuel carrasco', 'beth rodergas', 'miguel nández', 'hugo salazar', 'joan tena', 'carlos lozano'], ['3', '2003', 'vicente seguí porres', 'ramón del castillo', 'miguel cadenas', 'davinia cuevas', 'mario martínez', 'leticia pérez', 'carlos lozano'], ['4', '2005', 'sergio rivero', 'soraya arnelas', 'víctor estévez polo', 'idaira fernandez', 'fran dieli', 'edurne garcía', 'jesús vázquez'], ['5', '2006 - 2007', 'lorena gómez', 'daniel zueras', 'leo segarra sánchez', 'saray ramírez', 'moritz weisskopf', 'jose galisteo', 'jesús vázquez'], ['6', '2008', 'virginia maestro', 'pablo lópez', 'chipper cooke', 'manu castellano', 'sandra criado', 'mimi segura', 'jesús vázquez'], ['7', '2009', 'mario álvarez', 'brenda mau', 'jon allende', 'ángel capel', 'patricia navarro', 'sylvia parejo', 'jesús vázquez'], ['8', '2011', 'nahuel sachak', 'álex forriols', 'mario jefferson', 'alexandra masangkay', 'niccó', 'josh prada', 'pilar rubio']] |
1936 vfl season | https://en.wikipedia.org/wiki/1936_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10790804-4.html.csv | count | there were 6 game venues used during the 1936 vfl season . | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '6', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'venue'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record is arbitrary .', 'tostr': 'filter_all { all_rows ; venue }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; venue } }', 'tointer': 'select the rows whose venue record is arbitrary . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; venue } } ; 6 } = true', 'tointer': 'select the rows whose venue record is arbitrary . the number of such rows is 6 .'} | eq { count { filter_all { all_rows ; venue } } ; 6 } = true | select the rows whose venue record is arbitrary . the number of such rows is 6 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'venue_5': 5, '6_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'venue_5': 'venue', '6_6': '6'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'venue_5': [0], '6_6': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['north melbourne', '7.18 ( 60 )', 'south melbourne', '18.21 ( 129 )', 'arden street oval', '14000', '23 may 1936'], ['collingwood', '20.17 ( 137 )', 'essendon', '13.10 ( 88 )', 'victoria park', '11000', '23 may 1936'], ['carlton', '16.19 ( 115 )', 'richmond', '18.17 ( 125 )', 'princes park', '43000', '23 may 1936'], ['melbourne', '16.20 ( 116 )', 'hawthorn', '11.11 ( 77 )', 'mcg', '10152', '23 may 1936'], ['st kilda', '19.11 ( 125 )', 'geelong', '12.10 ( 82 )', 'junction oval', '22500', '23 may 1936'], ['footscray', '13.11 ( 89 )', 'fitzroy', '10.14 ( 74 )', 'western oval', '18000', '23 may 1936']] |
1979 vfl season | https://en.wikipedia.org/wiki/1979_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10823719-17.html.csv | count | when in there were more than 20000 people in the crowd , in round 17 of the 1979 vfl season , the home team scored more than 10 points twice . | {'scope': 'subset', 'criterion': 'greater_than', 'value': '10', 'result': '2', 'col': '2', 'subset': {'col': '6', 'criterion': 'greater_than', 'value': '20000'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'crowd', '20000'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; crowd ; 20000 }', 'tointer': 'select the rows whose crowd record is greater than 20000 .'}, 'home team score', '10'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose crowd record is greater than 20000 . among these rows , select the rows whose home team score record is greater than 10 .', 'tostr': 'filter_greater { filter_greater { all_rows ; crowd ; 20000 } ; home team score ; 10 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_greater { filter_greater { all_rows ; crowd ; 20000 } ; home team score ; 10 } }', 'tointer': 'select the rows whose crowd record is greater than 20000 . among these rows , select the rows whose home team score record is greater than 10 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_greater { filter_greater { all_rows ; crowd ; 20000 } ; home team score ; 10 } } ; 2 } = true', 'tointer': 'select the rows whose crowd record is greater than 20000 . among these rows , select the rows whose home team score record is greater than 10 . the number of such rows is 2 .'} | eq { count { filter_greater { filter_greater { all_rows ; crowd ; 20000 } ; home team score ; 10 } } ; 2 } = true | select the rows whose crowd record is greater than 20000 . among these rows , select the rows whose home team score record is greater than 10 . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_greater_1': 1, 'filter_greater_0': 0, 'all_rows_5': 5, 'crowd_6': 6, '20000_7': 7, 'home team score_8': 8, '10_9': 9, '2_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_greater_1': 'filter_greater', 'filter_greater_0': 'filter_greater', 'all_rows_5': 'all_rows', 'crowd_6': 'crowd', '20000_7': '20000', 'home team score_8': 'home team score', '10_9': '10', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_greater_1': [2], 'filter_greater_0': [1], 'all_rows_5': [0], 'crowd_6': [0], '20000_7': [0], 'home team score_8': [1], '10_9': [1], '2_10': [3]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['st kilda', '19.14 ( 128 )', 'south melbourne', '16.12 ( 108 )', 'moorabbin oval', '12969', '28 july 1979'], ['footscray', '12.10 ( 82 )', 'geelong', '18.14 ( 122 )', 'western oval', '14284', '28 july 1979'], ['carlton', '15.21 ( 111 )', 'hawthorn', '12.11 ( 83 )', 'princes park', '22159', '28 july 1979'], ['richmond', '16.15 ( 111 )', 'north melbourne', '19.10 ( 124 )', 'mcg', '38111', '28 july 1979'], ['essendon', '9.17 ( 71 )', 'collingwood', '14.8 ( 92 )', 'windy hill', '31968', '28 july 1979'], ['fitzroy', '36.22 ( 238 )', 'melbourne', '6.12 ( 48 )', 'vfl park', '12149', '28 july 1979']] |
the apprentice new zealand | https://en.wikipedia.org/wiki/The_Apprentice_New_Zealand | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26263322-1.html.csv | unique | of the candidates in the apprentice new zealand , linda slade is the only one who was 21 years old . | {'scope': 'all', 'row': '5', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': '21', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'age', '21'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose age record is equal to 21 .', 'tostr': 'filter_eq { all_rows ; age ; 21 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; age ; 21 } }', 'tointer': 'select the rows whose age record is equal to 21 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'age', '21'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose age record is equal to 21 .', 'tostr': 'filter_eq { all_rows ; age ; 21 }'}, 'candidate'], 'result': 'linda slade', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; age ; 21 } ; candidate }'}, 'linda slade'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; age ; 21 } ; candidate } ; linda slade }', 'tointer': 'the candidate record of this unqiue row is linda slade .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; age ; 21 } } ; eq { hop { filter_eq { all_rows ; age ; 21 } ; candidate } ; linda slade } } = true', 'tointer': 'select the rows whose age record is equal to 21 . there is only one such row in the table . the candidate record of this unqiue row is linda slade .'} | and { only { filter_eq { all_rows ; age ; 21 } } ; eq { hop { filter_eq { all_rows ; age ; 21 } ; candidate } ; linda slade } } = true | select the rows whose age record is equal to 21 . there is only one such row in the table . the candidate record of this unqiue row is linda slade . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'age_7': 7, '21_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'candidate_9': 9, 'linda slade_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'age_7': 'age', '21_8': '21', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'candidate_9': 'candidate', 'linda slade_10': 'linda slade'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'age_7': [0], '21_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'candidate_9': [2], 'linda slade_10': [3]} | ['candidate', 'background', 'original team', 'age', 'hometown', 'result'] | [['thomas ben', 'divisional manager', 'number 8', '34', 'auckland', 'hired by serepisos'], ['david wyatt', 'self - employed - media agency', 'number 8', '27', 'auckland', 'fired in the season finale'], ['catherine livingstone', 'self - employed - concierge service', 'athena', '33', 'auckland', 'fired in week 12'], ['karen reid', 'self - employed - practices in alternative medicine', 'athena', '33', 'auckland', 'fired in week 11'], ['linda slade', 'university student', 'athena', '21', 'christchurch', 'fired in week 10'], ['nicky clarke', 'pr specialist', 'athena', '28', 'auckland', 'fired in week 9'], ['daniel phillips', 'advertising account manager', 'number 8', '31', 'auckland', 'fired in week 8'], ['meena chhagan', 'accountant', 'athena', '24', 'wellington', 'fired in week 7'], ['richard henry', 'getfrank founder', 'number 8', '26', 'auckland', 'fired in week 7'], ['paul natac', 'infringement relationship manager', 'number 8', '28', 'auckland', 'fired in week 6'], ['chris whiteside', 'accountant', 'number 8', '28', 'christchurch', 'fired in week 4'], ['kirsty parkhill', 'business development manager', 'athena', '35', 'wellington', 'fired in week 3']] |
1982 vfl season | https://en.wikipedia.org/wiki/1982_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10824095-7.html.csv | count | in the 1982 vfl season , among the games where away team scored less than 20.00 , 3 of them had an attendance of more than 20,000 . | {'scope': 'subset', 'criterion': 'greater_than', 'value': '20000', 'result': '3', 'col': '6', 'subset': {'col': '4', 'criterion': 'less_than', 'value': '20'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'away team score', '20'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; away team score ; 20 }', 'tointer': 'select the rows whose away team score record is less than 20 .'}, 'crowd', '20000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose away team score record is less than 20 . among these rows , select the rows whose crowd record is greater than 20000 .', 'tostr': 'filter_greater { filter_less { all_rows ; away team score ; 20 } ; crowd ; 20000 }'}], 'result': '3', 'ind': 2, 'tostr': 'count { filter_greater { filter_less { all_rows ; away team score ; 20 } ; crowd ; 20000 } }', 'tointer': 'select the rows whose away team score record is less than 20 . among these rows , select the rows whose crowd record is greater than 20000 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_greater { filter_less { all_rows ; away team score ; 20 } ; crowd ; 20000 } } ; 3 } = true', 'tointer': 'select the rows whose away team score record is less than 20 . among these rows , select the rows whose crowd record is greater than 20000 . the number of such rows is 3 .'} | eq { count { filter_greater { filter_less { all_rows ; away team score ; 20 } ; crowd ; 20000 } } ; 3 } = true | select the rows whose away team score record is less than 20 . among these rows , select the rows whose crowd record is greater than 20000 . the number of such rows is 3 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_greater_1': 1, 'filter_less_0': 0, 'all_rows_5': 5, 'away team score_6': 6, '20_7': 7, 'crowd_8': 8, '20000_9': 9, '3_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_greater_1': 'filter_greater', 'filter_less_0': 'filter_less', 'all_rows_5': 'all_rows', 'away team score_6': 'away team score', '20_7': '20', 'crowd_8': 'crowd', '20000_9': '20000', '3_10': '3'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_greater_1': [2], 'filter_less_0': [1], 'all_rows_5': [0], 'away team score_6': [0], '20_7': [0], 'crowd_8': [1], '20000_9': [1], '3_10': [3]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['melbourne', '22.11 ( 143 )', 'north melbourne', '28.12 ( 180 )', 'mcg', '25704', '8 may 1982'], ['footscray', '15.20 ( 110 )', 'swans', '20.11 ( 131 )', 'western oval', '11487', '8 may 1982'], ['fitzroy', '19.15 ( 129 )', 'hawthorn', '17.15 ( 117 )', 'junction oval', '14675', '8 may 1982'], ['carlton', '15.20 ( 110 )', 'geelong', '7.7 ( 49 )', 'princes park', '28736', '8 may 1982'], ['essendon', '17.20 ( 122 )', 'collingwood', '9.13 ( 67 )', 'windy hill', '25510', '8 may 1982'], ['richmond', '21.14 ( 140 )', 'st kilda', '17.12 ( 114 )', 'vfl park', '33222', '8 may 1982']] |
ireland in the eurovision song contest 1989 | https://en.wikipedia.org/wiki/Ireland_in_the_Eurovision_Song_Contest_1989 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16956150-1.html.csv | ordinal | for irish singers in the 1989 eurovision song contest , the performer with the 2nd highest number of points is honor heffernan . | {'row': '2', 'col': '4', 'order': '2', 'col_other': '3', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'points', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; points ; 2 }'}, 'performer'], 'result': 'honor heffernan', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; points ; 2 } ; performer }'}, 'honor heffernan'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; points ; 2 } ; performer } ; honor heffernan } = true', 'tointer': 'select the row whose points record of all rows is 2nd maximum . the performer record of this row is honor heffernan .'} | eq { hop { nth_argmax { all_rows ; points ; 2 } ; performer } ; honor heffernan } = true | select the row whose points record of all rows is 2nd maximum . the performer record of this row is honor heffernan . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'points_5': 5, '2_6': 6, 'performer_7': 7, 'honor heffernan_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'points_5': 'points', '2_6': '2', 'performer_7': 'performer', 'honor heffernan_8': 'honor heffernan'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'points_5': [0], '2_6': [0], 'performer_7': [1], 'honor heffernan_8': [2]} | ['draw', 'song', 'performer', 'points', 'rank'] | [['1', 'the real me', 'kiev connolly & the missing passengers', '104', '1st'], ['2', 'easy', 'honor heffernan', '97', '2nd'], ['3', "this is n't war ( it 's revolution )", 'nicola kerr', '79', '3rd'], ['4', 'uaigneach', 'barry ronan', '48', '8th'], ['5', 'here we go', 'linda martin', '71', '6th'], ['6', 'angel eyes', 'jenny newman', '77', '5th'], ['7', 'song for you', 'dave lalor', '68', '7th'], ['8', 'it was meant to be', 'noelle', '79', '3rd']] |
carel godin de beaufort | https://en.wikipedia.org/wiki/Carel_Godin_de_Beaufort | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1219705-1.html.csv | count | there were only two years in which carel godin de beaufort scored points . | {'scope': 'all', 'criterion': 'greater_than', 'value': '0', 'result': '2', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'points', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose points record is greater than 0 .', 'tostr': 'filter_greater { all_rows ; points ; 0 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_greater { all_rows ; points ; 0 } }', 'tointer': 'select the rows whose points record is greater than 0 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_greater { all_rows ; points ; 0 } } ; 2 } = true', 'tointer': 'select the rows whose points record is greater than 0 . the number of such rows is 2 .'} | eq { count { filter_greater { all_rows ; points ; 0 } } ; 2 } = true | select the rows whose points record is greater than 0 . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_greater_0': 0, 'all_rows_4': 4, 'points_5': 5, '0_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_greater_0': 'filter_greater', 'all_rows_4': 'all_rows', 'points_5': 'points', '0_6': '0', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_greater_0': [1], 'all_rows_4': [0], 'points_5': [0], '0_6': [0], '2_7': [2]} | ['year', 'entrant', 'chassis', 'engine', 'points'] | [['1957', 'ecurie maarsbergen', 'porsche 550rs', 'porsche flat - 4', '0'], ['1958', 'ecurie maarsbergen', 'porsche rsk', 'porsche flat - 4', '0'], ['1959', 'ecurie maarsbergen', 'porsche rsk', 'porsche flat - 4', '0'], ['1959', 'scuderia ugolini', 'maserati 250f', 'maserati straight - 6', '0'], ['1960', 'ecurie maarsbergen', 'cooper t51', 'climax straight - 4', '0'], ['1961', 'ecurie maarsbergen', 'porsche 718', 'porsche flat - 4', '0'], ['1962', 'ecurie maarsbergen', 'porsche 718', 'porsche flat - 4', '2'], ['1963', 'ecurie maarsbergen', 'porsche 718', 'porsche flat - 4', '2'], ['1964', 'ecurie maarsbergen', 'porsche 718', 'porsche flat - 4', '0']] |
dorval | https://en.wikipedia.org/wiki/Dorval | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-189893-1.html.csv | comparative | in 2006 in dorval , more people had romanian as their first language than those that had polish . | {'row_1': '6', 'row_2': '8', 'col': '2', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'mother tongue', 'romanian'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose mother tongue record fuzzily matches to romanian .', 'tostr': 'filter_eq { all_rows ; mother tongue ; romanian }'}, 'population ( 2006 )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; mother tongue ; romanian } ; population ( 2006 ) }', 'tointer': 'select the rows whose mother tongue record fuzzily matches to romanian . take the population ( 2006 ) record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'mother tongue', 'polish'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose mother tongue record fuzzily matches to polish .', 'tostr': 'filter_eq { all_rows ; mother tongue ; polish }'}, 'population ( 2006 )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; mother tongue ; polish } ; population ( 2006 ) }', 'tointer': 'select the rows whose mother tongue record fuzzily matches to polish . take the population ( 2006 ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; mother tongue ; romanian } ; population ( 2006 ) } ; hop { filter_eq { all_rows ; mother tongue ; polish } ; population ( 2006 ) } } = true', 'tointer': 'select the rows whose mother tongue record fuzzily matches to romanian . take the population ( 2006 ) record of this row . select the rows whose mother tongue record fuzzily matches to polish . take the population ( 2006 ) record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; mother tongue ; romanian } ; population ( 2006 ) } ; hop { filter_eq { all_rows ; mother tongue ; polish } ; population ( 2006 ) } } = true | select the rows whose mother tongue record fuzzily matches to romanian . take the population ( 2006 ) record of this row . select the rows whose mother tongue record fuzzily matches to polish . take the population ( 2006 ) record of this row . the first record is greater than the second record . | 5 | 5 | {'greater_4': 4, 'result_5': 5, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'mother tongue_7': 7, 'romanian_8': 8, 'population (2006)_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'mother tongue_11': 11, 'polish_12': 12, 'population (2006)_13': 13} | {'greater_4': 'greater', 'result_5': 'true', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'mother tongue_7': 'mother tongue', 'romanian_8': 'romanian', 'population (2006)_9': 'population ( 2006 )', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'mother tongue_11': 'mother tongue', 'polish_12': 'polish', 'population (2006)_13': 'population ( 2006 )'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'mother tongue_7': [0], 'romanian_8': [0], 'population (2006)_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'mother tongue_11': [1], 'polish_12': [1], 'population (2006)_13': [3]} | ['mother tongue', 'population ( 2006 )', 'percentage ( 2006 )', 'population ( 2011 )', 'percentage ( 2011 )'] | [['english', '8085', '45.22 %', '7615', '42.41 %'], ['french', '5400', '30.20 %', '5490', '30.57 %'], ['chinese languages', '650', '3.64 %', '470', '2.62 %'], ['italian', '590', '3.30 %', '510', '2.84 %'], ['spanish', '315', '1.76 %', '515', '2.87 %'], ['romanian', '300', '1.68 %', '235', '1.31 %'], ['arabic', '295', '1.65 %', '350', '1.95 %'], ['polish', '205', '1.15 %', '145', '0.81 %'], ['filipino', '170', '0.95 %', '200', '1.11 %'], ['english and french', '250', '1.40 %', '390', '2.17 %'], ['english and a non - official language', '120', '0.67 %', '190', '1.06 %'], ['french and a non - official language', '50', '0.28 %', '145', '0.81 %']] |
2008 - 09 west ham united f.c. season | https://en.wikipedia.org/wiki/2008%E2%80%9309_West_Ham_United_F.C._season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18539546-24.html.csv | ordinal | columbus crew was the 2nd opponent west ham united f.c. played against during the 2008 - 09 season / . | {'row': '2', 'col': '1', 'order': '2', 'col_other': '5', 'max_or_min': 'min_to_max', 'value_mentioned': 'yes', 'scope': 'all', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'nth_min', 'args': ['all_rows', 'match', '2'], 'result': '2', 'ind': 0, 'tostr': 'nth_min { all_rows ; match ; 2 }', 'tointer': 'the 2nd minimum match record of all rows is 2 .'}, '2'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_min { all_rows ; match ; 2 } ; 2 }', 'tointer': 'the 2nd minimum match record of all rows is 2 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'match', '2'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; match ; 2 }'}, 'opponent'], 'result': 'columbus crew', 'ind': 3, 'tostr': 'hop { nth_argmin { all_rows ; match ; 2 } ; opponent }'}, 'columbus crew'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { nth_argmin { all_rows ; match ; 2 } ; opponent } ; columbus crew }', 'tointer': 'the opponent record of the row with 2nd minimum match record is columbus crew .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { nth_min { all_rows ; match ; 2 } ; 2 } ; eq { hop { nth_argmin { all_rows ; match ; 2 } ; opponent } ; columbus crew } } = true', 'tointer': 'the 2nd minimum match record of all rows is 2 . the opponent record of the row with 2nd minimum match record is columbus crew .'} | and { eq { nth_min { all_rows ; match ; 2 } ; 2 } ; eq { hop { nth_argmin { all_rows ; match ; 2 } ; opponent } ; columbus crew } } = true | the 2nd minimum match record of all rows is 2 . the opponent record of the row with 2nd minimum match record is columbus crew . | 6 | 6 | {'and_5': 5, 'result_6': 6, 'eq_1': 1, 'nth_min_0': 0, 'all_rows_7': 7, 'match_8': 8, '2_9': 9, '2_10': 10, 'str_eq_4': 4, 'str_hop_3': 3, 'nth_argmin_2': 2, 'all_rows_11': 11, 'match_12': 12, '2_13': 13, 'opponent_14': 14, 'columbus crew_15': 15} | {'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'nth_min_0': 'nth_min', 'all_rows_7': 'all_rows', 'match_8': 'match', '2_9': '2', '2_10': '2', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'nth_argmin_2': 'nth_argmin', 'all_rows_11': 'all_rows', 'match_12': 'match', '2_13': '2', 'opponent_14': 'opponent', 'columbus crew_15': 'columbus crew'} | {'and_5': [6], 'result_6': [], 'eq_1': [5], 'nth_min_0': [1], 'all_rows_7': [0], 'match_8': [0], '2_9': [0], '2_10': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'nth_argmin_2': [3], 'all_rows_11': [2], 'match_12': [2], '2_13': [2], 'opponent_14': [3], 'columbus crew_15': [4]} | ['match', 'date', 'competition or tour', 'ground', 'opponent', 'score1', 'scorers'] | [['1', '17 july 2008', 'friendly', 'a', 'hampton & richmond', '4 - 2', "bellamy 37 ' & 60 ' , noble 41 ' , hines 81 '"], ['2', '20 july 2008', 'friendly', 'a', 'columbus crew', '3 - 1', "ashton 6 ' , evans og 26 ' , reid 53 '"], ['3', '24 july 2008', 'major league soccer all - star game', 'a', 'major league all stars', '2 - 3', "ashton 26 ' & 67 '"], ['4', '29 july 2008', 'friendly', 'a', 'peterborough united', '2 - 0', "bellamy 3 ' & 42 '"], ['5', '1 august 2008', 'friendly', 'a', 'southampton', '2 - 2', "davenport 45 ' , hines 65 '"], ['6', '4 august 2008', 'friendly', 'a', 'ipswich town', '5 - 3', "ashton 4 ' , 57 ' & 74 ' , bellamy 10 ' , noble 79 '"], ['7', '9 august 2008', 'the bobby moore cup', 'h', 'villarreal', '1 - 1', "cole 1 '"]] |
doppler spectroscopy | https://en.wikipedia.org/wiki/Doppler_spectroscopy | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-10932739-2.html.csv | aggregation | the average radial velocities of the planets studied by doppler spectroscopy is about 14-15 m/s . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '14-15', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'radial velocity ( m / s )'], 'result': '14-15', 'ind': 0, 'tostr': 'avg { all_rows ; radial velocity ( m / s ) }'}, '14-15'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; radial velocity ( m / s ) } ; 14-15 } = true', 'tointer': 'the average of the radial velocity ( m / s ) record of all rows is 14-15 .'} | round_eq { avg { all_rows ; radial velocity ( m / s ) } ; 14-15 } = true | the average of the radial velocity ( m / s ) record of all rows is 14-15 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'radial velocity (m / s)_4': 4, '14-15_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'radial velocity (m / s)_4': 'radial velocity ( m / s )', '14-15_5': '14-15'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'radial velocity (m / s)_4': [0], '14-15_5': [1]} | ['planet', 'planet type', 'semimajor axis ( au )', 'orbital period', 'radial velocity ( m / s )', 'detectable by :'] | [['51 pegasi b', 'hot jupiter', '0.05', '4.23 days', '55.9', 'first - generation spectrograph'], ['55 cancri d', 'gas giant', '5.77', '14.29 years', '45.2', 'first - generation spectrograph'], ['jupiter', 'gas giant', '5.20', '11.86 years', '12.4', 'first - generation spectrograph'], ['gliese 581c', 'super - earth', '0.07', '12.92 days', '3.18', 'second - generation spectrograph'], ['saturn', 'gas giant', '9.58', '29.46 years', '2.75', 'second - generation spectrograph'], ['alpha centauri bb', 'terrestrial planet', '0.04', '3.23 days', '0.510', 'second - generation spectrograph'], ['neptune', 'ice giant', '30.10', '164.79 years', '0.281', 'third - generation spectrograph'], ['earth', 'habitable planet', '1.00', '365.26 days', '0.089', 'third - generation spectrograph ( likely )']] |
east kent mavericks | https://en.wikipedia.org/wiki/East_Kent_Mavericks | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16994082-1.html.csv | superlative | the east kent mavericks had the most amount of ties in their 2011 season . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '9', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'ties'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; ties }'}, 'season'], 'result': '2011', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; ties } ; season }'}, '2011'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; ties } ; season } ; 2011 } = true', 'tointer': 'select the row whose ties record of all rows is maximum . the season record of this row is 2011 .'} | eq { hop { argmax { all_rows ; ties } ; season } ; 2011 } = true | select the row whose ties record of all rows is maximum . the season record of this row is 2011 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'ties_5': 5, 'season_6': 6, '2011_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'ties_5': 'ties', 'season_6': 'season', '2011_7': '2011'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'ties_5': [0], 'season_6': [1], '2011_7': [2]} | ['season', 'division', 'wins', 'ties', 'final position'] | [['2001', 'british senior flag league , southern', '3', '1', '2 / 4'], ['2002', 'british senior flag league , nine - man league', '5', '3', '2 / 7'], ['2003 to 2005', 'did not compete', 'did not compete', 'did not compete', 'did not compete'], ['2006', 'bafl division two south', '0', '0', '4 / 4'], ['2007', 'bafl division two south east', '5', '0', '3 / 6'], ['2008', 'bafl division two south east', '6', '0', '3 / 5'], ['2009', 'bafl division two south east', '8', '1', '1 / 4'], ['2010', 'bafl division one south east', '8', '1', '1 / 4'], ['2011', 'bafl division one south east', '2', '6', ''], ['2012', 'bafl division one south and central', '8', '2', '8 / 2']] |
2008 ford world women 's curling championship | https://en.wikipedia.org/wiki/2008_Ford_World_Women%27s_Curling_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1644876-2.html.csv | ordinal | of the teams that won less than 50 ends germany achieved the most stolen ends , 14 . | {'scope': 'subset', 'row': '9', 'col': '6', 'order': '1', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'yes', 'subset': {'col': '3', 'criterion': 'less_than', 'value': '50'}} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'nth_max', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'ends won', '50'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; ends won ; 50 }', 'tointer': 'select the rows whose ends won record is less than 50 .'}, 'stolen ends', '1'], 'result': '14', 'ind': 1, 'tostr': 'nth_max { filter_less { all_rows ; ends won ; 50 } ; stolen ends ; 1 }', 'tointer': 'select the rows whose ends won record is less than 50 . the 1st maximum stolen ends record of these rows is 14 .'}, '14'], 'result': True, 'ind': 2, 'tostr': 'eq { nth_max { filter_less { all_rows ; ends won ; 50 } ; stolen ends ; 1 } ; 14 }', 'tointer': 'select the rows whose ends won record is less than 50 . the 1st maximum stolen ends record of these rows is 14 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'ends won', '50'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; ends won ; 50 }', 'tointer': 'select the rows whose ends won record is less than 50 .'}, 'stolen ends', '1'], 'result': None, 'ind': 3, 'tostr': 'nth_argmax { filter_less { all_rows ; ends won ; 50 } ; stolen ends ; 1 }'}, 'locale'], 'result': 'germany', 'ind': 4, 'tostr': 'hop { nth_argmax { filter_less { all_rows ; ends won ; 50 } ; stolen ends ; 1 } ; locale }'}, 'germany'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { nth_argmax { filter_less { all_rows ; ends won ; 50 } ; stolen ends ; 1 } ; locale } ; germany }', 'tointer': 'the locale record of the row with 1st maximum stolen ends record is germany .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { nth_max { filter_less { all_rows ; ends won ; 50 } ; stolen ends ; 1 } ; 14 } ; eq { hop { nth_argmax { filter_less { all_rows ; ends won ; 50 } ; stolen ends ; 1 } ; locale } ; germany } } = true', 'tointer': 'select the rows whose ends won record is less than 50 . the 1st maximum stolen ends record of these rows is 14 . the locale record of the row with 1st maximum stolen ends record is germany .'} | and { eq { nth_max { filter_less { all_rows ; ends won ; 50 } ; stolen ends ; 1 } ; 14 } ; eq { hop { nth_argmax { filter_less { all_rows ; ends won ; 50 } ; stolen ends ; 1 } ; locale } ; germany } } = true | select the rows whose ends won record is less than 50 . the 1st maximum stolen ends record of these rows is 14 . the locale record of the row with 1st maximum stolen ends record is germany . | 8 | 7 | {'and_6': 6, 'result_7': 7, 'eq_2': 2, 'nth_max_1': 1, 'filter_less_0': 0, 'all_rows_8': 8, 'ends won_9': 9, '50_10': 10, 'stolen ends_11': 11, '1_12': 12, '14_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'nth_argmax_3': 3, 'stolen ends_14': 14, '1_15': 15, 'locale_16': 16, 'germany_17': 17} | {'and_6': 'and', 'result_7': 'true', 'eq_2': 'eq', 'nth_max_1': 'nth_max', 'filter_less_0': 'filter_less', 'all_rows_8': 'all_rows', 'ends won_9': 'ends won', '50_10': '50', 'stolen ends_11': 'stolen ends', '1_12': '1', '14_13': '14', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'nth_argmax_3': 'nth_argmax', 'stolen ends_14': 'stolen ends', '1_15': '1', 'locale_16': 'locale', 'germany_17': 'germany'} | {'and_6': [7], 'result_7': [], 'eq_2': [6], 'nth_max_1': [2], 'filter_less_0': [1, 3], 'all_rows_8': [0], 'ends won_9': [0], '50_10': [0], 'stolen ends_11': [1], '1_12': [1], '14_13': [2], 'str_eq_5': [6], 'str_hop_4': [5], 'nth_argmax_3': [4], 'stolen ends_14': [3], '1_15': [3], 'locale_16': [4], 'germany_17': [5]} | ['locale', 'skip', 'ends won', 'ends lost', 'blank ends', 'stolen ends', 'shot %'] | [['china', 'wang bingyu', '57', '43', '6', '18', '80 %'], ['canada', 'jennifer jones', '46', '43', '13', '9', '84 %'], ['switzerland', 'mirjam ott', '51', '46', '7', '11', '81 %'], ['japan', 'moe meguro', '49', '45', '17', '13', '77 %'], ['denmark', 'angelina jensen', '44', '51', '16', '7', '79 %'], ['sweden', 'stina viktorsson', '45', '51', '10', '7', '80 %'], ['united states', 'debbie mccormick', '51', '52', '6', '13', '78 %'], ['russia', 'ludmila privivkova', '45', '48', '11', '12', '78 %'], ['germany', 'andrea schöpp', '49', '45', '17', '14', '77 %'], ['scotland', 'gail munro', '43', '48', '17', '8', '77 %'], ['italy', 'diana gaspari', '45', '47', '14', '10', '74 %'], ['czech republic', 'kateřina urbanová', '40', '46', '16', '10', '72 %']] |
1935 vfl season | https://en.wikipedia.org/wiki/1935_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10790651-6.html.csv | majority | in the 1935 vfl season , most games on 3 june 1935 attracted more than 22000 people . | {'scope': 'subset', 'col': '6', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '22000', 'subset': {'col': '7', 'criterion': 'equal', 'value': '3 june 1935'}} | {'func': 'most_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '3 june 1935'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; 3 june 1935 }', 'tointer': 'select the rows whose date record fuzzily matches to 3 june 1935 .'}, 'crowd', '22000'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to 3 june 1935 . for the crowd records of these rows , most of them are greater than 22000 .', 'tostr': 'most_greater { filter_eq { all_rows ; date ; 3 june 1935 } ; crowd ; 22000 } = true'} | most_greater { filter_eq { all_rows ; date ; 3 june 1935 } ; crowd ; 22000 } = true | select the rows whose date record fuzzily matches to 3 june 1935 . for the crowd records of these rows , most of them are greater than 22000 . | 2 | 2 | {'most_greater_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'date_4': 4, '3 june 1935_5': 5, 'crowd_6': 6, '22000_7': 7} | {'most_greater_1': 'most_greater', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'date_4': 'date', '3 june 1935_5': '3 june 1935', 'crowd_6': 'crowd', '22000_7': '22000'} | {'most_greater_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'date_4': [0], '3 june 1935_5': [0], 'crowd_6': [1], '22000_7': [1]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['collingwood', '23.11 ( 149 )', 'footscray', '14.14 ( 98 )', 'victoria park', '17500', '1 june 1935'], ['hawthorn', '14.9 ( 93 )', 'geelong', '22.19 ( 151 )', 'glenferrie oval', '9500', '1 june 1935'], ['south melbourne', '23.14 ( 152 )', 'fitzroy', '10.11 ( 71 )', 'lake oval', '33000', '1 june 1935'], ['melbourne', '15.13 ( 103 )', 'st kilda', '15.20 ( 110 )', 'mcg', '22711', '3 june 1935'], ['essendon', '12.13 ( 85 )', 'richmond', '13.9 ( 87 )', 'windy hill', '26000', '3 june 1935'], ['carlton', '20.16 ( 136 )', 'north melbourne', '9.14 ( 68 )', 'princes park', '20000', '3 june 1935']] |
dwsn | https://en.wikipedia.org/wiki/DWSN | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17487395-1.html.csv | majority | the majority of the radio brandings in mom 's radio consumes 5 kw of power . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': '5', 'subset': None} | {'func': 'most_eq', 'args': ['all_rows', 'power ( kw )', '5'], 'result': True, 'ind': 0, 'tointer': 'for the power ( kw ) records of all rows , most of them are equal to 5 .', 'tostr': 'most_eq { all_rows ; power ( kw ) ; 5 } = true'} | most_eq { all_rows ; power ( kw ) ; 5 } = true | for the power ( kw ) records of all rows , most of them are equal to 5 . | 1 | 1 | {'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'power (kw)_3': 3, '5_4': 4} | {'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'power (kw)_3': 'power ( kw )', '5_4': '5'} | {'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'power (kw)_3': [0], '5_4': [0]} | ['branding', 'callsign', 'frequency', 'power ( kw )', 'location'] | [["mom 's radio 97.9 laoag", 'dwsn - fm', '97.9 mhz', '5 kw', 'laoag'], ["mom 's radio 95.9 naga", 'dzrb - fm', '95.9 mhz', '10 kw', 'naga'], ["mom 's radio 90.3 bacolod", 'dycp - fm', '90.3 mhz', '5 kw', 'bacolod'], ["mom 's radio 88.3 cebu", 'dyap - fm', '88.3 mhz', '5 kw', 'cebu'], ["mom 's radio 101.5 tacloban", 'dyjp - fm', '101.5 mhz', '2.5 kw', 'tacloban'], ["mom 's radio 101.9 zamboanga", 'dxjp - fm', '101.9 mhz', '5 kw', 'zamboanga'], ["mom 's radio 97.9 davao", 'dxss', '97.9 mhz', '10 kw', 'davao']] |
simon whitlock | https://en.wikipedia.org/wiki/Simon_Whitlock | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10254961-3.html.csv | superlative | simon whitlock 's best score of all time from 2010-2013 was in legs , with a score of 11-5 . | {'scope': 'all', 'col_superlative': '5', 'row_superlative': '3', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': 'n/a', 'subset': None} | {'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'score ( l ) = score in legs , ( s ) = score in sets'], 'result': '11 - 5 ( l )', 'ind': 0, 'tostr': 'max { all_rows ; score ( l ) = score in legs , ( s ) = score in sets }', 'tointer': 'the maximum score ( l ) = score in legs , ( s ) = score in sets record of all rows is 11 - 5 ( l ) .'}, '11 - 5 ( l )'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; score ( l ) = score in legs , ( s ) = score in sets } ; 11 - 5 ( l ) } = true', 'tointer': 'the maximum score ( l ) = score in legs , ( s ) = score in sets record of all rows is 11 - 5 ( l ) .'} | eq { max { all_rows ; score ( l ) = score in legs , ( s ) = score in sets } ; 11 - 5 ( l ) } = true | the maximum score ( l ) = score in legs , ( s ) = score in sets record of all rows is 11 - 5 ( l ) . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'max_0': 0, 'all_rows_3': 3, 'score (l) = score in legs , (s) = score in sets_4': 4, '11 - 5 (l)_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'max_0': 'max', 'all_rows_3': 'all_rows', 'score (l) = score in legs , (s) = score in sets_4': 'score ( l ) = score in legs , ( s ) = score in sets', '11 - 5 (l)_5': '11 - 5 ( l )'} | {'eq_1': [2], 'result_2': [], 'max_0': [1], 'all_rows_3': [0], 'score (l) = score in legs , (s) = score in sets_4': [0], '11 - 5 (l)_5': [1]} | ['outcome', 'year', 'championship', 'opponent in the final', 'score ( l ) = score in legs , ( s ) = score in sets'] | [['runner - up', '2010', 'world darts championship', 'phil taylor', '3 - 7 ( s )'], ['runner - up', '2012', 'premier league darts', 'phil taylor', '7 - 10 ( l )'], ['winner', '2012', 'european championship', 'wes newton', '11 - 5 ( l )'], ['runner - up', '2012', 'championship league darts', 'phil taylor', '4 - 6 ( l )'], ['runner - up', '2013', 'european championship', 'adrian lewis', '6 - 11 ( l )']] |
washington redskins draft history | https://en.wikipedia.org/wiki/Washington_Redskins_draft_history | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17100961-50.html.csv | count | 8 players are listed in the washington redskins draft history record . | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '8', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'name'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record is arbitrary .', 'tostr': 'filter_all { all_rows ; name }'}], 'result': '8', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; name } }', 'tointer': 'select the rows whose name record is arbitrary . the number of such rows is 8 .'}, '8'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; name } } ; 8 } = true', 'tointer': 'select the rows whose name record is arbitrary . the number of such rows is 8 .'} | eq { count { filter_all { all_rows ; name } } ; 8 } = true | select the rows whose name record is arbitrary . the number of such rows is 8 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'name_5': 5, '8_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'name_5': 'name', '8_6': '8'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'name_5': [0], '8_6': [2]} | ['round', 'pick', 'overall', 'name', 'position', 'college'] | [['1', '18', '18', 'art monk', 'wr', 'syracuse'], ['2', '27', '55', 'mat mendenhall', 'de', 'brigham young'], ['6', '17', '155', 'farley bell', 'lb', 'cincinnati'], ['7', '22', '187', 'melvin jones', 'g', 'houston'], ['9', '20', '241', 'lawrence mccullough', 'wr', 'illinois'], ['10', '19', '268', 'lewis walker', 'rb', 'utah'], ['11', '18', '295', 'mike matocha', 'de', 'texas - arlington'], ['12', '22', '327', 'marcene emmett', 'db', 'north alabama']] |
world golf championships | https://en.wikipedia.org/wiki/World_Golf_Championships | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1458666-4.html.csv | aggregation | the average number of individual winners in the world golf championships for all nations is 2.08 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '2.08', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'individual winners'], 'result': '2.08', 'ind': 0, 'tostr': 'avg { all_rows ; individual winners }'}, '2.08'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; individual winners } ; 2.08 } = true', 'tointer': 'the average of the individual winners record of all rows is 2.08 .'} | round_eq { avg { all_rows ; individual winners } ; 2.08 } = true | the average of the individual winners record of all rows is 2.08 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'individual winners_4': 4, '2.08_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'individual winners_4': 'individual winners', '2.08_5': '2.08'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'individual winners_4': [0], '2.08_5': [1]} | ['nation', 'total wins', 'team wins', 'individual wins', 'individual winners'] | [['united states', '32', '1', '31', '12'], ['australia', '5', '0', '5', '3'], ['england', '5', '1', '4', '3'], ['south africa', '4', '2', '2', '1'], ['northern ireland', '2', '0', '2', '1'], ['germany', '2', '1', '1', '1'], ['canada', '1', '0', '1', '1'], ['fiji', '1', '0', '1', '1'], ['sweden', '1', '0', '1', '1'], ['italy', '1', '0', '1', '1'], ['japan', '1', '1', '0', '0'], ['wales', '1', '1', '0', '0']] |
2002 french motorcycle grand prix | https://en.wikipedia.org/wiki/2002_French_motorcycle_Grand_Prix | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17036702-1.html.csv | superlative | carlos checa was the rider that completed the least amount of laps in the 2002 french motorcycle grand prix . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '20', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'laps'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; laps }'}, 'rider'], 'result': 'carlos checa', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; laps } ; rider }'}, 'carlos checa'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; laps } ; rider } ; carlos checa } = true', 'tointer': 'select the row whose laps record of all rows is minimum . the rider record of this row is carlos checa .'} | eq { hop { argmin { all_rows ; laps } ; rider } ; carlos checa } = true | select the row whose laps record of all rows is minimum . the rider record of this row is carlos checa . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'laps_5': 5, 'rider_6': 6, 'carlos checa_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'laps_5': 'laps', 'rider_6': 'rider', 'carlos checa_7': 'carlos checa'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'laps_5': [0], 'rider_6': [1], 'carlos checa_7': [2]} | ['rider', 'manufacturer', 'laps', 'time / retired', 'grid'] | [['valentino rossi', 'honda', '21', '34:22.335', '1'], ['tohru ukawa', 'honda', '21', '+ 0.217', '4'], ['max biaggi', 'yamaha', '21', '+ 0.604', '3'], ['norifumi abe', 'yamaha', '21', '+ 1.701', '11'], ['kenny roberts , jr', 'suzuki', '21', '+ 8.464', '9'], ['nobuatsu aoki', 'proton kr', '21', '+ 10.212', '10'], ['loris capirossi', 'honda', '21', '+ 12.437', '7'], ['alex barros', 'honda', '21', '+ 15.231', '15'], ['régis laconi', 'aprilia', '21', '+ 17.155', '14'], ['jeremy mcwilliams', 'proton kr', '21', '+ 21.847', '6'], ['john hopkins', 'yamaha', '21', '+ 25.121', '19'], ['sete gibernau', 'suzuki', '21', '+ 25.919', '16'], ['shinya nakano', 'yamaha', '21', '+ 26.227', '13'], ['jean - michel bayle', 'yamaha', '21', '+ 27.011', '18'], ['jurgen vd goorbergh', 'honda', '21', '+ 30.342', '17'], ['josé luis cardoso', 'yamaha', '21', '+ 36.574', '20'], ['daijiro kato', 'honda', '11', 'accident', '5'], ['olivier jacque', 'yamaha', '10', 'retirement', '12'], ['tetsuya harada', 'honda', '10', 'retirement', '8'], ['carlos checa', 'yamaha', '8', 'accident', '2']] |
1983 - 84 fa cup | https://en.wikipedia.org/wiki/1983%E2%80%9384_FA_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17437287-6.html.csv | count | three of the matches took place on 10 march 1984 . | {'scope': 'all', 'criterion': 'equal', 'value': '10 march 1984', 'result': '3', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', '10 march 1984'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to 10 march 1984 .', 'tostr': 'filter_eq { all_rows ; date ; 10 march 1984 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; date ; 10 march 1984 } }', 'tointer': 'select the rows whose date record fuzzily matches to 10 march 1984 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; date ; 10 march 1984 } } ; 3 } = true', 'tointer': 'select the rows whose date record fuzzily matches to 10 march 1984 . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; date ; 10 march 1984 } } ; 3 } = true | select the rows whose date record fuzzily matches to 10 march 1984 . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'date_5': 5, '10 march 1984_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'date_5': 'date', '10 march 1984_6': '10 march 1984', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'date_5': [0], '10 march 1984_6': [0], '3_7': [2]} | ['tie no', 'home team', 'score', 'away team', 'date'] | [['1', 'notts county', '1 - 2', 'everton', '10 march 1984'], ['2', 'sheffield wednesday', '0 - 0', 'southampton', '11 march 1984'], ['replay', 'southampton', '5 - 1', 'sheffield wednesday', '20 march 1984'], ['3', 'plymouth argyle', '0 - 0', 'derby county', '10 march 1984'], ['replay', 'derby county', '0 - 1', 'plymouth argyle', '14 march 1984'], ['4', 'birmingham city', '1 - 3', 'watford', '10 march 1984']] |
1990 pga championship | https://en.wikipedia.org/wiki/1990_PGA_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18132874-4.html.csv | count | of the players from the united states in the 1990 pga championship , there were three that won more than 52000 in currency . | {'scope': 'subset', 'criterion': 'greater_than', 'value': '52000', 'result': '3', 'col': '6', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'united states'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'united states'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; country ; united states }', 'tointer': 'select the rows whose country record fuzzily matches to united states .'}, 'money', '52000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose money record is greater than 52000 .', 'tostr': 'filter_greater { filter_eq { all_rows ; country ; united states } ; money ; 52000 }'}], 'result': '3', 'ind': 2, 'tostr': 'count { filter_greater { filter_eq { all_rows ; country ; united states } ; money ; 52000 } }', 'tointer': 'select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose money record is greater than 52000 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_greater { filter_eq { all_rows ; country ; united states } ; money ; 52000 } } ; 3 } = true', 'tointer': 'select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose money record is greater than 52000 . the number of such rows is 3 .'} | eq { count { filter_greater { filter_eq { all_rows ; country ; united states } ; money ; 52000 } } ; 3 } = true | select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose money record is greater than 52000 . the number of such rows is 3 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_greater_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'country_6': 6, 'united states_7': 7, 'money_8': 8, '52000_9': 9, '3_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_greater_1': 'filter_greater', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'country_6': 'country', 'united states_7': 'united states', 'money_8': 'money', '52000_9': '52000', '3_10': '3'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_greater_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'country_6': [0], 'united states_7': [0], 'money_8': [1], '52000_9': [1], '3_10': [3]} | ['place', 'player', 'country', 'score', 'to par', 'money'] | [['1', 'wayne grady', 'australia', '72 + 67 + 72 + 71 = 282', '- 6', '225000'], ['2', 'fred couples', 'united states', '69 + 71 + 73 + 72 = 285', '- 3', '135000'], ['3', 'gil morgan', 'united states', '77 + 72 + 65 + 72 = 286', '- 2', '90000'], ['4', 'bill britton', 'united states', '72 + 74 + 72 + 71 = 289', '+ 1', '73500'], ['t5', 'chip beck', 'united states', '71 + 70 + 78 + 71 = 290', '+ 2', '51667'], ['t5', 'billy mayfair', 'united states', '70 + 71 + 75 + 74 = 290', '+ 2', '51667'], ['t5', 'loren roberts', 'united states', '73 + 71 + 70 + 76 = 290', '+ 2', '51667'], ['t8', 'mark mcnulty', 'zimbabwe', '74 + 72 + 75 + 71 = 292', '+ 4', '34375'], ['t8', 'don pooley', 'united states', '75 + 74 + 71 + 72 = 292', '+ 4', '34375'], ['t8', 'tim simpson', 'united states', '71 + 73 + 75 + 73 = 292', '+ 4', '34375'], ['t8', 'payne stewart', 'united states', '71 + 72 + 70 + 79 = 292', '+ 4', '34375']] |
sandro floris | https://en.wikipedia.org/wiki/Sandro_Floris | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18284124-1.html.csv | unique | the only time sandro floris finished in the 8th position was at the 1990 european championships . | {'scope': 'all', 'row': '5', 'col': '4', 'col_other': '1,2', 'criterion': 'equal', 'value': '8th', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', '8th'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to 8th .', 'tostr': 'filter_eq { all_rows ; position ; 8th }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; position ; 8th } }', 'tointer': 'select the rows whose position record fuzzily matches to 8th . there is only one such row in the table .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', '8th'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to 8th .', 'tostr': 'filter_eq { all_rows ; position ; 8th }'}, 'year'], 'result': '1990', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; position ; 8th } ; year }'}, '1990'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; position ; 8th } ; year } ; 1990 }', 'tointer': 'the year record of this unqiue row is 1990 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', '8th'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to 8th .', 'tostr': 'filter_eq { all_rows ; position ; 8th }'}, 'competition'], 'result': 'european championships', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; position ; 8th } ; competition }'}, 'european championships'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; position ; 8th } ; competition } ; european championships }', 'tointer': 'the competition record of this unqiue row is european championships .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; position ; 8th } ; year } ; 1990 } ; eq { hop { filter_eq { all_rows ; position ; 8th } ; competition } ; european championships } }', 'tointer': 'the year record of this unqiue row is 1990 . the competition record of this unqiue row is european championships .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; position ; 8th } } ; and { eq { hop { filter_eq { all_rows ; position ; 8th } ; year } ; 1990 } ; eq { hop { filter_eq { all_rows ; position ; 8th } ; competition } ; european championships } } } = true', 'tointer': 'select the rows whose position record fuzzily matches to 8th . there is only one such row in the table . the year record of this unqiue row is 1990 . the competition record of this unqiue row is european championships .'} | and { only { filter_eq { all_rows ; position ; 8th } } ; and { eq { hop { filter_eq { all_rows ; position ; 8th } ; year } ; 1990 } ; eq { hop { filter_eq { all_rows ; position ; 8th } ; competition } ; european championships } } } = true | select the rows whose position record fuzzily matches to 8th . there is only one such row in the table . the year record of this unqiue row is 1990 . the competition record of this unqiue row is european championships . | 10 | 8 | {'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, 'position_10': 10, '8th_11': 11, 'and_6': 6, 'eq_3': 3, 'num_hop_2': 2, 'year_12': 12, '1990_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'competition_14': 14, 'european championships_15': 15} | {'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_9': 'all_rows', 'position_10': 'position', '8th_11': '8th', 'and_6': 'and', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_12': 'year', '1990_13': '1990', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'competition_14': 'competition', 'european championships_15': 'european championships'} | {'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], 'position_10': [0], '8th_11': [0], 'and_6': [7], 'eq_3': [6], 'num_hop_2': [3], 'year_12': [2], '1990_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'competition_14': [4], 'european championships_15': [5]} | ['year', 'competition', 'venue', 'position', 'event'] | [['1988', 'olympic games', 'seoul , south korea', '5th', '4x100 m relay'], ['1989', 'world indoor championships', 'budapest , hungary', '4th', '200 m'], ['1989', 'european indoor championships', 'the hague , netherlands', '5th', '200 m'], ['1990', 'european indoor championships', 'glasgow , scotland', '1st', '200 m'], ['1990', 'european championships', 'split , yugoslavia', '8th', '200 m'], ['1990', 'european championships', 'split , yugoslavia', '3rd', '4x100 m relay'], ['1991', 'world championships', 'tokyo , japan', '5th', '4x100 m relay'], ['1991', 'mediterranean games', 'athens , greece', '3rd', '200 m'], ['1994', 'european championships', 'helsinki , finland', '3rd', '4x100 m relay'], ['1995', 'world championships', 'gothenburg , sweden', '3rd', '4x100 m relay'], ['1997', 'mediterranean games', 'bari , italy', '1st', '4x100 m relay']] |
1988 - 89 segunda división | https://en.wikipedia.org/wiki/1988%E2%80%9389_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12107896-2.html.csv | majority | in the 1988 - 89 segunda división , every team played 38 games . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': '38', 'subset': None} | {'func': 'most_eq', 'args': ['all_rows', 'played', '38'], 'result': True, 'ind': 0, 'tointer': 'for the played records of all rows , most of them are equal to 38 .', 'tostr': 'most_eq { all_rows ; played ; 38 } = true'} | most_eq { all_rows ; played ; 38 } = true | for the played records of all rows , most of them are equal to 38 . | 1 | 1 | {'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'played_3': 3, '38_4': 4} | {'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'played_3': 'played', '38_4': '38'} | {'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'played_3': [0], '38_4': [0]} | ['position', 'club', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference'] | [['1', 'cd castellón', '38', '51 + 13', '21', '9', '8', '49', '29', '+ 20'], ['2', 'rayo vallecano', '38', '49 + 11', '19', '11', '8', '61', '36', '+ 25'], ['3', 'cd tenerife', '38', '48 + 10', '20', '8', '10', '54', '36', '+ 18'], ['4', 'rcd mallorca', '38', '48 + 10', '21', '6', '11', '51', '26', '+ 25'], ['5', 'recreativo de huelva', '38', '42 + 4', '16', '10', '12', '46', '36', '+ 10'], ['6', 'racing de santander', '38', '42 + 4', '17', '8', '13', '56', '43', '+ 13'], ['7', 'ud salamanca', '38', '42 + 4', '14', '14', '10', '35', '33', '+ 2'], ['8', 'sestao', '38', '41 + 3', '14', '13', '11', '39', '32', '+ 7'], ['9', 'ue figueres', '38', '41 + 3', '16', '9', '13', '52', '50', '+ 2'], ['10', 'deportivo de la coruña', '38', '40 + 2', '16', '8', '14', '43', '35', '+ 8'], ['11', 'ud las palmas', '38', '40 + 2', '15', '10', '13', '52', '53', '- 1'], ['12', 'xerez cd', '38', '40 + 2', '13', '14', '11', '40', '38', '+ 2'], ['13', 'ce sabadell fc', '38', '39 + 1', '15', '9', '14', '49', '43', '+ 6'], ['14', 'real burgos', '38', '36 - 2', '9', '18', '11', '27', '34', '- 7'], ['15', 'castilla cf', '38', '36 - 2', '13', '10', '15', '50', '59', '- 9'], ['16', 'sd eibar', '38', '34 - 4', '8', '18', '12', '36', '42', '- 6'], ['17', 'barcelona atlètic', '38', '28 - 10', '8', '12', '18', '42', '58', '- 16'], ['18', 'ud alzira', '38', '26 - 12', '9', '8', '21', '29', '58', '- 29'], ['19', 'ue lleida', '38', '26 - 12', '8', '10', '20', '29', '43', '- 16'], ['20', 'cfj mollerussa', '38', '11 - 27', '3', '5', '30', '19', '75', '- 56']] |
missouri tigers men 's basketball | https://en.wikipedia.org/wiki/Missouri_Tigers_men%27s_basketball | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16201038-4.html.csv | ordinal | drake is the basketball team that the missouri tiger 's have the second highest winning current streak against . | {'row': '3', 'col': '8', 'order': '2', 'col_other': '1', 'max_or_min': 'max_to_min', 'value_mentioned': 'no', 'scope': 'all', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmax', 'args': ['all_rows', 'current streak', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; current streak ; 2 }'}, 'missouri vs'], 'result': 'drake', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; current streak ; 2 } ; missouri vs }'}, 'drake'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; current streak ; 2 } ; missouri vs } ; drake } = true', 'tointer': 'select the row whose current streak record of all rows is 2nd maximum . the missouri vs record of this row is drake .'} | eq { hop { nth_argmax { all_rows ; current streak ; 2 } ; missouri vs } ; drake } = true | select the row whose current streak record of all rows is 2nd maximum . the missouri vs record of this row is drake . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'current streak_5': 5, '2_6': 6, 'missouri vs_7': 7, 'drake_8': 8} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'nth_argmax_0': 'nth_argmax', 'all_rows_4': 'all_rows', 'current streak_5': 'current streak', '2_6': '2', 'missouri vs_7': 'missouri vs', 'drake_8': 'drake'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'current streak_5': [0], '2_6': [0], 'missouri vs_7': [1], 'drake_8': [2]} | ['missouri vs', 'overall record', 'columbia', 'opponents venue', 'neutral site', 'last 5 meetings', 'last 10 meetings', 'current streak'] | [['colorado', 'mu , 99 - 53', 'mu , 57 - 11', 'cu , 34 - 30', 'mu , 12 - 8', 'mu , 4 - 1', 'mu , 9 - 1', 'w 1'], ['creighton', 'mu , 9 - 7', 'mu , 3 - 2', 'tied , 4 - 4', 'mu , 2 - 1', 'mu , 3 - 2', 'cu , 6 - 4', 'l 1'], ['drake', 'mu , 27 - 7', 'mu , 17 - 3', 'mu , 10 - 4', 'tied , 0 - 0', 'mu , 4 - 1', 'mu , 8 - 2', 'w 4'], ['illinois', 'ui , 27 - 16', 'ui , 3 - 2', 'ui , 4 - 1', 'ui , 20 - 13', 'mu , 4 - 1', 'ui , 6 - 4', 'w 4'], ['indiana', 'tied , 9 - 9', 'mu , 5 - 3', 'iu , 6 - 3', 'mu , 1 - 0', 'mu , 4 - 1', 'tied , 5 - 5', 'w 3'], ['iowa', 'ui , 10 - 7', 'mu , 4 - 2', 'ui , 7 - 2', 'tied , 1 - 1', 'mu , 3 - 2', 'tied , 5 - 5', 'w 2'], ['nebraska', 'mu , 126 - 93', 'mu , 70 - 25', 'nu , 56 - 42', 'mu , 14 - 12', 'mu , 3 - 2', 'tied , 5 - 5', 'l 1'], ['saint louis', 'mu , 21 - 19', 'slu , 12 - 10', 'mu , 11 - 7', 'tied , 0 - 0', 'mu , 3 - 2', 'tied , 5 - 5', 'w 2'], ['washington u of stl', 'mu , 71 - 29', 'mu , 42 - 8', 'mu , 29 - 21', 'tied , 0 - 0', 'mu , 5 - 0', 'mu , 8 - 2', 'w 7']] |
mercedes - benz r230 | https://en.wikipedia.org/wiki/Mercedes-Benz_R230 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1867831-3.html.csv | majority | the majority of the sl series cars have peak power at or above 6000 rpm . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'greater_than_eq', 'value': '6000', 'subset': None} | {'func': 'most_greater_eq', 'args': ['all_rows', 'power rpm', '6000'], 'result': True, 'ind': 0, 'tointer': 'for the power rpm records of all rows , most of them are greater than or equal to 6000 .', 'tostr': 'most_greater_eq { all_rows ; power rpm ; 6000 } = true'} | most_greater_eq { all_rows ; power rpm ; 6000 } = true | for the power rpm records of all rows , most of them are greater than or equal to 6000 . | 1 | 1 | {'most_greater_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'power rpm_3': 3, '6000_4': 4} | {'most_greater_eq_0': 'most_greater_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'power rpm_3': 'power rpm', '6000_4': '6000'} | {'most_greater_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'power rpm_3': [0], '6000_4': [0]} | ['model', 'years', 'type / code', 'power rpm', 'torque rpm'] | [['sl 280', '2008 - 2009', 'cc ( cuin ) v6 ( m272 )', '6000', '2500 - 5000'], ['sl 300', '2009 -', 'cc ( cuin ) v6 ( m272 )', '6000', '2500 - 5000'], ['sl 350', '2008 -', 'cc ( cuin ) v6 ( m272 )', '6500', '6500'], ['sl 500 , sl 550', '2006 -', 'cc ( cuin ) v8 ( m273 )', '6000', '2800 - 4800'], ['sl 63 amg', '2008 -', 'cc ( cuin ) v8 ( m156 .984 )', '6800', '5250'], ['sl 600', '2006 - 2009', 'cc ( cuin ) v12 biturbo ( m275 )', '5000', '1900 - 3500'], ['sl 65 amg', '2004 -', 'cc ( cuin ) v12 biturbo ( m275 amg )', '4800 - 5100', '2000 - 4000'], ['sl 65 amg black series', '2008 -', 'cc ( cuin ) v12 biturbo ( m275 amg )', '5400', '2200 - 4200']] |
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