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2008 - 09 belgian first division | https://en.wikipedia.org/wiki/2008%E2%80%9309_Belgian_First_Division | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17260623-1.html.csv | aggregation | the average stadium capacity for clubs in the belgian first division is 15684 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '15684', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'capacity'], 'result': '15684', 'ind': 0, 'tostr': 'avg { all_rows ; capacity }'}, '15684'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; capacity } ; 15684 } = true', 'tointer': 'the average of the capacity record of all rows is 15684 .'} | round_eq { avg { all_rows ; capacity } ; 15684 } = true | the average of the capacity record of all rows is 15684 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'capacity_4': 4, '15684_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'capacity_4': 'capacity', '15684_5': '15684'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'capacity_4': [0], '15684_5': [1]} | ['club', 'location', 'current manager', 'team captain', 'stadium', 'capacity'] | [['standard liège', 'liège', 'lászló bölöni', 'steven defour', 'stade maurice dufrasne', '30000'], ['rsc anderlecht', 'anderlecht', 'ariel jacobs', 'olivier deschacht', 'constant vanden stock stadium', '28063'], ['club brugge kv', 'bruges', 'jacky mathijssen', 'philippe clement', 'jan breydel stadium', '29415'], ['cercle brugge ksv', 'bruges', 'glen de boeck', 'denis viane', 'jan breydel stadium', '29415'], ['kfc germinal beerschot', 'antwerp', 'aimé anthuenis', 'daniel cruz', 'olympisch stadion', '12148'], ['kaa gent', 'ghent', "michel preud ' homme", 'bryan ruiz', 'jules ottenstadion', '12919'], ['sv zulte waregem', 'waregem', 'francky dury', 'ludwin van nieuwenhuyze', 'regenboogstadion', '8500'], ['r charleroi sc', 'charleroi', 'john collins', 'frank defays', 'stade du pays de charleroi', '25000'], ['kvc westerlo', 'westerlo', 'jan ceulemans', 'jef delen', 'het kuipje', '8200'], ['krc genk', 'genk', 'pierre denier and hans visser ( caretakers )', 'hans cornelis', 'cristal arena', '24900'], ['re mouscron', 'mouscron', 'enzo scifo', 'gonzague van dooren', 'stade le canonnier', '11500'], ['ksc lokeren oost - vlaanderen', 'lokeren', 'aleksandar janković', 'olivier doll', 'daknamstadion', '10000'], ['kv mechelen', 'mechelen', 'peter maes', 'jonas ivens', 'veolia - stadion', '14145'], ['ksv roeselare', 'roeselare', 'dennis van wijk', 'stefaan tanghe', 'schiervelde stadion', '9036'], ['fc verbroedering dender eh', 'denderleeuw', 'johan boskamp', 'steven de petter', 'florent beeckmanstadion', '6800'], ['raec mons', 'mons', 'christophe dessy ( caretaker )', 'roberto mirri', 'stade charles tondreau', '9504'], ['kv kortrijk', 'kortrijk', 'hein vanhaezebrouck', 'stéphane demets', 'guldensporen stadion', '8770'], ['afc tubize', 'tubize', 'albert cartier', 'gregory neels', 'stade leburton', '4000']] |
list of the busiest airports in brazil | https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_Brazil | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15494883-26.html.csv | aggregation | the top 15 busiest airports in brazil in 2004 averaged 4,773,780 total passengers . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '4773780', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'total passengers'], 'result': '4773780', 'ind': 0, 'tostr': 'avg { all_rows ; total passengers }'}, '4773780'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; total passengers } ; 4773780 } = true', 'tointer': 'the average of the total passengers record of all rows is 4773780 .'} | round_eq { avg { all_rows ; total passengers } ; 4773780 } = true | the average of the total passengers record of all rows is 4773780 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'total passengers_4': 4, '4773780_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'total passengers_4': 'total passengers', '4773780_5': '4773780'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'total passengers_4': [0], '4773780_5': [1]} | ['rank', 'location', 'total passengers', 'annual change', 'capacity in use'] | [['1', 'são paulo', '13611227', '12.8 %', '113.4 %'], ['2', 'são paulo', '12940193', '11.7 %', '78.4 %'], ['3', 'brasília', '9926786', '45.1 %', '134.1 %'], ['4', 'rio de janeiro', '6024930', '30.4 %', '40.2 %'], ['5', 'rio de janeiro', '4887306', '9.2 %', '152.7 %'], ['6', 'salvador', '4145371', '20.0 %', '69.1 %'], ['7', 'porto alegre', '3215545', '11.6 %', '52.7 %'], ['8', 'belo horizonte', '3194715', '7.5 %', '213.0 %'], ['9', 'recife', '3173672', '16.1 %', '63.5 %'], ['10', 'curitiba', '2840349', '13.0 %', '81.2 %'], ['11', 'fortaleza', '2317869', '24.0 %', '77.3 %'], ['12', 'florianópolis', '1382577', '7.8 %', '125.7 %'], ['13', 'manaus', '1368968', '10.3 %', '75.4 %'], ['14', 'belém', '1330965', '13.5 %', '49.3 %'], ['15', 'vitória', '1246222', '6.1 %', '222.5 %']] |
2008 - 09 phoenix suns season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Phoenix_Suns_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17340355-5.html.csv | majority | during november of the 2008 - 09 season , steve nash had the highest assist total in the majority of games for the phoenix suns . | {'scope': 'all', 'col': '7', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'steve nash', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'high assists', 'steve nash'], 'result': True, 'ind': 0, 'tointer': 'for the high assists records of all rows , most of them fuzzily match to steve nash .', 'tostr': 'most_eq { all_rows ; high assists ; steve nash } = true'} | most_eq { all_rows ; high assists ; steve nash } = true | for the high assists records of all rows , most of them fuzzily match to steve nash . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'high assists_3': 3, 'steve nash_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'high assists_3': 'high assists', 'steve nash_4': 'steve nash'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'high assists_3': [0], 'steve nash_4': [0]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['3', 'november 1', 'portland', 'w 107 - 96 ( ot )', "amar ' e stoudemire ( 23 )", "amar ' e stoudemire ( 13 )", 'steve nash ( 7 )', 'us airways center 18422', '2 - 1'], ['4', 'november 4', 'new jersey', 'w 114 - 86 ( ot )', 'raja bell ( 22 )', 'matt barnes ( 7 )', 'steve nash ( 11 )', 'izod center 15230', '3 - 1'], ['5', 'november 5', 'indiana', 'w 113 - 103 ( ot )', "amar ' e stoudemire ( 49 )", "amar ' e stoudemire ( 11 )", "amar ' e stoudemire , steve nash ( 6 )", 'conseco fieldhouse 11660', '4 - 1'], ['6', 'november 7', 'chicago', 'l 83 - 100 ( ot )', "amar ' e stoudemire ( 26 )", "robin lopez , amar ' e stoudemire ( 7 )", 'steve nash ( 5 )', 'united center 21967', '4 - 2'], ['7', 'november 8', 'milwaukee', 'w 104 - 96 ( ot )', "shaquille o'neal ( 29 )", "shaquille o'neal , grant hill ( 11 )", 'steve nash ( 7 )', 'bradley center 17935', '5 - 2'], ['8', 'november 10', 'memphis', 'w 107 - 102 ( ot )', 'leandro barbosa ( 27 )', 'matt barnes ( 8 )', 'steve nash ( 6 )', 'us airways center 18422', '6 - 2'], ['9', 'november 12', 'houston', 'l 82 - 94 ( ot )', "leandro barbosa , shaquille o'neal ( 18 )", "shaquille o'neal ( 13 )", "shaquille o'neal , steve nash ( 3 )", 'us airways center 18422', '6 - 3'], ['10', 'november 14', 'sacramento', 'w 97 - 95 ( ot )', "shaquille o'neal ( 29 )", "shaquille o'neal ( 13 )", "shaquille o'neal ( 6 )", 'arco arena 12810', '7 - 3'], ['11', 'november 16', 'detroit', 'w 104 - 86 ( ot )', "amar ' e stoudemire ( 29 )", "amar ' e stoudemire ( 11 )", 'steve nash ( 7 )', 'us airways center 18422', '8 - 3'], ['12', 'november 17', 'utah', 'l 97 - 109 ( ot )', "amar ' e stoudemire ( 30 )", "amar ' e stoudemire ( 8 )", 'steve nash ( 8 )', 'energysolutions arena 19911', '8 - 4'], ['13', 'november 20', 'la lakers', 'l 92 - 105 ( ot )', "amar ' e stoudemire ( 21 )", "shaquille o'neal ( 9 )", 'steve nash ( 10 )', 'us airways center 18422', '8 - 5'], ['14', 'november 22', 'portland', 'w 102 - 92 ( ot )', "shaquille o'neal ( 19 )", "shaquille o'neal ( 17 )", 'steve nash ( 7 )', 'us airways center 18422', '9 - 5'], ['15', 'november 25', 'oklahoma city', 'w 99 - 98 ( ot )', "amar ' e stoudemire ( 22 )", 'steve nash ( 8 )', 'steve nash ( 15 )', 'ford center 19136', '10 - 5'], ['16', 'november 26', 'minnesota', 'w 110 - 102 ( ot )', 'steve nash ( 20 )', "shaquille o'neal ( 10 )", 'steve nash ( 6 )', 'target center 11708', '11 - 5'], ['17', 'november 28', 'miami', 'l 92 - 107 ( ot )', 'leandro barbosa ( 20 )', "shaquille o'neal ( 9 )", 'leandro barbosa ( 5 )', 'us airways center 18422', '11 - 6'], ['18', 'november 30', 'new jersey', 'l 109 - 117 ( ot )', 'steve nash ( 26 )', "amar ' e stoudemire ( 12 )", 'steve nash ( 9 )', 'us airways center 18422', '11 - 7']] |
list of tennis stadiums by capacity | https://en.wikipedia.org/wiki/List_of_tennis_stadiums_by_capacity | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14476860-3.html.csv | unique | only one of the world 's largest tennis stadiums by capacity is in italy . | {'scope': 'all', 'row': '13', 'col': '5', 'col_other': 'n/a', 'criterion': 'equal', 'value': 'italy', 'subset': None} | {'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'italy'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to italy .', 'tostr': 'filter_eq { all_rows ; country ; italy }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; country ; italy } } = true', 'tointer': 'select the rows whose country record fuzzily matches to italy . there is only one such row in the table .'} | only { filter_eq { all_rows ; country ; italy } } = true | select the rows whose country record fuzzily matches to italy . there is only one such row in the table . | 2 | 2 | {'only_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'country_4': 4, 'italy_5': 5} | {'only_1': 'only', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'country_4': 'country', 'italy_5': 'italy'} | {'only_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'country_4': [0], 'italy_5': [0]} | ['rank', 'stadium', 'capacity', 'city', 'country'] | [['1', 'queensland sport and athletics centre', '49000', 'brisbane', 'australia'], ['2', 'estadio olímpico de sevilla', '27200', 'seville', 'spain'], ['3', 'belgrade arena', '23000', 'belgrade', 'serbia'], ['4', 'las ventas', '21000', 'madrid', 'spain'], ['5', 'royal dublin society', '6000', 'dublin', 'ireland'], ['6', 'o2 arena ( prague )', '17000', 'prague', 'czech republic'], ['7', 'palau sant jordi', '16500', 'barcelona', 'spain'], ['8', 'estadio mary terán de weiss', '14510', 'buenos aires', 'argentina'], ['9', 'palacio de deportes de santander', '14000', 'santander', 'spain'], ['10', 'memorial coliseum', '12000', 'portland , oregon', 'united states'], ['11', 'nokia arena', '11700', 'tel aviv', 'israel'], ['12', 'public auditorium', '11500', 'cleveland', 'united states'], ['13', 'mediolanum forum', '11200', 'milan', 'italy'], ['14', 'bill graham civic auditorium', '7000', 'san francisco', 'united states'], ['15', 'sibamac arena', '4500', 'bratislava', 'slovakia'], ['16', 'idrottens hus', '2400', 'helsingborg', 'sweden']] |
united states house of representatives elections , 2000 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_2000 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341423-40.html.csv | unique | of the ones with the party republican , the only one with the results of retired republican hold had the incumbent of mark sanford . | {'scope': 'subset', 'row': '1', 'col': '5', 'col_other': '2,3', 'criterion': 'equal', 'value': 'retired republican hold', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'republican'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'party', 'republican'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; party ; republican }', 'tointer': 'select the rows whose party record fuzzily matches to republican .'}, 'results', 'retired republican hold'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose party record fuzzily matches to republican . among these rows , select the rows whose results record fuzzily matches to retired republican hold .', 'tostr': 'filter_eq { filter_eq { all_rows ; party ; republican } ; results ; retired republican hold }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; party ; republican } ; results ; retired republican hold } }', 'tointer': 'select the rows whose party record fuzzily matches to republican . among these rows , select the rows whose results record fuzzily matches to retired republican hold . 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', 'party', 'republican'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; party ; republican }', 'tointer': 'select the rows whose party record fuzzily matches to republican .'}, 'results', 'retired republican hold'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose party record fuzzily matches to republican . among these rows , select the rows whose results record fuzzily matches to retired republican hold .', 'tostr': 'filter_eq { filter_eq { all_rows ; party ; republican } ; results ; retired republican hold }'}, 'incumbent'], 'result': 'mark sanford', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; party ; republican } ; results ; retired republican hold } ; incumbent }'}, 'mark sanford'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; party ; republican } ; results ; retired republican hold } ; incumbent } ; mark sanford }', 'tointer': 'the incumbent record of this unqiue row is mark sanford .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; party ; republican } ; results ; retired republican hold } } ; eq { hop { filter_eq { filter_eq { all_rows ; party ; republican } ; results ; retired republican hold } ; incumbent } ; mark sanford } } = true', 'tointer': 'select the rows whose party record fuzzily matches to republican . among these rows , select the rows whose results record fuzzily matches to retired republican hold . there is only one such row in the table . the incumbent record of this unqiue row is mark sanford .'} | and { only { filter_eq { filter_eq { all_rows ; party ; republican } ; results ; retired republican hold } } ; eq { hop { filter_eq { filter_eq { all_rows ; party ; republican } ; results ; retired republican hold } ; incumbent } ; mark sanford } } = true | select the rows whose party record fuzzily matches to republican . among these rows , select the rows whose results record fuzzily matches to retired republican hold . there is only one such row in the table . the incumbent record of this unqiue row is mark sanford . | 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, 'party_8': 8, 'republican_9': 9, 'results_10': 10, 'retired republican hold_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'incumbent_12': 12, 'mark sanford_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', 'party_8': 'party', 'republican_9': 'republican', 'results_10': 'results', 'retired republican hold_11': 'retired republican hold', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'incumbent_12': 'incumbent', 'mark sanford_13': 'mark sanford'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'party_8': [0], 'republican_9': [0], 'results_10': [1], 'retired republican hold_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'incumbent_12': [3], 'mark sanford_13': [4]} | ['district', 'incumbent', 'party', 'first elected', 'results', 'candidates'] | [['south carolina 1', 'mark sanford', 'republican', '1994', 'retired republican hold', 'henry brown ( r ) 60 % andy brack ( d ) 36 %'], ['south carolina 2', 'floyd spence', 'republican', '1970', 're - elected', 'floyd spence ( r ) 58 % jane frederick ( d ) 41 %'], ['south carolina 3', 'lindsey graham', 'republican', '1994', 're - elected', 'lindsey graham ( r ) 68 % george brightharp ( d ) 31 %'], ['south carolina 4', 'jim demint', 'republican', '1998', 're - elected', 'jim demint ( r ) 80 %'], ['south carolina 5', 'john spratt', 'democratic', '1982', 're - elected', 'john spratt ( d ) 59 % carl gullick ( r ) 40 %']] |
united states house of representatives elections , 1964 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1964 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341865-11.html.csv | count | 2 of the elections were first elected before the year 1950 . | {'scope': 'all', 'criterion': 'less_than', 'value': '1950', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'first elected', '1950'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose first elected record is less than 1950 .', 'tostr': 'filter_less { all_rows ; first elected ; 1950 }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_less { all_rows ; first elected ; 1950 } }', 'tointer': 'select the rows whose first elected record is less than 1950 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_less { all_rows ; first elected ; 1950 } } ; 2 } = true', 'tointer': 'select the rows whose first elected record is less than 1950 . the number of such rows is 2 .'} | eq { count { filter_less { all_rows ; first elected ; 1950 } } ; 2 } = true | select the rows whose first elected record is less than 1950 . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_less_0': 0, 'all_rows_4': 4, 'first elected_5': 5, '1950_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_less_0': 'filter_less', 'all_rows_4': 'all_rows', 'first elected_5': 'first elected', '1950_6': '1950', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_less_0': [1], 'all_rows_4': [0], 'first elected_5': [0], '1950_6': [0], '2_7': [2]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['florida 1', 'robert l f sikes', 'democratic', '1940', 're - elected', 'robert l f sikes ( d ) unopposed'], ['florida 3', 'claude pepper', 'democratic', '1962', 're - elected', "claude pepper ( d ) 65.7 % paul j o'neill ( r ) 34.3 %"], ['florida 4', 'dante fascell', 'democratic', '1954', 're - elected', 'dante fascell ( d ) 63.9 % jay mcglon ( r ) 36.1 %'], ['florida 5', 'albert s herlong , jr', 'democratic', '1948', 're - elected', 'albert s herlong , jr ( d ) unopposed'], ['florida 6', 'paul rogers', 'democratic', '1954', 're - elected', 'paul rogers ( d ) 66.0 % john d steele ( r ) 34.0 %'], ['florida 7', 'james a haley', 'democratic', '1952', 're - elected', 'james a haley ( d ) unopposed'], ['florida 8', 'donald ray matthews', 'democratic', '1952', 're - elected', 'donald ray matthews ( d ) unopposed'], ['florida 9', 'don fuqua', 'democratic', '1962', 're - elected', 'don fuqua ( d ) unopposed']] |
2003 games of the small states of europe | https://en.wikipedia.org/wiki/2003_Games_of_the_Small_States_of_Europe | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11316160-1.html.csv | ordinal | cyprus had the second highest number of silver medals in the 2003 games of the small states of europe . | {'row': '1', '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 }'}, 'nation'], 'result': 'cyprus', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; silver ; 2 } ; nation }'}, 'cyprus'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; silver ; 2 } ; nation } ; cyprus } = true', 'tointer': 'select the row whose silver record of all rows is 2nd maximum . the nation record of this row is cyprus .'} | eq { hop { nth_argmax { all_rows ; silver ; 2 } ; nation } ; cyprus } = true | select the row whose silver record of all rows is 2nd maximum . the nation record of this row is cyprus . | 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, 'nation_7': 7, 'cyprus_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', 'nation_7': 'nation', 'cyprus_8': 'cyprus'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'silver_5': [0], '2_6': [0], 'nation_7': [1], 'cyprus_8': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'cyprus', '34', '20', '27', '81'], ['2', 'luxembourg', '21', '17', '15', '53'], ['3', 'iceland', '20', '24', '23', '67'], ['4', 'malta', '11', '18', '15', '44'], ['5', 'monaco', '7', '7', '10', '24'], ['6', 'san marino', '6', '10', '9', '25'], ['7', 'andorra', '4', '6', '8', '18'], ['8', 'liechtenstein', '2', '1', '2', '5']] |
2008 indian premier league | https://en.wikipedia.org/wiki/2008_Indian_Premier_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15734036-10.html.csv | aggregation | the total number of runs for players in the 2008 indian premier league is 1974 . | {'scope': 'all', 'col': '4', 'type': 'sum', 'result': '1974', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'runs'], 'result': '1974', 'ind': 0, 'tostr': 'sum { all_rows ; runs }'}, '1974'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; runs } ; 1974 } = true', 'tointer': 'the sum of the runs record of all rows is 1974 .'} | round_eq { sum { all_rows ; runs } ; 1974 } = true | the sum of the runs record of all rows is 1974 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'runs_4': 4, '1974_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'runs_4': 'runs', '1974_5': '1974'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'runs_4': [0], '1974_5': [1]} | ['player', 'team', 'inns', 'runs', 'balls'] | [['virender sehwag', 'delhi daredevils', '14', '406', '220'], ['yusuf pathan', 'rajasthan royals', '15', '435', '243'], ['sanath jayasuriya', 'mumbai indians', '14', '514', '309'], ['yuvraj singh', 'kings xi punjab', '14', '299', '184'], ['kumar sangakkara', 'kings xi punjab', '9', '320', '198']] |
2009 deutsche tourenwagen masters season | https://en.wikipedia.org/wiki/2009_Deutsche_Tourenwagen_Masters_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21321935-2.html.csv | ordinal | the second game of the deutch masters season in 2009 was played on may 31 . | {'row': '2', 'col': '3', 'order': '2', 'col_other': 'n/a', 'max_or_min': 'min_to_max', 'value_mentioned': 'yes', 'scope': 'all', 'subset': None} | {'func': 'eq', 'args': [{'func': 'nth_min', 'args': ['all_rows', 'date', '2'], 'result': '31 may', 'ind': 0, 'tostr': 'nth_min { all_rows ; date ; 2 }', 'tointer': 'the 2nd minimum date record of all rows is 31 may .'}, '31 may'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_min { all_rows ; date ; 2 } ; 31 may } = true', 'tointer': 'the 2nd minimum date record of all rows is 31 may .'} | eq { nth_min { all_rows ; date ; 2 } ; 31 may } = true | the 2nd minimum date record of all rows is 31 may . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'nth_min_0': 0, 'all_rows_3': 3, 'date_4': 4, '2_5': 5, '31 may_6': 6} | {'eq_1': 'eq', 'result_2': 'true', 'nth_min_0': 'nth_min', 'all_rows_3': 'all_rows', 'date_4': 'date', '2_5': '2', '31 may_6': '31 may'} | {'eq_1': [2], 'result_2': [], 'nth_min_0': [1], 'all_rows_3': [0], 'date_4': [0], '2_5': [0], '31 may_6': [1]} | ['round', 'circuit', 'date', 'pole position', 'fastest lap', 'winning driver', 'winning team'] | [['1', 'hockenheimring', '17 may', 'mattias ekström', 'mattias ekström', 'tom kristensen', 'abt sportsline'], ['2', 'eurospeedway lausitz', '31 may', 'mattias ekström', 'jamie green', 'gary paffett', 'hwa team'], ['3', 'norisring , nuremberg', '28 june', 'timo scheider', 'katherine legge', 'jamie green', 'persson motorsport'], ['4', 'circuit park zandvoort', '19 july', 'oliver jarvis', 'mattias ekström', 'gary paffett', 'hwa team'], ['5', 'motorsport arena oschersleben', '2 august', 'tom kristensen', 'timo scheider', 'timo scheider', 'abt sportsline'], ['6', 'nürburgring', '16 august', 'martin tomczyk', 'mattias ekström', 'martin tomczyk', 'abt sportsline'], ['7', 'brands hatch , kent', '6 september', 'paul di resta', 'paul di resta', 'paul di resta', 'hwa team'], ['8', 'circuit de catalunya , barcelona', '20 september', 'tom kristensen', 'timo scheider', 'timo scheider', 'abt sportsline'], ['9', 'dijon - prenois', '11 october', 'bruno spengler', 'paul di resta', 'gary paffett', 'hwa team']] |
2009 isle of man tt | https://en.wikipedia.org/wiki/2009_Isle_of_Man_TT | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21607058-1.html.csv | superlative | cameron donald 1000cc suzuki is the rider that recorded the fastest speed on thursday , june 4th of the 2009 isle of man tt . | {'scope': 'all', 'col_superlative': '6', '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', 'thurs 4 june'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; thurs 4 june }'}, 'rider'], 'result': 'cameron donald 1000cc suzuki', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; thurs 4 june } ; rider }'}, 'cameron donald 1000cc suzuki'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; thurs 4 june } ; rider } ; cameron donald 1000cc suzuki } = true', 'tointer': 'select the row whose thurs 4 june record of all rows is minimum . the rider record of this row is cameron donald 1000cc suzuki .'} | eq { hop { argmin { all_rows ; thurs 4 june } ; rider } ; cameron donald 1000cc suzuki } = true | select the row whose thurs 4 june record of all rows is minimum . the rider record of this row is cameron donald 1000cc suzuki . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'thurs 4 june_5': 5, 'rider_6': 6, 'cameron donald 1000cc suzuki_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'thurs 4 june_5': 'thurs 4 june', 'rider_6': 'rider', 'cameron donald 1000cc suzuki_7': 'cameron donald 1000cc suzuki'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'thurs 4 june_5': [0], 'rider_6': [1], 'cameron donald 1000cc suzuki_7': [2]} | ['rank', 'rider', 'mon 1 june', 'tue 2 june', 'wed 3 june', 'thurs 4 june', 'fri 5 june'] | [['1', 'cameron donald 1000cc suzuki', "18 ' 16.16 123.912 mph", "18 ' 15.21 124.020 mph", "19 ' 31.12 115.981 mph", "17 ' 13.25 131.457 mph", '-- no time'], ['2', 'john mcguinness 1000cc honda', "17 ' 40.60 128.067 mph", "17 ' 27.56 129.661 mph", "17 ' 23.46 130.171 mph", "17 ' 52.90 126.599 mph", '-- no time'], ['3', 'bruce anstey 1000cc suzuki', "18 ' 29.06 122.471 mph", "17 ' 53.22 126.561 mph", "17 ' 42.12 127.884 mph", "17 ' 23.79 130.129 mph", '-- no time'], ['4', 'guy martin 1000cc honda', "18 ' 02.89 125.431 mph", "17 ' 38.72 128.294 mph", "17 ' 31.56 129.168 mph", "17 ' 33.86 128.886 mph", "17 ' 32.83 129.013 mph"], ['5', 'ian hutchinson 1000cc honda', '-- no time', "17 ' 47.24 127.271 mph", "17 ' 41.46 127.963 mph", "17 ' 32.71 129.027 mph", "17 ' 46.76 127.328 mph"], ['6', 'steve plater 1000cc honda', "18 ' 14.54 124.096 mph", '-- no time', "17 ' 37.42 128.453 mph", "17 ' 33.08 128.982 mph", '-- no time'], ['7', 'conor cummins 1000cc kawasaki', "17 ' 56.57 121.167 mph", '-- no time', "18 ' 10.12 124.599 mph", "17 ' 38.40 128.334 mph", "17 ' 44.59 127.588 mph"], ['8', 'gary johnson 1000cc honda', "18 ' 05.00 125.187 mph", "18 ' 09.82 124.633 mph", "18 ' 15.02 124.042 mph", "17 ' 45.04 127.533 mph", "17 ' 42.87 127.794 mph"], ['9', 'adrian archibald 1000cc suzuki', "17 ' 51.68 126.743 mph", "17 ' 51.15 126.806 mph", "19 ' 04.43 118.687 mph", "17 ' 43.13 127.762 mph", '-- no time'], ['10', 'ian lougher 1000cc yamaha', "18 ' 37.37 121.561 mph", "18 ' 09.20 124.704 mph", "17 ' 50.99 126.825 mph", "17 ' 44.25 127.628 mph", "18 ' 04.02 125.301 mph"], ['11', 'keith amor 1000cc honda', "18 ' 28.55 122.528 mph", "17 ' 58.65 125.924 mph", "17 ' 59.88 125.781 mph", "17 ' 44.48 127.600 mph", "17 ' 55.50 126.293 mph"], ['12', 'ryan farquhar 1000cc kawasaki', "18 ' 11.52 124.440 mph", "18 ' 36.60 121.644 mph", "18 ' 19.47 123.539 mph", "17 ' 58.92 125.893 mph", "17 ' 51.75 126.734 mph"], ['13', 'carl rennie 1000cc honda', "18 ' 30.86 122.273 mph", '-- no time', "18 ' 10.03 124.609 mph", "17 ' 55.85 126.252 mph", "17 ' 53.08 126.578 mph"], ['14', 'michael dunlop 1000cc yamaha', '-- no time', '-- no time', "18 ' 33.73 121.958 mph", "18 ' 22.81 123.165 mph", "18 ' 00.16 125.749 mph"], ['15', 'michael rutter 1000cc suzuki', '-- no time', "18 ' 13.25 124.243 mph", "18 ' 06.24 125.044 mph", "18 ' 02.20 125.511 mph", '-- no time'], ['16', 'dan stewart 1000cc honda', '-- no time', "18 ' 00.57 125.701 mph", "18 ' 12.45 124.333 mph", "18 ' 04.63 125.229 mph", "18 ' 06.61 125.002 mph"], ['17', 'mark miller 1000cc suzuki', "18 ' 56.90 119.743 mph", "18 ' 53.13 119.869 mph", "18 ' 40.52 121.219 mph", "18 ' 14.00 124.157 mph", '-- no time'], ['18', 'mark parrett 1000cc yamaha', "18 ' 45.30 120.704 mph", "18 ' 42.14 121.040 mph", "18 ' 30.84 122.275 mph", "18 ' 15.96 123.935 mph", '-- no time'], ['19', 'john burrows 1000cc suzuki', '-- no time', "18 ' 27.93 122.596 mph", "18 ' 22.11 123.244 mph", "18 ' 19.37 123.551 mph", "18 ' 16.78 123.843 mph"]] |
peruvian segunda división | https://en.wikipedia.org/wiki/Peruvian_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12335018-1.html.csv | unique | deportivo coopsol is the only team in the pervian segunda division to have only 1 top division title . | {'scope': 'all', 'row': '6', 'col': '9', 'col_other': '1', 'criterion': 'equal', 'value': '1', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'top division titles', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose top division titles record is equal to 1 .', 'tostr': 'filter_eq { all_rows ; top division titles ; 1 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; top division titles ; 1 } }', 'tointer': 'select the rows whose top division titles record is equal to 1 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'top division titles', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose top division titles record is equal to 1 .', 'tostr': 'filter_eq { all_rows ; top division titles ; 1 }'}, 'team'], 'result': 'deportivo coopsol', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; top division titles ; 1 } ; team }'}, 'deportivo coopsol'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; top division titles ; 1 } ; team } ; deportivo coopsol }', 'tointer': 'the team record of this unqiue row is deportivo coopsol .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; top division titles ; 1 } } ; eq { hop { filter_eq { all_rows ; top division titles ; 1 } ; team } ; deportivo coopsol } } = true', 'tointer': 'select the rows whose top division titles record is equal to 1 . there is only one such row in the table . the team record of this unqiue row is deportivo coopsol .'} | and { only { filter_eq { all_rows ; top division titles ; 1 } } ; eq { hop { filter_eq { all_rows ; top division titles ; 1 } ; team } ; deportivo coopsol } } = true | select the rows whose top division titles record is equal to 1 . there is only one such row in the table . the team record of this unqiue row is deportivo coopsol . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'top division titles_7': 7, '1_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'team_9': 9, 'deportivo coopsol_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'top division titles_7': 'top division titles', '1_8': '1', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'team_9': 'team', 'deportivo coopsol_10': 'deportivo coopsol'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'top division titles_7': [0], '1_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'team_9': [2], 'deportivo coopsol_10': [3]} | ['team', 'city', 'founded', 'first season in segunda división', 'first season of current spell in segunda división', 'stadium', 'capacity', 'field', 'top division titles', 'last top division title'] | [['alfonso ugarte', 'puno', '1928', '2006', '2013', 'enrique torres belón', '20000', 'grass', '0', '-'], ['alianza universidad', 'huánuco', '1939', '2012', '2012', 'heraclio tapia', '15000', 'grass', '0', '-'], ['atlético minero', 'matucana', '1997', '2006', '2009', 'municipal de matucana', '5000', 'grass', '0', '-'], ['atlético torino', 'talara', '1946', '2009', '2009', 'campeonísimo', '8000', 'grass', '0', '-'], ['defensor san alejandro', 'aguaytía', '1969', '2013', '2013', 'aliardo soria pérez', '13000', 'grass', '0', '-'], ['deportivo coopsol', 'chancay', '1964', '1999', '1999', 'rómulo shaw cisneros', '13000', 'grass', '1', '2000'], ['deportivo municipal', 'lima', '1935', '1968', '2013', 'miguel grau', '15000', 'grass', '2', '2006'], ['sport boys', 'callao', '1927', '1988', '2013', 'miguel grau', '15000', 'grass', '2', '2009'], ['sport victoria', 'ica', '1919', '2013', '2013', 'max augustín', '24576', 'grass', '0', '-'], ['sportivo huracán', 'arequipa', '1927', '2013', '2013', 'mariano melgar', '20000', 'grass', '0', '-'], ['walter ormeño', 'cañete', '1950', '1988', '2013', 'oscar ramos cabieses', '8000', 'grass', '0', '-']] |
synchronized swimming at the 2008 summer olympics - women 's duet | https://en.wikipedia.org/wiki/Synchronized_swimming_at_the_2008_Summer_Olympics_%E2%80%93_Women%27s_duet | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18789596-2.html.csv | unique | in the women 's duet in sychronized swimming at the 2008 summer olympics , the only athletes from italy were beatrice adelizzi & giulia lapi . | {'scope': 'all', 'row': '7', 'col': '1', 'col_other': '2', 'criterion': 'equal', 'value': 'italy', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'italy'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to italy .', 'tostr': 'filter_eq { all_rows ; country ; italy }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; country ; italy } }', 'tointer': 'select the rows whose country record fuzzily matches to italy . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'italy'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to italy .', 'tostr': 'filter_eq { all_rows ; country ; italy }'}, 'athlete'], 'result': 'beatrice adelizzi & giulia lapi', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; country ; italy } ; athlete }'}, 'beatrice adelizzi & giulia lapi'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; country ; italy } ; athlete } ; beatrice adelizzi & giulia lapi }', 'tointer': 'the athlete record of this unqiue row is beatrice adelizzi & giulia lapi .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; country ; italy } } ; eq { hop { filter_eq { all_rows ; country ; italy } ; athlete } ; beatrice adelizzi & giulia lapi } } = true', 'tointer': 'select the rows whose country record fuzzily matches to italy . there is only one such row in the table . the athlete record of this unqiue row is beatrice adelizzi & giulia lapi .'} | and { only { filter_eq { all_rows ; country ; italy } } ; eq { hop { filter_eq { all_rows ; country ; italy } ; athlete } ; beatrice adelizzi & giulia lapi } } = true | select the rows whose country record fuzzily matches to italy . there is only one such row in the table . the athlete record of this unqiue row is beatrice adelizzi & giulia lapi . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'country_7': 7, 'italy_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'athlete_9': 9, 'beatrice adelizzi & giulia lapi_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'country_7': 'country', 'italy_8': 'italy', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'athlete_9': 'athlete', 'beatrice adelizzi & giulia lapi_10': 'beatrice adelizzi & giulia lapi'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'country_7': [0], 'italy_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'athlete_9': [2], 'beatrice adelizzi & giulia lapi_10': [3]} | ['country', 'athlete', 'technical', 'free', 'total'] | [['russia', 'anastasia davydova & anastasiya yermakova', '49.334', '49.917', '99.251'], ['spain', 'andrea fuentes & gemma mengual', '48.834', '49.500', '98.334'], ['japan', 'saho harada & emiko suzuki', '48.250', '48.917', '97.167'], ['china', 'jiang tingting & jiang wenwen', '48.084', '48.250', '96.334'], ['united states', 'christina jones & andrea nott', '47.750', '47.750', '95.500'], ['canada', 'marie - pier boudreau gagnon & isabelle rampling', '47.417', '47.667', '95.084'], ['italy', 'beatrice adelizzi & giulia lapi', '46.834', '46.917', '93.751'], ['ukraine', 'darya yushko & kseniya sydorenko', '46.084', '46.584', '92.668'], ['netherlands', 'bianca van der velden & sonja van der velden', '45.584', '46.083', '91.667'], ['greece', 'evanthia makrygianni & despoina solomou', '45.834', '45.667', '91.501'], ['france', 'apolline dreyfuss & lila meesseman - bakir', '44.750', '45.583', '90.333'], ['switzerland', 'magdalena brunner & ariane schneider', '44.250', '45.000', '89.250']] |
list of tallest buildings in indianapolis | https://en.wikipedia.org/wiki/List_of_tallest_buildings_in_Indianapolis | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14565330-3.html.csv | ordinal | of the tallest buildings in indianapolis , the one with the 2nd highest number of floors is aul tower . | {'row': '4', 'col': '5', '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', 'floors', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; floors ; 2 }'}, 'name'], 'result': 'aul tower', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; floors ; 2 } ; name }'}, 'aul tower'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; floors ; 2 } ; name } ; aul tower } = true', 'tointer': 'select the row whose floors record of all rows is 2nd maximum . the name record of this row is aul tower .'} | eq { hop { nth_argmax { all_rows ; floors ; 2 } ; name } ; aul tower } = true | select the row whose floors record of all rows is 2nd maximum . the name record of this row is aul tower . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'floors_5': 5, '2_6': 6, 'name_7': 7, 'aul tower_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', 'floors_5': 'floors', '2_6': '2', 'name_7': 'name', 'aul tower_8': 'aul tower'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'floors_5': [0], '2_6': [0], 'name_7': [1], 'aul tower_8': [2]} | ['name', 'street address', 'years as tallest', 'height ft ( m )', 'floors'] | [['indiana statehouse', '04.0 200 west washington street', '1888 - 1962', '255 ( 78 )', '4'], ['city - county building', '07.0 200 east washington street', '1962 - 1970', '372 ( 113 )', '28'], ['one indiana square', '01.0 1 indiana square', '1970 - 1982', '504 ( 154 )', '36'], ['aul tower', '07.0 200 north illinois street', '1982 - 1990', '533 ( 162 )', '38'], ['bank one tower', '05.0 111 monument circle', '1990 - present', '830 ( 253 )', '48']] |
2010 southeastern conference football season | https://en.wikipedia.org/wiki/2010_Southeastern_Conference_football_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26842217-6.html.csv | unique | of the 2010 southeastern conference football games played in tennessee , only one had an attendance over 100000 . | {'scope': 'subset', 'row': '7', 'col': '8', 'col_other': 'n/a', 'criterion': 'greater_than', 'value': '100000', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'tennessee'}} | {'func': 'only', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home team', 'tennessee'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; home team ; tennessee }', 'tointer': 'select the rows whose home team record fuzzily matches to tennessee .'}, 'attendance', '100000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose home team record fuzzily matches to tennessee . among these rows , select the rows whose attendance record is greater than 100000 .', 'tostr': 'filter_greater { filter_eq { all_rows ; home team ; tennessee } ; attendance ; 100000 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_greater { filter_eq { all_rows ; home team ; tennessee } ; attendance ; 100000 } } = true', 'tointer': 'select the rows whose home team record fuzzily matches to tennessee . among these rows , select the rows whose attendance record is greater than 100000 . there is only one such row in the table .'} | only { filter_greater { filter_eq { all_rows ; home team ; tennessee } ; attendance ; 100000 } } = true | select the rows whose home team record fuzzily matches to tennessee . among these rows , select the rows whose attendance record is greater than 100000 . there is only one such row in the table . | 3 | 3 | {'only_2': 2, 'result_3': 3, 'filter_greater_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'home team_5': 5, 'tennessee_6': 6, 'attendance_7': 7, '100000_8': 8} | {'only_2': 'only', 'result_3': 'true', 'filter_greater_1': 'filter_greater', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'home team_5': 'home team', 'tennessee_6': 'tennessee', 'attendance_7': 'attendance', '100000_8': '100000'} | {'only_2': [3], 'result_3': [], 'filter_greater_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'home team_5': [0], 'tennessee_6': [0], 'attendance_7': [1], '100000_8': [1]} | ['date', 'time', 'visiting team', 'home team', 'site', 'broadcast', 'result', 'attendance'] | [['september 9', '7:30 pm', '21 auburn', 'mississippi state', 'davis wade stadium starkville , ms', 'espn', 'aub 17 - 14', '54806'], ['september 11', '12:00 pm', '22 georgia', '24 south carolina', 'williams - brice stadium columbia , sc', 'espn', 'usc 17 - 6', '80974'], ['september 11', '12:21 pm', 'south florida', '8 florida', 'ben hill griffin stadium gainesville , fl', 'sec network', 'w 38 - 14', '90612'], ['september 11', '7:00 pm', '19 lsu', 'vanderbilt', 'vanderbilt stadium nashville , tn', 'espnu', 'lsu 27 - 3', '36940'], ['september 11', '7:00 pm', '19 penn state', '1 alabama', 'bryant - denny stadium tuscaloosa , al', 'espn', 'w 24 - 3', '101821'], ['september 11', '7:00 pm', 'louisiana - monroe', '14 arkansas', 'war memorial stadium little rock , ar', 'fsn', 'w 31 - 7', '55705'], ['september 11', '7:00 pm', '7 oregon', 'tennessee', 'neyland stadium knoxville , tn', 'espn2', 'l 48 - 13', '102035'], ['september 11', '7:30 pm', 'western kentucky', 'kentucky', 'commonwealth stadium lexington , ky', 'css', 'w 63 - 28', '66584']] |
1989 detroit lions season | https://en.wikipedia.org/wiki/1989_Detroit_Lions_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15916193-2.html.csv | count | in the 1989 detroit lions season , among the games played in december , 3 of them drew more than 10,000 people . | {'scope': 'subset', 'criterion': 'greater_than', 'value': '10000', 'result': '3', 'col': '5', 'subset': {'col': '2', 'criterion': 'greater_than_eq', 'value': 'december 3 , 1989'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_greater_eq', 'args': ['all_rows', 'date', 'december 3 , 1989'], 'result': None, 'ind': 0, 'tostr': 'filter_greater_eq { all_rows ; date ; december 3 , 1989 }', 'tointer': 'select the rows whose date record is greater than or equal to december 3 , 1989 .'}, 'attendance', '10000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record is greater than or equal to december 3 , 1989 . among these rows , select the rows whose attendance record is greater than 10000 .', 'tostr': 'filter_greater { filter_greater_eq { all_rows ; date ; december 3 , 1989 } ; attendance ; 10000 }'}], 'result': '3', 'ind': 2, 'tostr': 'count { filter_greater { filter_greater_eq { all_rows ; date ; december 3 , 1989 } ; attendance ; 10000 } }', 'tointer': 'select the rows whose date record is greater than or equal to december 3 , 1989 . among these rows , select the rows whose attendance record is greater than 10000 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_greater { filter_greater_eq { all_rows ; date ; december 3 , 1989 } ; attendance ; 10000 } } ; 3 } = true', 'tointer': 'select the rows whose date record is greater than or equal to december 3 , 1989 . among these rows , select the rows whose attendance record is greater than 10000 . the number of such rows is 3 .'} | eq { count { filter_greater { filter_greater_eq { all_rows ; date ; december 3 , 1989 } ; attendance ; 10000 } } ; 3 } = true | select the rows whose date record is greater than or equal to december 3 , 1989 . among these rows , select the rows whose attendance record is greater than 10000 . the number of such rows is 3 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_greater_1': 1, 'filter_greater_eq_0': 0, 'all_rows_5': 5, 'date_6': 6, 'december 3 , 1989_7': 7, 'attendance_8': 8, '10000_9': 9, '3_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_greater_1': 'filter_greater', 'filter_greater_eq_0': 'filter_greater_eq', 'all_rows_5': 'all_rows', 'date_6': 'date', 'december 3 , 1989_7': 'december 3 , 1989', 'attendance_8': 'attendance', '10000_9': '10000', '3_10': '3'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_greater_1': [2], 'filter_greater_eq_0': [1], 'all_rows_5': [0], 'date_6': [0], 'december 3 , 1989_7': [0], 'attendance_8': [1], '10000_9': [1], '3_10': [3]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 10 , 1989', 'phoenix cardinals', 'l 16 - 13', '36735'], ['2', 'september 17 , 1989', 'new york giants', 'l 24 - 14', '76021'], ['3', 'september 24 , 1989', 'chicago bears', 'l 47 - 27', '71418'], ['4', 'october 1 , 1989', 'pittsburgh steelers', 'l 23 - 3', '43804'], ['5', 'october 8 , 1989', 'minnesota vikings', 'l 24 - 17', '55380'], ['6', 'october 15 , 1989', 'tampa bay buccaneers', 'w 17 - 16', '46225'], ['7', 'october 22 , 1989', 'minnesota vikings', 'l 20 - 7', '51579'], ['8', 'october 29 , 1989', 'green bay packers', 'l 23 - 20 ot', '53731'], ['9', 'november 5 , 1989', 'houston oilers', 'l 35 - 31', '48056'], ['10', 'november 12 , 1989', 'green bay packers', 'w 31 - 22', '44324'], ['11', 'november 19 , 1989', 'cincinnati bengals', 'l 42 - 7', '55720'], ['12', 'november 23 , 1989', 'cleveland browns', 'w 13 - 10', '65624'], ['13', 'december 3 , 1989', 'new orleans saints', 'w 21 - 14', '38550'], ['14', 'december 10 , 1989', 'chicago bears', 'w 27 - 17', '52650'], ['15', 'december 17 , 1989', 'tampa bay buccaneers', 'w 33 - 7', '40362'], ['16', 'december 24 , 1989', 'atlanta falcons', 'w 31 - 24', '7792']] |
united states house of representatives elections , 1926 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1926 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342379-10.html.csv | unique | gordon lee was the only georgia incumbent who retired in the 1926 united states house of representatives elections . | {'scope': 'all', 'row': '7', 'col': '5', 'col_other': '2', 'criterion': 'fuzzily_match', 'value': 'retired', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'retired'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to retired .', 'tostr': 'filter_eq { all_rows ; result ; retired }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; result ; retired } }', 'tointer': 'select the rows whose result record fuzzily matches to retired . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'retired'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to retired .', 'tostr': 'filter_eq { all_rows ; result ; retired }'}, 'incumbent'], 'result': 'gordon lee', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; result ; retired } ; incumbent }'}, 'gordon lee'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; result ; retired } ; incumbent } ; gordon lee }', 'tointer': 'the incumbent record of this unqiue row is gordon lee .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; result ; retired } } ; eq { hop { filter_eq { all_rows ; result ; retired } ; incumbent } ; gordon lee } } = true', 'tointer': 'select the rows whose result record fuzzily matches to retired . there is only one such row in the table . the incumbent record of this unqiue row is gordon lee .'} | and { only { filter_eq { all_rows ; result ; retired } } ; eq { hop { filter_eq { all_rows ; result ; retired } ; incumbent } ; gordon lee } } = true | select the rows whose result record fuzzily matches to retired . there is only one such row in the table . the incumbent record of this unqiue row is gordon lee . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'result_7': 7, 'retired_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'incumbent_9': 9, 'gordon lee_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'result_7': 'result', 'retired_8': 'retired', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'incumbent_9': 'incumbent', 'gordon lee_10': 'gordon lee'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'result_7': [0], 'retired_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'incumbent_9': [2], 'gordon lee_10': [3]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['georgia 1', 'charles gordon edwards', 'democratic', '1924', 're - elected', 'charles gordon edwards ( d ) unopposed'], ['georgia 2', 'edward e cox', 'democratic', '1924', 're - elected', 'edward e cox ( d ) unopposed'], ['georgia 3', 'charles r crisp', 'democratic', '1912', 're - elected', 'charles r crisp ( d ) unopposed'], ['georgia 4', 'william c wright', 'democratic', '1918', 're - elected', 'william c wright ( d ) unopposed'], ['georgia 5', 'william d upshaw', 'democratic', '1918', 'lost renomination democratic hold', 'leslie jasper steele ( d ) unopposed'], ['georgia 6', 'samuel rutherford', 'democratic', '1924', 're - elected', 'samuel rutherford ( d ) unopposed'], ['georgia 7', 'gordon lee', 'democratic', '1904', 'retired democratic hold', 'malcolm c tarver ( d ) unopposed'], ['georgia 8', 'charles h brand', 'democratic', '1916', 're - elected', 'charles h brand ( d ) unopposed'], ['georgia 9', 'thomas montgomery bell', 'democratic', '1904', 're - elected', 'thomas montgomery bell ( d ) unopposed'], ['georgia 10', 'carl vinson', 'democratic', '1914', 're - elected', 'carl vinson ( d ) unopposed'], ['georgia 11', 'william c lankford', 'democratic', '1918', 're - elected', 'william c lankford ( d ) unopposed']] |
shaun micheel | https://en.wikipedia.org/wiki/Shaun_Micheel | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1551537-3.html.csv | aggregation | shaun micheel had an average of around 3 cuts made in the various pga tournaments . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '3', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'cuts made'], 'result': '3', 'ind': 0, 'tostr': 'avg { all_rows ; cuts made }'}, '3'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; cuts made } ; 3 } = true', 'tointer': 'the average of the cuts made record of all rows is 3 .'} | round_eq { avg { all_rows ; cuts made } ; 3 } = true | the average of the cuts made record of all rows is 3 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'cuts made_4': 4, '3_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'cuts made_4': 'cuts made', '3_5': '3'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'cuts made_4': [0], '3_5': [1]} | ['tournament', 'wins', 'top - 5', 'top - 25', 'events', 'cuts made'] | [['masters tournament', '0', '0', '1', '5', '1'], ['us open', '0', '0', '1', '7', '3'], ['the open championship', '0', '0', '0', '4', '2'], ['pga championship', '1', '2', '3', '10', '6'], ['totals', '1', '2', '5', '26', '12']] |
1983 world judo championships | https://en.wikipedia.org/wiki/1983_World_Judo_Championships | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15807776-2.html.csv | count | there were only three nations that were awarded more than two medals in the 1983 world judo championships . | {'scope': 'all', 'criterion': 'greater_than', 'value': '2', 'result': '3', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'total', '2'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose total record is greater than 2 .', 'tostr': 'filter_greater { all_rows ; total ; 2 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_greater { all_rows ; total ; 2 } }', 'tointer': 'select the rows whose total record is greater than 2 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_greater { all_rows ; total ; 2 } } ; 3 } = true', 'tointer': 'select the rows whose total record is greater than 2 . the number of such rows is 3 .'} | eq { count { filter_greater { all_rows ; total ; 2 } } ; 3 } = true | select the rows whose total record is greater than 2 . the number of such rows is 3 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_greater_0': 0, 'all_rows_4': 4, 'total_5': 5, '2_6': 6, '3_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_greater_0': 'filter_greater', 'all_rows_4': 'all_rows', 'total_5': 'total', '2_6': '2', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_greater_0': [1], 'all_rows_4': [0], 'total_5': [0], '2_6': [0], '3_7': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'japan', '4', '1', '2', '7'], ['2', 'soviet union', '2', '1', '2', '5'], ['3', 'east germany', '2', '0', '2', '4'], ['4', 'italy', '0', '1', '1', '2'], ['4', 'hungary', '0', '1', '1', '2'], ['6', 'france', '0', '1', '0', '1'], ['6', 'czech republic', '0', '1', '0', '1'], ['6', 'great britain', '0', '1', '0', '1'], ['6', 'netherlands', '0', '1', '0', '1'], ['10', 'germany', '0', '0', '2', '2'], ['10', 'belgium', '0', '0', '2', '2'], ['10', 'romania', '0', '0', '2', '2'], ['13', 'united states', '0', '0', '1', '1'], ['13', 'poland', '0', '0', '1', '1']] |
wuji county | https://en.wikipedia.org/wiki/Wuji_County | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12425097-1.html.csv | aggregation | the average area of the towns and townships in wuji county is about 47km squared . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '47', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'area ( km square )'], 'result': '47', 'ind': 0, 'tostr': 'avg { all_rows ; area ( km square ) }'}, '47'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; area ( km square ) } ; 47 } = true', 'tointer': 'the average of the area ( km square ) record of all rows is 47 .'} | round_eq { avg { all_rows ; area ( km square ) } ; 47 } = true | the average of the area ( km square ) record of all rows is 47 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'area (km square)_4': 4, '47_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'area (km square)_4': 'area ( km square )', '47_5': '47'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'area (km square)_4': [0], '47_5': [1]} | ['name', 'hanzi', 'area ( km square )', 'population', 'villages'] | [['wuji town', '无极镇', '57', '76851', '25'], ['qiji town', '七汲镇', '54', '41584', '20'], ['zhangduangu town', '张段固镇', '51', '40916', '20'], ['beisu town', '北苏镇', '54', '54639', '18'], ['guozhuang town', '郭庄镇', '43', '43636', '23'], ['dachen town', '大陈镇', '42', '31297', '13'], ['haozhuang township', '郝庄乡', '55', '37786', '19'], ['donghoufang township', '东侯坊乡', '56', '48665', '24'], ['lichengdao township', '里城道乡', '44', '40411', '19'], ['nanliu township', '南流乡', '30', '24802', '12'], ['gaotou hui autonomous township', '高头回族乡', '32', '33722', '15']] |
united states house of representatives elections , 1922 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1922 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342426-5.html.csv | comparative | the candidates who took the seats in the 1922 united states house of representatives elections in both district 5 and district 6 of arkansas were placed due to the previous politician retiring . | {'row_1': '5', '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', 'district', 'arkansas 5'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose district record fuzzily matches to arkansas 5 .', 'tostr': 'filter_eq { all_rows ; district ; arkansas 5 }'}, 'result'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; district ; arkansas 5 } ; result }', 'tointer': 'select the rows whose district record fuzzily matches to arkansas 5 . take the result record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'district', 'arkansas 6'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose district record fuzzily matches to arkansas 6 .', 'tostr': 'filter_eq { all_rows ; district ; arkansas 6 }'}, 'result'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; district ; arkansas 6 } ; result }', 'tointer': 'select the rows whose district record fuzzily matches to arkansas 6 . take the result record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { all_rows ; district ; arkansas 5 } ; result } ; hop { filter_eq { all_rows ; district ; arkansas 6 } ; result } }', 'tointer': 'select the rows whose district record fuzzily matches to arkansas 5 . take the result record of this row . select the rows whose district record fuzzily matches to arkansas 6 . 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', 'district', 'arkansas 5'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose district record fuzzily matches to arkansas 5 .', 'tostr': 'filter_eq { all_rows ; district ; arkansas 5 }'}, 'result'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; district ; arkansas 5 } ; result }', 'tointer': 'select the rows whose district record fuzzily matches to arkansas 5 . take the result record of this row .'}, 'retired democratic hold'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; district ; arkansas 5 } ; result } ; retired democratic hold }', 'tointer': 'the result record of the first row is retired democratic hold .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'district', 'arkansas 6'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose district record fuzzily matches to arkansas 6 .', 'tostr': 'filter_eq { all_rows ; district ; arkansas 6 }'}, 'result'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; district ; arkansas 6 } ; result }', 'tointer': 'select the rows whose district record fuzzily matches to arkansas 6 . take the result record of this row .'}, 'retired democratic hold'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; district ; arkansas 6 } ; result } ; retired democratic hold }', 'tointer': 'the result record of the second row is retired democratic hold .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; district ; arkansas 5 } ; result } ; retired democratic hold } ; eq { hop { filter_eq { all_rows ; district ; arkansas 6 } ; result } ; retired democratic hold } }', 'tointer': 'the result record of the first row is retired democratic hold . the result record of the second row is retired democratic hold .'}], 'result': True, 'ind': 8, 'tostr': 'and { eq { hop { filter_eq { all_rows ; district ; arkansas 5 } ; result } ; hop { filter_eq { all_rows ; district ; arkansas 6 } ; result } } ; and { eq { hop { filter_eq { all_rows ; district ; arkansas 5 } ; result } ; retired democratic hold } ; eq { hop { filter_eq { all_rows ; district ; arkansas 6 } ; result } ; retired democratic hold } } } = true', 'tointer': 'select the rows whose district record fuzzily matches to arkansas 5 . take the result record of this row . select the rows whose district record fuzzily matches to arkansas 6 . take the result record of this row . the first record fuzzily matches to the second record . the result record of the first row is retired democratic hold . the result record of the second row is retired democratic hold .'} | and { eq { hop { filter_eq { all_rows ; district ; arkansas 5 } ; result } ; hop { filter_eq { all_rows ; district ; arkansas 6 } ; result } } ; and { eq { hop { filter_eq { all_rows ; district ; arkansas 5 } ; result } ; retired democratic hold } ; eq { hop { filter_eq { all_rows ; district ; arkansas 6 } ; result } ; retired democratic hold } } } = true | select the rows whose district record fuzzily matches to arkansas 5 . take the result record of this row . select the rows whose district record fuzzily matches to arkansas 6 . take the result record of this row . the first record fuzzily matches to the second record . the result record of the first row is retired democratic hold . the result record of the second row is retired democratic hold . | 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, 'district_11': 11, 'arkansas 5_12': 12, 'result_13': 13, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'district_15': 15, 'arkansas 6_16': 16, 'result_17': 17, 'and_7': 7, 'str_eq_5': 5, 'retired democratic hold_18': 18, 'str_eq_6': 6, 'retired democratic hold_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', 'district_11': 'district', 'arkansas 5_12': 'arkansas 5', 'result_13': 'result', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'district_15': 'district', 'arkansas 6_16': 'arkansas 6', 'result_17': 'result', 'and_7': 'and', 'str_eq_5': 'str_eq', 'retired democratic hold_18': 'retired democratic hold', 'str_eq_6': 'str_eq', 'retired democratic hold_19': 'retired democratic hold'} | {'and_8': [9], 'result_9': [], 'str_eq_4': [8], 'str_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'district_11': [0], 'arkansas 5_12': [0], 'result_13': [2], 'str_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'district_15': [1], 'arkansas 6_16': [1], 'result_17': [3], 'and_7': [8], 'str_eq_5': [7], 'retired democratic hold_18': [5], 'str_eq_6': [7], 'retired democratic hold_19': [6]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['arkansas 1', 'william j driver', 'democratic', '1920', 're - elected', 'william j driver ( d ) unopposed'], ['arkansas 2', 'william a oldfield', 'democratic', '1908', 're - elected', 'william a oldfield ( d ) unopposed'], ['arkansas 3', 'john n tillman', 'democratic', '1914', 're - elected', 'john n tillman ( d ) unopposed'], ['arkansas 4', 'otis wingo', 'democratic', '1912', 're - elected', 'otis wingo ( d ) unopposed'], ['arkansas 5', 'henderson m jacoway', 'democratic', '1910', 'retired democratic hold', 'heartsill ragon ( d ) unopposed'], ['arkansas 6', 'chester w taylor', 'democratic', '1921', 'retired democratic hold', 'lewis e sawyer ( d ) unopposed']] |
thai clubs in the afc cup | https://en.wikipedia.org/wiki/Thai_clubs_in_the_AFC_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16707879-4.html.csv | majority | in the thai clubs in the afc club , most of the games did not have a score of 0:0 . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'not_equal', 'value': '0:0', 'subset': None} | {'func': 'most_str_not_eq', 'args': ['all_rows', 'score', '0:0'], 'result': True, 'ind': 0, 'tointer': 'for the score records of all rows , most of them do not match to 0:0 .', 'tostr': 'most_not_eq { all_rows ; score ; 0:0 } = true'} | most_not_eq { all_rows ; score ; 0:0 } = true | for the score records of all rows , most of them do not match to 0:0 . | 1 | 1 | {'most_str_not_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'score_3': 3, '0:0_4': 4} | {'most_str_not_eq_0': 'most_str_not_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'score_3': 'score', '0:0_4': '0:0'} | {'most_str_not_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'score_3': [0], '0:0_4': [0]} | ['season', 'team 1', 'score', 'team 2', 'venue'] | [['2010', 'south china', '0:0', 'muangthong united', 'hong kong stadium , hong kong'], ['2010', 'muangthong united', '3:1', 'vb sports club', 'yamaha stadium ( thailand )'], ['2010', 'muangthong united', '4:1', 'persiwa wamena', 'yamaha stadium ( thailand )'], ['2010', 'vb sports club', '2:3', 'muangthong united', 'national stadium , maldives'], ['2010', 'muangthong united', '0:1', 'south china', 'surakul stadium , thailand'], ['2010', 'persiwa wamena', '2:2', 'muangthong united', 'gajayana stadium , indonesia'], ['2010', 'al - rayyan', '1:1 ( aet ) ( 2:4 p )', 'muangthong united', 'umm - affai stadium , qatar'], ['2010', 'al - karamah', '1:0', 'muangthong united', 'khaled bin walid stadium , syria'], ['2010', 'muangthong united', '2:0', 'al - karamah', 'yamaha stadium ( thailand )'], ['2010', 'muangthong united', '1:0', 'al - ittihad', 'yamaha stadium ( thailand )'], ['2010', 'al - ittihad', '2:0', 'muangthong united', 'aleppo international stadium , syria'], ['2011', 'muangthong united', '4:0', 't & t hanoi', 'scg stadium , thailand'], ['2011', 'tampines rovers', '1:1', 'muangthong united', 'jalan besar stadium , singapore'], ['2011', 'muangthong united', '1:0', 'victory sc', 'scg stadium , thailand'], ['2011', 'victory sc', '0:4', 'muangthong united', 'national stadium , maldives'], ['2011', 't & t hanoi', '0:0', 'muangthong united', 'hang day stadium , vietnam'], ['2011', 'muangthong united', '4:0', 'tampines rovers', 'scg stadium , thailand'], ['2011', 'muangthong united', '4:0', 'al ahed', 'scg stadium , thailand'], ['2011', 'kuwait sc', '1:0', 'muangthong united', 'al kuwait sports club stadium , kuwait'], ['2011', 'muangthong united', '0:0', 'kuwait sc', 'scg stadium , thailand']] |
1977 - 78 new york rangers season | https://en.wikipedia.org/wiki/1977%E2%80%9378_New_York_Rangers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17310913-3.html.csv | superlative | in november 1977 , the new york rangers ' highest score was 8 . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '4', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': 'n/a', 'subset': None} | {'func': 'eq', 'args': [{'func': 'max', 'args': ['all_rows', 'score'], 'result': '8 - 4', 'ind': 0, 'tostr': 'max { all_rows ; score }', 'tointer': 'the maximum score record of all rows is 8 - 4 .'}, '8 - 4'], 'result': True, 'ind': 1, 'tostr': 'eq { max { all_rows ; score } ; 8 - 4 } = true', 'tointer': 'the maximum score record of all rows is 8 - 4 .'} | eq { max { all_rows ; score } ; 8 - 4 } = true | the maximum score record of all rows is 8 - 4 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'max_0': 0, 'all_rows_3': 3, 'score_4': 4, '8 - 4_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'max_0': 'max', 'all_rows_3': 'all_rows', 'score_4': 'score', '8 - 4_5': '8 - 4'} | {'eq_1': [2], 'result_2': [], 'max_0': [1], 'all_rows_3': [0], 'score_4': [0], '8 - 4_5': [1]} | ['game', 'november', 'opponent', 'score', 'record'] | [['11', '2', 'colorado rockies', '6 - 2', '4 - 6 - 1'], ['12', '4', 'vancouver canucks', '5 - 1', '5 - 6 - 1'], ['13', '5', 'los angeles kings', '3 - 1', '5 - 7 - 1'], ['14', '9', 'buffalo sabres', '8 - 4', '6 - 7 - 1'], ['15', '12', 'detroit red wings', '3 - 1', '6 - 8 - 1'], ['16', '13', 'atlanta flames', '5 - 2', '6 - 9 - 1'], ['17', '16', 'chicago black hawks', '5 - 2', '7 - 9 - 1'], ['18', '19', 'pittsburgh penguins', '5 - 5', '7 - 9 - 2'], ['19', '20', 'vancouver canucks', '3 - 0', '7 - 10 - 2'], ['20', '23', 'colorado rockies', '6 - 3', '8 - 10 - 2'], ['21', '26', 'boston bruins', '3 - 2', '8 - 11 - 2'], ['22', '27', 'buffalo sabres', '3 - 2', '8 - 12 - 2'], ['23', '30', 'st louis blues', '4 - 0', '9 - 12 - 2']] |
mars hill network | https://en.wikipedia.org/wiki/Mars_Hill_Network | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12454334-1.html.csv | unique | wmhr is the only mars hill network radio station licensed in the city of syracuse . | {'scope': 'all', 'row': '4', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': 'syracuse , ny', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'city of license', 'syracuse , ny'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose city of license record fuzzily matches to syracuse , ny .', 'tostr': 'filter_eq { all_rows ; city of license ; syracuse , ny }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; city of license ; syracuse , ny } }', 'tointer': 'select the rows whose city of license record fuzzily matches to syracuse , ny . 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 of license', 'syracuse , ny'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose city of license record fuzzily matches to syracuse , ny .', 'tostr': 'filter_eq { all_rows ; city of license ; syracuse , ny }'}, 'call sign'], 'result': 'wmhr', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; city of license ; syracuse , ny } ; call sign }'}, 'wmhr'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; city of license ; syracuse , ny } ; call sign } ; wmhr }', 'tointer': 'the call sign record of this unqiue row is wmhr .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; city of license ; syracuse , ny } } ; eq { hop { filter_eq { all_rows ; city of license ; syracuse , ny } ; call sign } ; wmhr } } = true', 'tointer': 'select the rows whose city of license record fuzzily matches to syracuse , ny . there is only one such row in the table . the call sign record of this unqiue row is wmhr .'} | and { only { filter_eq { all_rows ; city of license ; syracuse , ny } } ; eq { hop { filter_eq { all_rows ; city of license ; syracuse , ny } ; call sign } ; wmhr } } = true | select the rows whose city of license record fuzzily matches to syracuse , ny . there is only one such row in the table . the call sign record of this unqiue row is wmhr . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'city of license_7': 7, 'syracuse , ny_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'call sign_9': 9, 'wmhr_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'city of license_7': 'city of license', 'syracuse , ny_8': 'syracuse , ny', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'call sign_9': 'call sign', 'wmhr_10': 'wmhr'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'city of license_7': [0], 'syracuse , ny_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'call sign_9': [2], 'wmhr_10': [3]} | ['call sign', 'frequency', 'city of license', 'facility id', 'erp / power w', 'height m ( ft )', 'class'] | [['wmhi', '94.7 fm', 'cape vincent , ny', '40435', '5800', '-', 'a'], ['wmhn', '89.3 fm', 'webster , ny', '40430', '1000', '-', 'a'], ['wmhq', '90.1 fm', 'malone , ny', '89863', '2700', '-', 'a'], ['wmhr', '102.9 fm', 'syracuse , ny', '40432', '20000', '-', 'b'], ['wmhu', '91.1 fm', 'cold brook , ny', '174468', '560', '-', 'a']] |
list of the largest trading partners of india | https://en.wikipedia.org/wiki/List_of_the_largest_trading_partners_of_India | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-26160007-1.html.csv | superlative | the largest trading partner of india with the highest amount of imports is china . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '2', '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', 'imports'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; imports }'}, 'country'], 'result': 'china', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; imports } ; country }'}, 'china'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; imports } ; country } ; china } = true', 'tointer': 'select the row whose imports record of all rows is maximum . the country record of this row is china .'} | eq { hop { argmax { all_rows ; imports } ; country } ; china } = true | select the row whose imports record of all rows is maximum . the country record of this row is china . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'imports_5': 5, 'country_6': 6, 'china_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'imports_5': 'imports', 'country_6': 'country', 'china_7': 'china'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'imports_5': [0], 'country_6': [1], 'china_7': [2]} | ['country', 'exports', 'imports', 'total trade', 'trade balance'] | [['united arab emirates', '36265.15', '38436.47', '74701.61', '- 2171.32'], ['china', '13503.00', '54324.04', '67827.04', '- 40821.04'], ['united states', '36152.30', '24343.73', '60496.03', '11808.57'], ['saudi arabia', '9783.81', '34130.50', '43914.31', '- 24346.69'], ['switzerland', '1116.98', '29915.78', '31032.76', '- 28798.80'], ['singapore', '13608.65', '7754.38', '21363.03', '5854.27'], ['germany', '7244.63', '14373.91', '21618.54', '- 7129.28'], ['hong kong', '12278.31', '8078.58', '20356.89', '4199.74'], ['indonesia', '5331.47', '14774.27', '20105.75', '- 9442.80'], ['iraq', '1278.13', '20155.94', '21434.07', '- 18877.81'], ['japan', '6099.06', '12514.07', '18613.14', '- 6415.01'], ['belgium', '5506.63', '10087.16', '15593.80', '- 4580.53'], ['kuwait', '1060.80', '16569.63', '17630.43', '- 15508.83'], ['iran', '3351.07', '11603.79', '14954.86', '- 8252.72']] |
telecommunications in moldova | https://en.wikipedia.org/wiki/Telecommunications_in_Moldova | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19246-1.html.csv | superlative | for telecommunicatoins in moldova , the largest connection speed by an orange carrier was 236.8 kbit / s. | {'scope': 'subset', 'col_superlative': '4', 'row_superlative': '2', 'value_mentioned': 'yes', 'max_or_min': 'max', 'other_col': '1', 'subset': {'col': '1', 'criterion': 'equal', 'value': 'orange'}} | {'func': 'eq', 'args': [{'func': 'max', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'carrier', 'orange'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; carrier ; orange }', 'tointer': 'select the rows whose carrier record fuzzily matches to orange .'}, 'connection speed'], 'result': '236.8 kbit / s', 'ind': 1, 'tostr': 'max { filter_eq { all_rows ; carrier ; orange } ; connection speed }', 'tointer': 'select the rows whose carrier record fuzzily matches to orange . the maximum connection speed record of these rows is 236.8 kbit / s .'}, '236.8 kbit / s'], 'result': True, 'ind': 2, 'tostr': 'eq { max { filter_eq { all_rows ; carrier ; orange } ; connection speed } ; 236.8 kbit / s } = true', 'tointer': 'select the rows whose carrier record fuzzily matches to orange . the maximum connection speed record of these rows is 236.8 kbit / s .'} | eq { max { filter_eq { all_rows ; carrier ; orange } ; connection speed } ; 236.8 kbit / s } = true | select the rows whose carrier record fuzzily matches to orange . the maximum connection speed record of these rows is 236.8 kbit / s . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'max_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'carrier_5': 5, 'orange_6': 6, 'connection speed_7': 7, '236.8 kbit / s_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'max_1': 'max', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'carrier_5': 'carrier', 'orange_6': 'orange', 'connection speed_7': 'connection speed', '236.8 kbit / s_8': '236.8 kbit / s'} | {'eq_2': [3], 'result_3': [], 'max_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'carrier_5': [0], 'orange_6': [0], 'connection speed_7': [1], '236.8 kbit / s_8': [2]} | ['carrier', 'standard', 'frequency', 'connection speed', 'launch date ( ddmmyyyy )'] | [['orange', 'gsm gprs', '900 mhz and 1800 mhz', '56 kbit / s', '14.09.2005'], ['orange', 'gsm edge', '900 mhz and 1800 mhz', '236.8 kbit / s', '17.04.2006'], ['moldcell', 'gsm gprs', '900 mhz and 1800 mhz', '56 kbit / s', '31.01.2005'], ['moldcell', 'gsm edge', '900 mhz and 1800 mhz', '236.8 kbit / s', '07.06.2005'], ['unitã', 'cdma 1x', '450 mhz', '153 kbit / s', '01.03.2007'], ['idc', 'cdma 1x', '800 mhz', '153 kbit / s', '09.09.1999']] |
ingo schultz | https://en.wikipedia.org/wiki/Ingo_Schultz | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15186827-1.html.csv | unique | ingo schultz placed first one time in the 2002 european championships in the 400 meter dash . | {'scope': 'all', 'row': '3', 'col': '4', 'col_other': '2,5', 'criterion': 'equal', 'value': '1st', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', '1st'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to 1st .', 'tostr': 'filter_eq { all_rows ; result ; 1st }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; result ; 1st } }', 'tointer': 'select the rows whose result record fuzzily matches to 1st . there is only one such row in the table .'}, {'func': 'and', 'args': [{'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', '1st'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to 1st .', 'tostr': 'filter_eq { all_rows ; result ; 1st }'}, 'tournament'], 'result': 'european championships', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; result ; 1st } ; tournament }'}, 'european championships'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; result ; 1st } ; tournament } ; european championships }', 'tointer': 'the tournament record of this unqiue row is european championships .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', '1st'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to 1st .', 'tostr': 'filter_eq { all_rows ; result ; 1st }'}, 'extra'], 'result': '400 m', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; result ; 1st } ; extra }'}, '400 m'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; result ; 1st } ; extra } ; 400 m }', 'tointer': 'the extra record of this unqiue row is 400 m .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; result ; 1st } ; tournament } ; european championships } ; eq { hop { filter_eq { all_rows ; result ; 1st } ; extra } ; 400 m } }', 'tointer': 'the tournament record of this unqiue row is european championships . the extra record of this unqiue row is 400 m .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; result ; 1st } } ; and { eq { hop { filter_eq { all_rows ; result ; 1st } ; tournament } ; european championships } ; eq { hop { filter_eq { all_rows ; result ; 1st } ; extra } ; 400 m } } } = true', 'tointer': 'select the rows whose result record fuzzily matches to 1st . there is only one such row in the table . the tournament record of this unqiue row is european championships . the extra record of this unqiue row is 400 m .'} | and { only { filter_eq { all_rows ; result ; 1st } } ; and { eq { hop { filter_eq { all_rows ; result ; 1st } ; tournament } ; european championships } ; eq { hop { filter_eq { all_rows ; result ; 1st } ; extra } ; 400 m } } } = true | select the rows whose result record fuzzily matches to 1st . there is only one such row in the table . the tournament record of this unqiue row is european championships . the extra record of this unqiue row is 400 m . | 10 | 8 | {'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, 'result_10': 10, '1st_11': 11, 'and_6': 6, 'str_eq_3': 3, 'str_hop_2': 2, 'tournament_12': 12, 'european championships_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'extra_14': 14, '400 m_15': 15} | {'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_9': 'all_rows', 'result_10': 'result', '1st_11': '1st', 'and_6': 'and', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'tournament_12': 'tournament', 'european championships_13': 'european championships', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'extra_14': 'extra', '400 m_15': '400 m'} | {'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], 'result_10': [0], '1st_11': [0], 'and_6': [7], 'str_eq_3': [6], 'str_hop_2': [3], 'tournament_12': [2], 'european championships_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'extra_14': [4], '400 m_15': [5]} | ['year', 'tournament', 'venue', 'result', 'extra'] | [['2000', 'european indoor championships', 'ghent , belgium', '2nd', '4x400 m relay'], ['2001', 'world championships', 'edmonton , canada', '2nd', '400 m'], ['2002', 'european championships', 'munich , germany', '1st', '400 m'], ['2002', 'european championships', 'munich , germany', '7th', '4x400 m relay'], ['2002', 'world cup', 'madrid , spain', '2nd', '400 m'], ['2002', 'world cup', 'madrid , spain', '7th', '4x400 m relay'], ['2004', 'olympic games', 'athens , greece', '7th', '4x400 m relay']] |
günter netzer | https://en.wikipedia.org/wiki/G%C3%BCnter_Netzer | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1085623-1.html.csv | comparative | günter netzer scored a goal in athens , greece earlier than he did in oslo , norway . | {'row_1': '1', 'row_2': '3', 'col': '1', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'athens , greece'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to athens , greece .', 'tostr': 'filter_eq { all_rows ; venue ; athens , greece }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; venue ; athens , greece } ; date }', 'tointer': 'select the rows whose venue record fuzzily matches to athens , greece . take the date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'oslo , norway'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose venue record fuzzily matches to oslo , norway .', 'tostr': 'filter_eq { all_rows ; venue ; oslo , norway }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; venue ; oslo , norway } ; date }', 'tointer': 'select the rows whose venue record fuzzily matches to oslo , norway . take the date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; venue ; athens , greece } ; date } ; hop { filter_eq { all_rows ; venue ; oslo , norway } ; date } } = true', 'tointer': 'select the rows whose venue record fuzzily matches to athens , greece . take the date record of this row . select the rows whose venue record fuzzily matches to oslo , norway . take the date record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; venue ; athens , greece } ; date } ; hop { filter_eq { all_rows ; venue ; oslo , norway } ; date } } = true | select the rows whose venue record fuzzily matches to athens , greece . take the date record of this row . select the rows whose venue record fuzzily matches to oslo , norway . 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, 'venue_7': 7, 'athens , greece_8': 8, 'date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'venue_11': 11, 'oslo , norway_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', 'venue_7': 'venue', 'athens , greece_8': 'athens , greece', 'date_9': 'date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'venue_11': 'venue', 'oslo , norway_12': 'oslo , norway', 'date_13': 'date'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'venue_7': [0], 'athens , greece_8': [0], 'date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'venue_11': [1], 'oslo , norway_12': [1], 'date_13': [3]} | ['date', 'venue', 'score', 'result', 'competition'] | [['22 november 1970', 'athens , greece', '1 - 0', '3 - 1', 'friendly'], ['12 june 1971', 'karlsruhe , germany', '1 - 0', '2 - 0', 'uefa euro 1972 qualifying'], ['22 june 1971', 'oslo , norway', '7 - 0', '7 - 1', 'friendly'], ['8 september 1971', 'hanover , germany', '4 - 0', '5 - 0', 'friendly'], ['29 april 1972', 'london , uk', '2 - 1', '3 - 1', 'uefa euro 1972 qualifying'], ['15 november 1972', 'düsseldorf , germany', '4 - 0', '5 - 1', 'friendly']] |
liselotte neumann | https://en.wikipedia.org/wiki/Liselotte_Neumann | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1710991-1.html.csv | unique | the us women 's open was the only tournament in which liselotte neumann had a winning score of -7 . | {'scope': 'all', 'row': '1', 'col': '3', 'col_other': '2', 'criterion': 'fuzzily_match', 'value': '- 7', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winning score', '- 7'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winning score record fuzzily matches to - 7 .', 'tostr': 'filter_eq { all_rows ; winning score ; - 7 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; winning score ; - 7 } }', 'tointer': 'select the rows whose winning score record fuzzily matches to - 7 . 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 score', '- 7'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winning score record fuzzily matches to - 7 .', 'tostr': 'filter_eq { all_rows ; winning score ; - 7 }'}, 'tournament'], 'result': "us women 's open", 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; winning score ; - 7 } ; tournament }'}, "us women 's open"], 'result': True, 'ind': 3, 'tostr': "eq { hop { filter_eq { all_rows ; winning score ; - 7 } ; tournament } ; us women 's open }", 'tointer': "the tournament record of this unqiue row is us women 's open ."}], 'result': True, 'ind': 4, 'tostr': "and { only { filter_eq { all_rows ; winning score ; - 7 } } ; eq { hop { filter_eq { all_rows ; winning score ; - 7 } ; tournament } ; us women 's open } } = true", 'tointer': "select the rows whose winning score record fuzzily matches to - 7 . there is only one such row in the table . the tournament record of this unqiue row is us women 's open ."} | and { only { filter_eq { all_rows ; winning score ; - 7 } } ; eq { hop { filter_eq { all_rows ; winning score ; - 7 } ; tournament } ; us women 's open } } = true | select the rows whose winning score record fuzzily matches to - 7 . there is only one such row in the table . the tournament record of this unqiue row is us women 's open . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'winning score_7': 7, '- 7_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'tournament_9': 9, "us women 's open_10": 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'winning score_7': 'winning score', '- 7_8': '- 7', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'tournament_9': 'tournament', "us women 's open_10": "us women 's open"} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'winning score_7': [0], '- 7_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'tournament_9': [2], "us women 's open_10": [3]} | ['date', 'tournament', 'winning score', 'margin of victory', 'runner ( s ) - up'] | [['7 sep 1988', "us women 's open", '- 7 ( 67 + 72 + 69 + 69 = 277 )', '3 strokes', 'patty sheehan'], ['10 nov 1991', 'mazda japan classic', '- 5 ( 70 + 72 + 69 = 211 )', '2 strokes', 'caroline keggi , dottie pepper'], ['12 jun 1994', 'minnesota lpga classic', '- 11 ( 68 + 71 + 66 = 205 )', '2 strokes', 'hiromi kobayashi'], ['12 aug 1994', "weetabix women 's british open", '- 14 ( 71 + 67 + 70 + 72 = 280 )', '3 strokes', 'dottie pepper , annika sörenstam'], ['2 oct 1994', 'ghp heartland classic', '- 10 ( 70 + 71 + 67 + 70 = 278 )', '3 strokes', 'elaine crosby , pearl sinn'], ['14 jan 1996', 'chrysler - plymouth tournament of champions', '- 13 ( 67 + 66 + 72 + 70 = 275 )', '11 strokes', 'karrie webb'], ['17 mar 1996', "ping / welch 's championship ( tucson )", '- 12 ( 68 + 71 + 69 + 68 = 276 )', '1 stroke', 'cathy johnston - forbes'], ['6 jun 1996', 'edina realty lpga classic', '- 9 ( 67 + 73 + 67 = 207 )', 'playoff', 'brandie burton , carin koch , suzanne strudwick'], ['21 sep 1997', "welch 's championship", '- 12 ( 67 + 70 + 69 + 70 = 276 )', '3 strokes', 'nancy harvey'], ['9 nov 1997', 'toray japan queens cup', '- 11 ( 68 + 70 + 67 = 205 )', '1 sttroke', 'lorie kane'], ['22 mar 1998', 'standard register ping', '- 13 ( 69 + 67 + 69 + 74 = 279 )', 'playoff', 'rosie jones'], ['26 apr 1998', 'chick - fil - a charity championship', '- 14 ( 67 + 65 + 70 = 202 )', '2 strokes', 'lori kane , dottie pepper'], ['10 oct 2004', 'asahi ryokuken international championship', '- 15 ( 68 + 68 + 69 + 68 = 273 )', '3 strokes', 'grace park']] |
2010 - 11 oklahoma city thunder season | https://en.wikipedia.org/wiki/2010%E2%80%9311_Oklahoma_City_Thunder_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27712702-11.html.csv | unique | in the 2010 - 11 oklahoma city thunder season , the only game where the venue was fedex forum , was march 7th . | {'scope': 'all', 'row': '4', 'col': '8', 'col_other': '2', 'criterion': 'fuzzily_match', 'value': 'fedexforum', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location attendance', 'fedexforum'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location attendance record fuzzily matches to fedexforum .', 'tostr': 'filter_eq { all_rows ; location attendance ; fedexforum }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; location attendance ; fedexforum } }', 'tointer': 'select the rows whose location attendance record fuzzily matches to fedexforum . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location attendance', 'fedexforum'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location attendance record fuzzily matches to fedexforum .', 'tostr': 'filter_eq { all_rows ; location attendance ; fedexforum }'}, 'date'], 'result': 'march 7', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; location attendance ; fedexforum } ; date }'}, 'march 7'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; location attendance ; fedexforum } ; date } ; march 7 }', 'tointer': 'the date record of this unqiue row is march 7 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; location attendance ; fedexforum } } ; eq { hop { filter_eq { all_rows ; location attendance ; fedexforum } ; date } ; march 7 } } = true', 'tointer': 'select the rows whose location attendance record fuzzily matches to fedexforum . there is only one such row in the table . the date record of this unqiue row is march 7 .'} | and { only { filter_eq { all_rows ; location attendance ; fedexforum } } ; eq { hop { filter_eq { all_rows ; location attendance ; fedexforum } ; date } ; march 7 } } = true | select the rows whose location attendance record fuzzily matches to fedexforum . there is only one such row in the table . the date record of this unqiue row is march 7 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'location attendance_7': 7, 'fedexforum_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, 'march 7_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'location attendance_7': 'location attendance', 'fedexforum_8': 'fedexforum', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', 'march 7_10': 'march 7'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'location attendance_7': [0], 'fedexforum_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], 'march 7_10': [3]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['59', 'march 2', 'indiana', 'w 113 - 89 ( ot )', 'kevin durant , russell westbrook ( 21 )', 'serge ibaka ( 12 )', 'russell westbrook ( 9 )', 'oklahoma city arena 18203', '37 - 22'], ['60', 'march 4', 'atlanta', 'w 111 - 104 ( ot )', 'kevin durant ( 29 )', 'kevin durant ( 8 )', 'russell westbrook ( 9 )', 'philips arena 17916', '38 - 22'], ['61', 'march 6', 'phoenix', 'w 122 - 118 ( ot )', 'russell westbrook ( 32 )', 'nick collison , thabo sefolosha ( 9 )', 'russell westbrook ( 11 )', 'oklahoma city arena 18203', '39 - 22'], ['62', 'march 7', 'memphis', 'l 101 - 107 ( ot )', 'russell westbrook ( 27 )', 'kevin durant , james harden , serge ibaka ( 6 )', 'russell westbrook ( 7 )', 'fedexforum 13903', '39 - 23'], ['63', 'march 9', 'philadelphia', 'w 110 - 105 ( ot )', 'kevin durant ( 34 )', 'kevin durant ( 16 )', 'russell westbrook ( 12 )', 'wells fargo center 19283', '40 - 23'], ['64', 'march 11', 'detroit', 'w 104 - 94 ( ot )', 'kevin durant ( 24 )', 'kevin durant ( 9 )', 'russell westbrook ( 11 )', 'oklahoma city arena 18203', '41 - 23'], ['65', 'march 13', 'cleveland', 'w 95 - 75 ( ot )', 'russell westbrook ( 20 )', 'serge ibaka ( 14 )', 'eric maynor ( 8 )', 'quicken loans arena 19811', '42 - 23'], ['66', 'march 14', 'washington', 'w 116 - 89 ( ot )', 'kevin durant ( 32 )', 'kendrick perkins ( 9 )', 'russell westbrook ( 12 )', 'verizon center 17921', '43 - 23'], ['67', 'march 16', 'miami', 'w 96 - 85 ( ot )', 'kevin durant ( 29 )', 'serge ibaka ( 12 )', 'kevin durant ( 6 )', 'american airlines arena 20083', '44 - 23'], ['68', 'march 18', 'charlotte', 'w 99 - 82 ( ot )', 'kevin durant ( 25 )', 'serge ibaka ( 13 )', 'russell westbrook ( 7 )', 'oklahoma city arena 18203', '45 - 23'], ['70', 'march 23', 'utah', 'w 106 - 94 ( ot )', 'russell westbrook ( 31 )', 'serge ibaka ( 13 )', 'russell westbrook ( 5 )', 'oklahoma city arena 18203', '46 - 24'], ['71', 'march 25', 'minnesota', 'w 111 - 103 ( ot )', 'kevin durant ( 23 )', 'serge ibaka ( 10 )', 'russell westbrook ( 8 )', 'oklahoma city arena 18203', '47 - 24'], ['72', 'march 27', 'portland', 'w 99 - 90 ( ot )', 'russell westbrook ( 28 )', 'kendrick perkins ( 10 )', 'russell westbrook ( 7 )', 'oklahoma city arena 18203', '48 - 24'], ['73', 'march 29', 'golden state', 'w 115 - 114 ( ot )', 'kevin durant ( 39 )', 'kendrick perkins ( 13 )', 'russell westbrook ( 9 )', 'oklahoma city arena 18203', '49 - 24']] |
chiefs - raiders rivalry | https://en.wikipedia.org/wiki/Chiefs%E2%80%93Raiders_rivalry | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11840325-4.html.csv | aggregation | the chiefs scored 59 points against the raiders in the 1979 season . | {'scope': 'subset', 'col': '4', 'type': 'sum', 'result': '59', 'subset': {'col': '1', 'criterion': 'equal', 'value': '1979'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'year', '1979'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; year ; 1979 }', 'tointer': 'select the rows whose year record is equal to 1979 .'}, 'result'], 'result': '59', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; year ; 1979 } ; result }'}, '59'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; year ; 1979 } ; result } ; 59 } = true', 'tointer': 'select the rows whose year record is equal to 1979 . the sum of the result record of these rows is 59 .'} | round_eq { sum { filter_eq { all_rows ; year ; 1979 } ; result } ; 59 } = true | select the rows whose year record is equal to 1979 . the sum of the result record of these rows is 59 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'year_5': 5, '1979_6': 6, 'result_7': 7, '59_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'year_5': 'year', '1979_6': '1979', 'result_7': 'result', '59_8': '59'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'year_5': [0], '1979_6': [0], 'result_7': [1], '59_8': [2]} | ['year', 'date', 'winner', 'result', 'loser', 'location'] | [['1970', 'november 1', 'kansas city chiefs', '17 - 17', 'oakland raiders', 'municipal stadium'], ['1970', 'december 12', 'oakland raiders', '20 - 6', 'kansas city chiefs', 'oakland - alameda county coliseum'], ['1971', 'october 31', 'kansas city chiefs', '20 - 20', 'oakland raiders', 'oakland - alameda county coliseum'], ['1971', 'december 12', 'kansas city chiefs', '16 - 14', 'oakland raiders', 'municipal stadium'], ['1972', 'november 5', 'kansas city chiefs', '27 - 14', 'oakland raiders', 'arrowhead stadium'], ['1972', 'november 26', 'oakland raiders', '26 - 3', 'kansas city chiefs', 'oakland - alameda county coliseum'], ['1973', 'september 30', 'kansas city chiefs', '16 - 3', 'oakland raiders', 'arrowhead stadium'], ['1973', 'december 8', 'oakland raiders', '37 - 7', 'kansas city chiefs', 'oakland - alameda county coliseum'], ['1974', 'september 22', 'oakland raiders', '27 - 7', 'kansas city chiefs', 'oakland - alameda county coliseum'], ['1974', 'december 8', 'oakland raiders', '7 - 6', 'kansas city chiefs', 'arrowhead stadium'], ['1975', 'october 12', 'kansas city chiefs', '42 - 10', 'oakland raiders', 'arrowhead stadium'], ['1975', 'december 21', 'oakland raiders', '28 - 20', 'kansas city chiefs', 'oakland - alameda county coliseum'], ['1976', 'september 20', 'oakland raiders', '24 - 21', 'kansas city chiefs', 'arrowhead stadium'], ['1976', 'november 14', 'oakland raiders', '21 - 10', 'kansas city chiefs', 'oakland - alameda county coliseum'], ['1977', 'october 3', 'oakland raiders', '37 - 28', 'kansas city chiefs', 'oakland - alameda county coliseum'], ['1977', 'december 18', 'oakland raiders', '21 - 20', 'kansas city chiefs', 'arrowhead stadium'], ['1978', 'october 15', 'oakland raiders', '28 - 6', 'kansas city chiefs', 'oakland - alameda county coliseum'], ['1978', 'november 5', 'oakland raiders', '20 - 10', 'kansas city chiefs', 'arrowhead stadium'], ['1979', 'september 23', 'kansas city chiefs', '35 - 7', 'oakland raiders', 'arrowhead stadium'], ['1979', 'november 18', 'kansas city chiefs', '24 - 21', 'oakland raiders', 'oakland - alameda county coliseum']] |
list of reality television show franchises | https://en.wikipedia.org/wiki/List_of_reality_television_show_franchises | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24224647-2.html.csv | comparative | among clash of the choirs franchisees , det store korslaget premiered after körslaget . | {'row_1': '8', 'row_2': '12', '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', 'local name', 'det store korslaget'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose local name record fuzzily matches to det store korslaget .', 'tostr': 'filter_eq { all_rows ; local name ; det store korslaget }'}, 'year premiered'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; local name ; det store korslaget } ; year premiered }', 'tointer': 'select the rows whose local name record fuzzily matches to det store korslaget . take the year premiered record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'local name', 'körslaget'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose local name record fuzzily matches to körslaget .', 'tostr': 'filter_eq { all_rows ; local name ; körslaget }'}, 'year premiered'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; local name ; körslaget } ; year premiered }', 'tointer': 'select the rows whose local name record fuzzily matches to körslaget . take the year premiered record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; local name ; det store korslaget } ; year premiered } ; hop { filter_eq { all_rows ; local name ; körslaget } ; year premiered } } = true', 'tointer': 'select the rows whose local name record fuzzily matches to det store korslaget . take the year premiered record of this row . select the rows whose local name record fuzzily matches to körslaget . take the year premiered record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; local name ; det store korslaget } ; year premiered } ; hop { filter_eq { all_rows ; local name ; körslaget } ; year premiered } } = true | select the rows whose local name record fuzzily matches to det store korslaget . take the year premiered record of this row . select the rows whose local name record fuzzily matches to körslaget . take the year premiered 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, 'local name_7': 7, 'det store korslaget_8': 8, 'year premiered_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'local name_11': 11, 'körslaget_12': 12, 'year premiered_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', 'local name_7': 'local name', 'det store korslaget_8': 'det store korslaget', 'year premiered_9': 'year premiered', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'local name_11': 'local name', 'körslaget_12': 'körslaget', 'year premiered_13': 'year premiered'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'local name_7': [0], 'det store korslaget_8': [0], 'year premiered_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'local name_11': [1], 'körslaget_12': [1], 'year premiered_13': [3]} | ['region / country', 'local name', 'main presenter', 'network', 'year premiered'] | [['china', '夢想合唱團 mengxiang hechang tuan', 'sa beining', 'cctv - 1', '2011'], ['denmark', 'allstars', 'lisbeth østergaard', 'tv2', '2008'], ['estonia', 'laululahing', 'tarmo leinatamm', 'etv', '2008'], ['finland', 'kuorosota', 'kristiina komulainen', 'nelonen', '2009'], ['france', 'la bataille des chorales', 'benjamin castaldi', 'tf1', '2009'], ['latvia', 'koru kari', 'lauris reiniks', 'tv3', '2008'], ['lithuania', 'chorų karai', 'vytautas šapranauskas & jurgita jurkutė', 'tv3', '2010'], ['norway', 'det store korslaget', 'øyvind mund', 'tv2', '2009'], ['poland', 'bitwa na glosy', 'hubert urbanski & piotr kedzierski', 'tvp 2', '2010'], ['russia', 'битва хоров', 'valery meladze', 'rossiya 1', '2012'], ['spain', 'la batalla de los coros', 'josep lobató', 'cuatro', '2008'], ['sweden', 'körslaget', 'gry forssell', 'tv4', '2008'], ['switzerland', 'kampf der chöre', 'sven epiney', 'sf 1', '2010'], ['turkey', 'korolar çarpışıyor', 'behzat uighur', 'show tv', '2009'], ['vietnam', 'hợp ca tranh tài', 'nguyên khang', 'vtv3', '2012']] |
india national under - 23 football team results | https://en.wikipedia.org/wiki/India_national_under-23_football_team_results | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25428629-1.html.csv | count | 2 india national under-23 football team game locations occurred in india . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'india', 'result': '2', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'india'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to india .', 'tostr': 'filter_eq { all_rows ; location ; india }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; location ; india } }', 'tointer': 'select the rows whose location record fuzzily matches to india . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; location ; india } } ; 2 } = true', 'tointer': 'select the rows whose location record fuzzily matches to india . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; location ; india } } ; 2 } = true | select the rows whose location record fuzzily matches to india . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'location_5': 5, 'india_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'location_5': 'location', 'india_6': 'india', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location_5': [0], 'india_6': [0], '2_7': [2]} | ['date', 'tournament', 'location', 'opponent', 'stadium', 'score', 'indian scorers'] | [['23 february 2011', '2012 olympic qualifier', 'pune , india', 'myanmar', 'balewadi sports complex', '2 - 1', 'jeje lalpekhlua , malsawmfela'], ['9 march 2011', '2012 olympic qualifier', 'yangon , myanmar', 'myanmar', 'thuwunna stadium', '1 - 1', 'chinadorai sabeeth'], ['19 june 2011', '2012 olympic qualifier', 'doha , qatar', 'qatar', 'jassim bin hamad stadium', '1 - 3', 'jeje lalpekhlua'], ['23 june 2011', '2012 olympic qualifier', 'pune , india', 'qatar', 'balewadi sports complex', '1 - 1', 'own goal'], ['25 june 2012', '2014 afc u - 22 asian cup qualifiers', 'muscat , oman', 'iraq', 'royal oman police stadium', '1 - 2', 'alwyn george'], ['28 june 2012', '2014 afc u - 22 asian cup qualifiers', 'muscat , oman', 'united arab emirates', 'royal oman police stadium', '1 - 1', 'romeo fernandes']] |
2005 st. louis cardinals season | https://en.wikipedia.org/wiki/2005_St._Louis_Cardinals_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11195757-1.html.csv | count | in the 2005 st. louis cardinals season , among the games played after april 8 , 2 of them drew more than 35,000 people . | {'scope': 'subset', 'criterion': 'greater_than', 'value': '35000', 'result': '2', 'col': '5', 'subset': {'col': '1', 'criterion': 'greater_than', 'value': 'april 8'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'date', 'april 8'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; date ; april 8 }', 'tointer': 'select the rows whose date record is greater than april 8 .'}, 'attendance', '35000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record is greater than april 8 . among these rows , select the rows whose attendance record is greater than 35000 .', 'tostr': 'filter_greater { filter_greater { all_rows ; date ; april 8 } ; attendance ; 35000 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_greater { filter_greater { all_rows ; date ; april 8 } ; attendance ; 35000 } }', 'tointer': 'select the rows whose date record is greater than april 8 . among these rows , select the rows whose attendance record is greater than 35000 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_greater { filter_greater { all_rows ; date ; april 8 } ; attendance ; 35000 } } ; 2 } = true', 'tointer': 'select the rows whose date record is greater than april 8 . among these rows , select the rows whose attendance record is greater than 35000 . the number of such rows is 2 .'} | eq { count { filter_greater { filter_greater { all_rows ; date ; april 8 } ; attendance ; 35000 } } ; 2 } = true | select the rows whose date record is greater than april 8 . among these rows , select the rows whose attendance record is greater than 35000 . 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, 'date_6': 6, 'april 8_7': 7, 'attendance_8': 8, '35000_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', 'date_6': 'date', 'april 8_7': 'april 8', 'attendance_8': 'attendance', '35000_9': '35000', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_greater_1': [2], 'filter_greater_0': [1], 'all_rows_5': [0], 'date_6': [0], 'april 8_7': [0], 'attendance_8': [1], '35000_9': [1], '2_10': [3]} | ['date', 'opponent', 'score', 'loss', 'attendance', 'record'] | [['april 5', 'astros 6:05 pm', '7 - 3', 'oswalt ( 0 - 1 )', '43567', '1 - 0'], ['april 6', 'astros 4:05 pm', '4 - 1', 'tavárez ( 0 - 1 )', '28496', '1 - 1'], ['april 8', 'phillies 1:15 pm', '6 - 5', 'madson ( 0 - 1 )', '50074', '2 - 1'], ['april 9', 'phillies 1:15 pm', '10 - 4', 'suppan ( 0 - 1 )', '39242', '2 - 2'], ['april 10', 'phillies 1:15 pm', '13 - 4', 'carpenter ( 1 - 1 )', '37971', '2 - 3'], ['april 12', 'reds 7:45 pm', '5 - 1', 'harang ( 1 - 1 )', '33617', '3 - 3']] |
carlos kirmayr | https://en.wikipedia.org/wiki/Carlos_Kirmayr | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17262467-1.html.csv | majority | carlos kirmayr played on clay in most of the tournaments that he participated in . | {'scope': 'all', 'col': '4', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'clay', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'surface', 'clay'], 'result': True, 'ind': 0, 'tointer': 'for the surface records of all rows , all of them fuzzily match to clay .', 'tostr': 'all_eq { all_rows ; surface ; clay } = true'} | all_eq { all_rows ; surface ; clay } = true | for the surface records of all rows , all of them fuzzily match to clay . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'surface_3': 3, 'clay_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'surface_3': 'surface', 'clay_4': 'clay'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'surface_3': [0], 'clay_4': [0]} | ['outcome', 'date', 'championship', 'surface', 'opponent in the final', 'score in the final'] | [['runner - up', '1976', 'santiago , chile', 'clay', 'josé higueras', '7 - 5 , 4 - 6 , 4 - 6'], ['runner - up', '1979', 'cairo , egypt', 'clay', 'peter feigl', '5 - 7 , 6 - 3 , 1 - 6'], ['runner - up', '1980', 'bogotá , colombia', 'clay', 'dominique bedel', '4 - 6 , 6 - 7'], ['runner - up', '1981', 'forest hills , us', 'clay', 'eddie dibbs', '3 - 6 , 2 - 6'], ['runner - up', '1982', 'guarujá , brazil', 'clay', 'van winitsky', '3 - 6 , 3 - 6']] |
1981 vfl season | https://en.wikipedia.org/wiki/1981_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10823950-1.html.csv | superlative | the largest crowd occurred when the venue was vfl park . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '6', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '5', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'crowd'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; crowd }'}, 'venue'], 'result': 'vfl park', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; crowd } ; venue }'}, 'vfl park'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; crowd } ; venue } ; vfl park } = true', 'tointer': 'select the row whose crowd record of all rows is maximum . the venue record of this row is vfl park .'} | eq { hop { argmax { all_rows ; crowd } ; venue } ; vfl park } = true | select the row whose crowd record of all rows is maximum . the venue record of this row is vfl park . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'crowd_5': 5, 'venue_6': 6, 'vfl park_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'crowd_5': 'crowd', 'venue_6': 'venue', 'vfl park_7': 'vfl park'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'crowd_5': [0], 'venue_6': [1], 'vfl park_7': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['north melbourne', '21.19 ( 145 )', 'south melbourne', '12.25 ( 97 )', 'arden street oval', '19437', '28 march 1981'], ['footscray', '16.12 ( 108 )', 'st kilda', '23.19 ( 157 )', 'western oval', '19101', '28 march 1981'], ['melbourne', '16.16 ( 112 )', 'hawthorn', '23.15 ( 153 )', 'mcg', '32202', '28 march 1981'], ['geelong', '10.17 ( 77 )', 'essendon', '10.11 ( 71 )', 'kardinia park', '37303', '28 march 1981'], ['fitzroy', '20.13 ( 133 )', 'collingwood', '22.27 ( 159 )', 'junction oval', '27200', '28 march 1981'], ['carlton', '22.12 ( 144 )', 'richmond', '12.10 ( 82 )', 'vfl park', '56372', '28 march 1981']] |
1945 - 46 huddersfield town f.c. season | https://en.wikipedia.org/wiki/1945%E2%80%9346_Huddersfield_Town_F.C._season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-19730892-1.html.csv | count | 12 players are listed as members of the huddersfield town f.c. during the 1945 - 46 season . | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '12', 'col': '1', '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': '12', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; name } }', 'tointer': 'select the rows whose name record is arbitrary . the number of such rows is 12 .'}, '12'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; name } } ; 12 } = true', 'tointer': 'select the rows whose name record is arbitrary . the number of such rows is 12 .'} | eq { count { filter_all { all_rows ; name } } ; 12 } = true | select the rows whose name record is arbitrary . the number of such rows is 12 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'name_5': 5, '12_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'name_5': 'name', '12_6': '12'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'name_5': [0], '12_6': [2]} | ['name', 'nation', 'position', 'fa cup apps', 'fa cup goals', 'total apps', 'total goals'] | [['graham bailey', 'england', 'df', '2', '0', '2', '0'], ['jeff barker', 'england', 'df', '2', '0', '2', '0'], ['albert bateman', 'england', 'mf', '2', '0', '2', '0'], ['eddie carr', 'england', 'mf', '1', '0', '1', '0'], ['don clegg', 'england', 'gk', '2', '0', '2', '0'], ['jimmy glazzard', 'england', 'fw', '2', '0', '2', '0'], ['george green', 'england', 'df', '2', '0', '2', '0'], ['george howe', 'england', 'df', '1', '0', '1', '0'], ['vic metcalfe', 'england', 'mf', '1', '0', '1', '0'], ['arthur morton', 'england', 'df', '2', '0', '2', '0'], ['joe poole', 'england', 'fw', '1', '0', '1', '0'], ['billy price', 'england', 'fw', '2', '1', '2', '1']] |
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 | count | when the 49ers won during the 1968 season , the attendance exceeded 40000 four times . | {'scope': 'subset', 'criterion': 'greater_than', 'value': '40000', 'result': '4', 'col': '5', 'subset': {'col': '4', 'criterion': 'fuzzily_match', 'value': 'w'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'w'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; result ; w }', 'tointer': 'select the rows whose result record fuzzily matches to w .'}, 'attendance', '40000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose result record fuzzily matches to w . among these rows , select the rows whose attendance record is greater than 40000 .', 'tostr': 'filter_greater { filter_eq { all_rows ; result ; w } ; attendance ; 40000 }'}], 'result': '4', 'ind': 2, 'tostr': 'count { filter_greater { filter_eq { all_rows ; result ; w } ; attendance ; 40000 } }', 'tointer': 'select the rows whose result record fuzzily matches to w . among these rows , select the rows whose attendance record is greater than 40000 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_greater { filter_eq { all_rows ; result ; w } ; attendance ; 40000 } } ; 4 } = true', 'tointer': 'select the rows whose result record fuzzily matches to w . among these rows , select the rows whose attendance record is greater than 40000 . the number of such rows is 4 .'} | eq { count { filter_greater { filter_eq { all_rows ; result ; w } ; attendance ; 40000 } } ; 4 } = true | select the rows whose result record fuzzily matches to w . among these rows , select the rows whose attendance record is greater than 40000 . the number of such rows is 4 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_greater_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'result_6': 6, 'w_7': 7, 'attendance_8': 8, '40000_9': 9, '4_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', 'result_6': 'result', 'w_7': 'w', 'attendance_8': 'attendance', '40000_9': '40000', '4_10': '4'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_greater_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'result_6': [0], 'w_7': [0], 'attendance_8': [1], '40000_9': [1], '4_10': [3]} | ['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']] |
sports in st. louis | https://en.wikipedia.org/wiki/Sports_in_St._Louis | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21564794-3.html.csv | superlative | the st. louis stars won the highest number of championships of former st. louis sports teams . | {'scope': 'all', 'col_superlative': '7', 'row_superlative': '6', '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', 'championships in st louis'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; championships in st louis }'}, 'team'], 'result': 'st louis stars', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; championships in st louis } ; team }'}, 'st louis stars'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; championships in st louis } ; team } ; st louis stars } = true', 'tointer': 'select the row whose championships in st louis record of all rows is maximum . the team record of this row is st louis stars .'} | eq { hop { argmax { all_rows ; championships in st louis } ; team } ; st louis stars } = true | select the row whose championships in st louis record of all rows is maximum . the team record of this row is st louis stars . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'championships in st louis_5': 5, 'team_6': 6, 'st louis stars_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'championships in st louis_5': 'championships in st louis', 'team_6': 'team', 'st louis stars_7': 'st louis stars'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'championships in st louis_5': [0], 'team_6': [1], 'st louis stars_7': [2]} | ['team', 'sport', 'league', 'established', 'began in st louis', 'venue', 'championships in st louis', 'left st louis'] | [['st louis stampede', 'arena football', 'arena football league', '1987', '1994', 'scottrade center', '0', '1995'], ['st louis browns', 'baseball', 'american league', '1894', '1902', "sportsman 's park", '0', '1954'], ['st louis stars', 'baseball', 'negro american league', '1937', '1939', 'stars park', '0', '1939'], ['st louis terriers', 'baseball', 'federal league', '1914', '1914', "handlan 's park", '0', '1915'], ['st louis maroons', 'baseball', 'national league', '1884', '1884', 'union base ball park', '0', '1886'], ['st louis stars', 'baseball', 'negro national league', '1922', '1931', 'stars park', '3 ( 1928 , 1930 , 1931 )', '1931'], ['spirits of st louis', 'basketball', 'american basketball association', '1967', '1974', 'st louis arena', '0', '1976'], ['st louis hawks', 'basketball', 'national basketball association', '1946', '1955', 'kiel auditorium', '1 ( 1958 )', '1968'], ['st louis bombers', 'basketball', 'national basketball association', '1946', '1950', 'st louis arena', '0', '1950'], ['st louis cardinals', 'football', 'national football league', '1898', '1960', 'busch stadium', '0', '1988'], ['st louis all stars', 'football', 'national football league', '1923', '1923', "sportsman 's park", '0', '1923'], ['st louis gunners', 'football', 'national football league', '1931', '1931', 'st louis national guard armory', '0', '1934'], ['missouri river otters', 'hockey', 'united hockey league', '1991', '1999', 'family arena', '0', '2006'], ['st louis eagles', 'hockey', 'national hockey league', '1917', '1934', 'st louis arena', '0', '1936'], ['st louis ambush', 'indoor soccer', 'national professional soccer league', '1984', '1992', 'st louis arena / scottrade center', '1 ( 1995 )', '2000'], ['st louis steamers / st louis storm', 'indoor soccer', 'major indoor soccer league', '1977', '1979', 'st louis arena', '0', '1992']] |
1959 cleveland browns season | https://en.wikipedia.org/wiki/1959_Cleveland_Browns_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10651674-1.html.csv | count | among the 1959 cleveland brown 's games in september , 2 of them had an attendance of more than 30000 . | {'scope': 'subset', 'criterion': 'greater_than', 'value': '30000', 'result': '2', 'col': '5', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'september'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'september'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; september }', 'tointer': 'select the rows whose date record fuzzily matches to september .'}, 'attendance', '30000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to september . among these rows , select the rows whose attendance record is greater than 30000 .', 'tostr': 'filter_greater { filter_eq { all_rows ; date ; september } ; attendance ; 30000 }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_greater { filter_eq { all_rows ; date ; september } ; attendance ; 30000 } }', 'tointer': 'select the rows whose date record fuzzily matches to september . among these rows , select the rows whose attendance record is greater than 30000 . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_greater { filter_eq { all_rows ; date ; september } ; attendance ; 30000 } } ; 2 } = true', 'tointer': 'select the rows whose date record fuzzily matches to september . among these rows , select the rows whose attendance record is greater than 30000 . the number of such rows is 2 .'} | eq { count { filter_greater { filter_eq { all_rows ; date ; september } ; attendance ; 30000 } } ; 2 } = true | select the rows whose date record fuzzily matches to september . among these rows , select the rows whose attendance record is greater than 30000 . the number of such rows is 2 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_greater_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'date_6': 6, 'september_7': 7, 'attendance_8': 8, '30000_9': 9, '2_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', 'date_6': 'date', 'september_7': 'september', 'attendance_8': 'attendance', '30000_9': '30000', '2_10': '2'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_greater_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'date_6': [0], 'september_7': [0], 'attendance_8': [1], '30000_9': [1], '2_10': [3]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'august 12 , 1959', 'pittsburgh steelers', 'l 34 - 20', '27432'], ['2', 'august 22 , 1959', 'detroit lions at akron', 'l 9 - 3', '22654'], ['3', 'august 30 , 1959', 'san francisco 49ers', 'l 17 - 14', '24737'], ['4', 'september 5 , 1959', 'los angeles rams', 'w 27 - 24', '55883'], ['5', 'september 13 , 1959', 'detroit lions', 'l 31 - 28', '33435'], ['6', 'september 19 , 1959', 'chicago bears', 'w 33 - 31', '25316']] |
mark calcavecchia | https://en.wikipedia.org/wiki/Mark_Calcavecchia | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1544297-7.html.csv | comparative | mark calcavecchia had more top 10 finishes in the masters tournament than in the us open . | {'row_1': '1', 'row_2': '2', 'col': '3', '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', 'tournament', 'masters tournament'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tournament record fuzzily matches to masters tournament .', 'tostr': 'filter_eq { all_rows ; tournament ; masters tournament }'}, 'top - 5'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; tournament ; masters tournament } ; top - 5 }', 'tointer': 'select the rows whose tournament record fuzzily matches to masters tournament . take the top - 5 record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'us open'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose tournament record fuzzily matches to us open .', 'tostr': 'filter_eq { all_rows ; tournament ; us open }'}, 'top - 5'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; tournament ; us open } ; top - 5 }', 'tointer': 'select the rows whose tournament record fuzzily matches to us open . take the top - 5 record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; tournament ; masters tournament } ; top - 5 } ; hop { filter_eq { all_rows ; tournament ; us open } ; top - 5 } } = true', 'tointer': 'select the rows whose tournament record fuzzily matches to masters tournament . take the top - 5 record of this row . select the rows whose tournament record fuzzily matches to us open . take the top - 5 record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; tournament ; masters tournament } ; top - 5 } ; hop { filter_eq { all_rows ; tournament ; us open } ; top - 5 } } = true | select the rows whose tournament record fuzzily matches to masters tournament . take the top - 5 record of this row . select the rows whose tournament record fuzzily matches to us open . take the top - 5 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, 'tournament_7': 7, 'masters tournament_8': 8, 'top - 5_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'tournament_11': 11, 'us open_12': 12, 'top - 5_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', 'tournament_7': 'tournament', 'masters tournament_8': 'masters tournament', 'top - 5_9': 'top - 5', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'tournament_11': 'tournament', 'us open_12': 'us open', 'top - 5_13': 'top - 5'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'tournament_7': [0], 'masters tournament_8': [0], 'top - 5_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'tournament_11': [1], 'us open_12': [1], 'top - 5_13': [3]} | ['tournament', 'wins', 'top - 5', 'top - 10', 'top - 25', 'events', 'cuts made'] | [['masters tournament', '0', '2', '2', '10', '18', '13'], ['us open', '0', '0', '0', '6', '20', '10'], ['the open championship', '1', '1', '3', '9', '27', '19'], ['pga championship', '0', '1', '2', '4', '21', '14'], ['totals', '1', '4', '7', '29', '86', '56']] |
2007 open championship | https://en.wikipedia.org/wiki/2007_Open_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12278571-4.html.csv | aggregation | in the 2007 open championship , athletes from the united states had an average score of 68.67 . | {'scope': 'subset', 'col': '4', 'type': 'average', 'result': '68.67', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'united states'}} | {'func': 'round_eq', 'args': [{'func': 'avg', '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 .'}, 'score'], 'result': '68.67', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; country ; united states } ; score }'}, '68.67'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; country ; united states } ; score } ; 68.67 } = true', 'tointer': 'select the rows whose country record fuzzily matches to united states . the average of the score record of these rows is 68.67 .'} | round_eq { avg { filter_eq { all_rows ; country ; united states } ; score } ; 68.67 } = true | select the rows whose country record fuzzily matches to united states . the average of the score record of these rows is 68.67 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'country_5': 5, 'united states_6': 6, 'score_7': 7, '68.67_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'country_5': 'country', 'united states_6': 'united states', 'score_7': 'score', '68.67_8': '68.67'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'country_5': [0], 'united states_6': [0], 'score_7': [1], '68.67_8': [2]} | ['place', 'player', 'country', 'score', 'to par'] | [['1', 'sergio garcía', 'spain', '65', '- 6'], ['2', 'paul mcginley', 'ireland', '67', '- 4'], ['t3', 'markus brier', 'austria', '68', '- 3'], ['t3', 'ángel cabrera', 'argentina', '68', '- 3'], ['t3', 'michael campbell', 'new zealand', '68', '- 3'], ['t3', 'rory mcilroy ( a )', 'northern ireland', '68', '- 3'], ['t3', 'boo weekley', 'united states', '68', '- 3'], ['t8', 'kj choi', 'south korea', '69', '- 2'], ['t8', 'stewart cink', 'united states', '69', '- 2'], ['t8', 'pádraig harrington', 'ireland', '69', '- 2'], ['t8', 'miguel ángel jiménez', 'spain', '69', '- 2'], ['t8', 'tiger woods', 'united states', '69', '- 2']] |
real salt lake | https://en.wikipedia.org/wiki/Real_Salt_Lake | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1053453-2.html.csv | aggregation | for real salt lake the total goals in which the nation was usa was 74 . | {'scope': 'subset', 'col': '5', 'type': 'sum', 'result': '74', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'usa'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nation', 'usa'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; nation ; usa }', 'tointer': 'select the rows whose nation record fuzzily matches to usa .'}, 'goals'], 'result': '74', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; nation ; usa } ; goals }'}, '74'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; nation ; usa } ; goals } ; 74 } = true', 'tointer': 'select the rows whose nation record fuzzily matches to usa . the sum of the goals record of these rows is 74 .'} | round_eq { sum { filter_eq { all_rows ; nation ; usa } ; goals } ; 74 } = true | select the rows whose nation record fuzzily matches to usa . the sum of the goals record of these rows is 74 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'nation_5': 5, 'usa_6': 6, 'goals_7': 7, '74_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'nation_5': 'nation', 'usa_6': 'usa', 'goals_7': 'goals', '74_8': '74'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'nation_5': [0], 'usa_6': [0], 'goals_7': [1], '74_8': [2]} | ['rank', 'player', 'nation', 'games', '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'], ['11', 'jámison olave', 'col', '120', '10', '2008 - 2012'], ['12', 'will johnson', 'can', '114', '9', '2008 - 2012']] |
sport in saint petersburg | https://en.wikipedia.org/wiki/Sport_in_Saint_Petersburg | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12978801-1.html.csv | unique | spartak st petersburg is the only basketball venue in saint petersburg . | {'scope': 'all', 'row': '2', 'col': '3', 'col_other': '1', 'criterion': 'equal', 'value': 'basketball', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'sport', 'basketball'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose sport record fuzzily matches to basketball .', 'tostr': 'filter_eq { all_rows ; sport ; basketball }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; sport ; basketball } }', 'tointer': 'select the rows whose sport record fuzzily matches to basketball . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'sport', 'basketball'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose sport record fuzzily matches to basketball .', 'tostr': 'filter_eq { all_rows ; sport ; basketball }'}, 'club'], 'result': 'spartak st petersburg', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; sport ; basketball } ; club }'}, 'spartak st petersburg'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; sport ; basketball } ; club } ; spartak st petersburg }', 'tointer': 'the club record of this unqiue row is spartak st petersburg .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; sport ; basketball } } ; eq { hop { filter_eq { all_rows ; sport ; basketball } ; club } ; spartak st petersburg } } = true', 'tointer': 'select the rows whose sport record fuzzily matches to basketball . there is only one such row in the table . the club record of this unqiue row is spartak st petersburg .'} | and { only { filter_eq { all_rows ; sport ; basketball } } ; eq { hop { filter_eq { all_rows ; sport ; basketball } ; club } ; spartak st petersburg } } = true | select the rows whose sport record fuzzily matches to basketball . there is only one such row in the table . the club record of this unqiue row is spartak st petersburg . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'sport_7': 7, 'basketball_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'club_9': 9, 'spartak st petersburg_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'sport_7': 'sport', 'basketball_8': 'basketball', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'club_9': 'club', 'spartak st petersburg_10': 'spartak st petersburg'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'sport_7': [0], 'basketball_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'club_9': [2], 'spartak st petersburg_10': [3]} | ['club', 'league', 'sport', 'venue', 'established'] | [['zenit st petersburg', 'rfpl', 'football', 'petrovsky stadium', '1926'], ['spartak st petersburg', 'pbl', 'basketball', 'yubileyny sports palace', '1935'], ['avtomobilist st petesburg', 'vsl', 'volleyball', 'platonov volleyball academy', '1935'], ['ska st petersburg', 'khl', 'ice hockey', 'ice palace', '1946'], ['politekh st petersburg', 'mfsl', 'futsal', 'kalinin district mfok', '1995'], ['petrotrest st petersburg', 'fnl', 'football', 'msa petrovsky', '2001'], ['ska - 1946 st petersburg', 'mhl', 'ice hockey', 'msa yubileyny', '2009'], ['serebryanye lvy', 'mhl', 'ice hockey', 'spartak ice palace', '2010']] |
1994 miami dolphins season | https://en.wikipedia.org/wiki/1994_Miami_Dolphins_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16023821-1.html.csv | superlative | the miami dolphins ' game on october 9 had the most attendance of their 1994 season . | {'scope': 'all', 'col_superlative': '6', '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 }'}, 'date'], 'result': 'october 9 , 1994', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; attendance } ; date }'}, 'october 9 , 1994'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; attendance } ; date } ; october 9 , 1994 } = true', 'tointer': 'select the row whose attendance record of all rows is maximum . the date record of this row is october 9 , 1994 .'} | eq { hop { argmax { all_rows ; attendance } ; date } ; october 9 , 1994 } = true | select the row whose attendance record of all rows is maximum . the date record of this row is october 9 , 1994 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, 'date_6': 6, 'october 9 , 1994_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', 'date_6': 'date', 'october 9 , 1994_7': 'october 9 , 1994'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], 'date_6': [1], 'october 9 , 1994_7': [2]} | ['week', 'date', 'opponent', 'result', 'tv time', 'attendance'] | [['1', 'september 4 , 1994', 'new england patriots', 'w 39 - 35', 'nbc 4:15 pm', '71023'], ['2', 'september 11 , 1994', 'green bay packers', 'w 24 - 14', 'nbc 1:00 pm', '55011'], ['3', 'september 18 , 1994', 'new york jets', 'w 28 - 14', 'nbc 1:00 pm', '68977'], ['4', 'september 25 , 1994', 'minnesota vikings', 'l 38 - 35', 'nbc 1:00 pm', '64035'], ['5', 'october 2 , 1994', 'cincinnati bengals', 'w 23 - 7', 'tnt 8:15 pm', '55056'], ['6', 'october 9 , 1994', 'buffalo bills', 'l 21 - 11', 'nbc 1:00 pm', '79491'], ['7', 'october 16 , 1994', 'los angeles raiders', 'w 20 - 17', 'nbc 1:00 pm', '70112'], ['9', 'october 30 , 1994', 'new england patriots', 'w 23 - 3', 'nbc 4:15 pm', '59167'], ['10', 'november 6 , 1994', 'indianapolis colts', 'w 22 - 21', 'nbc 1:00 pm', '71158'], ['11', 'november 13 , 1994', 'chicago bears', 'l 17 - 14', 'fox 1:00 pm', '64871'], ['12', 'november 20 , 1994', 'pittsburgh steelers', 'l 16 - 13', 'nbc 1:00 pm', '59148'], ['13', 'november 27 , 1994', 'new york jets', 'w 28 - 24', 'nbc 4:15 pm', '75606'], ['14', 'december 4 , 1994', 'buffalo bills', 'l 42 - 31', 'espn 8:15 pm', '69538'], ['15', 'december 12 , 1994', 'kansas city chiefs', 'w 45 - 28', 'abc 9:00 pm', '71578'], ['16', 'december 18 , 1994', 'indianapolis colts', 'l 10 - 6', 'nbc 1:00 pm', '58867'], ['17', 'december 25 , 1994', 'detroit lions', 'w 27 - 20', 'espn 8:15 pm', '70980']] |
1961 ohio state buckeyes football team | https://en.wikipedia.org/wiki/1961_Ohio_State_Buckeyes_football_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17814506-1.html.csv | aggregation | the average rank for the 1961 ohio state buckeyes football team was 4.89 . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '4.89', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'rank'], 'result': '4.89', 'ind': 0, 'tostr': 'avg { all_rows ; rank }'}, '4.89'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; rank } ; 4.89 } = true', 'tointer': 'the average of the rank record of all rows is 4.89 .'} | round_eq { avg { all_rows ; rank } ; 4.89 } = true | the average of the rank record of all rows is 4.89 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'rank_4': 4, '4.89_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'rank_4': 'rank', '4.89_5': '4.89'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'rank_4': [0], '4.89_5': [1]} | ['date', 'opponent', 'rank', 'site', 'result', 'attendance'] | [['september 30', 'texas christian', '3', 'ohio stadium columbus , oh', 't7 - 7', '82878'], ['october 7', 'ucla', '8', 'ohio stadium columbus , oh', 'w13 - 3', '82992'], ['october 14', 'illinois', '7', 'ohio stadium columbus , oh', 'w44 - 0', '82374'], ['october 21', 'northwestern', '7', 'dyche stadium evanston , il', 'w10 - 0', '43259'], ['october 28', 'wisconsin', '6', 'camp randall stadium madison , wi', 'w30 - 21', '58411'], ['november 4', '9 iowa', '5', 'ohio stadium columbus , oh', 'w29 - 13', '83795'], ['november 11', 'indiana', '3', 'memorial stadium bloomington , in', 'w16 - 7', '27108'], ['november 18', 'oregon', '3', 'ohio stadium columbus , oh', 'w22 - 12', '82073'], ['november 25', 'michigan', '2', 'michigan stadium ann arbor , mi', 'w50 - 20', '80444']] |
1969 cleveland browns season | https://en.wikipedia.org/wiki/1969_Cleveland_Browns_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10652161-2.html.csv | aggregation | the average attendance in the 1969 browns season was around 50000-53000 fans . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '50000-53000', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '50000-53000', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '50000-53000'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 50000-53000 } = true', 'tointer': 'the average of the attendance record of all rows is 50000-53000 .'} | round_eq { avg { all_rows ; attendance } ; 50000-53000 } = true | the average of the attendance record of all rows is 50000-53000 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '50000-53000_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '50000-53000_5': '50000-53000'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '50000-53000_5': [1]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'august 10 , 1969', 'san francisco 49ers at seattle', 'w 24 - 19', '32219'], ['2', 'august 16 , 1969', 'los angeles rams', 'w 10 - 3', '54937'], ['3', 'august 23 , 1969', 'san diego chargers', 't 19 - 19', '36005'], ['4', 'august 30 , 1969', 'green bay packers', 'l 27 - 17', '85532'], ['5', 'september 6 , 1969', 'washington redskins', 'w 20 - 10', '45994'], ['6', 'september 13 , 1969', 'minnesota vikings at akron', 'l 23 - 16', '28561']] |
1996 u.s. open ( golf ) | https://en.wikipedia.org/wiki/1996_U.S._Open_%28golf%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17162199-5.html.csv | ordinal | steve jones had the second lowest score value in the 1996 u.s. open golf tournament . | {'row': '2', 'col': '4', 'order': '2', 'col_other': '2', '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', 'score', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; score ; 2 }'}, 'player'], 'result': 'steve jones', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; score ; 2 } ; player }'}, 'steve jones'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; score ; 2 } ; player } ; steve jones } = true', 'tointer': 'select the row whose score record of all rows is 2nd minimum . the player record of this row is steve jones .'} | eq { hop { nth_argmin { all_rows ; score ; 2 } ; player } ; steve jones } = true | select the row whose score record of all rows is 2nd minimum . the player record of this row is steve jones . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'score_5': 5, '2_6': 6, 'player_7': 7, 'steve jones_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', 'score_5': 'score', '2_6': '2', 'player_7': 'player', 'steve jones_8': 'steve jones'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'score_5': [0], '2_6': [0], 'player_7': [1], 'steve jones_8': [2]} | ['place', 'player', 'country', 'score', 'to par'] | [['1', 'tom lehman', 'united states', '71 + 72 + 65 = 208', '- 2'], ['2', 'steve jones', 'united states', '74 + 66 + 69 = 209', '- 1'], ['t3', 'davis love iii', 'united states', '71 + 69 + 70 = 210', 'e'], ['t3', 'john morse', 'united states', '68 + 74 + 68 = 210', 'e'], ['t3', 'frank nobilo', 'new zealand', '69 + 71 + 70 = 210', 'e'], ['t6', 'woody austin', 'united states', '67 + 72 + 72 = 211', '+ 1'], ['t6', 'ernie els', 'south africa', '72 + 67 + 72 = 211', '+ 1'], ['t6', 'jim furyk', 'united states', '72 + 69 + 70 = 211', '+ 1'], ['t6', 'colin montgomerie', 'scotland', '70 + 72 + 69 = 211', '+ 1'], ['t6', 'sam torrance', 'scotland', '71 + 69 + 71 = 211', '+ 1']] |
spaceport | https://en.wikipedia.org/wiki/Spaceport | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-179174-2.html.csv | aggregation | in the list of spaceport and flights the total orbital launches from baikonur cosmodrome , kazakhstan is 123 . | {'scope': 'subset', 'col': '5', 'type': 'sum', 'result': '123', 'subset': {'col': '1', 'criterion': 'equal', 'value': 'baikonur cosmodrome , kazakhstan'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'spaceport', 'baikonur cosmodrome , kazakhstan'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; spaceport ; baikonur cosmodrome , kazakhstan }', 'tointer': 'select the rows whose spaceport record fuzzily matches to baikonur cosmodrome , kazakhstan .'}, 'flights'], 'result': '123', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; spaceport ; baikonur cosmodrome , kazakhstan } ; flights }'}, '123'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; spaceport ; baikonur cosmodrome , kazakhstan } ; flights } ; 123 } = true', 'tointer': 'select the rows whose spaceport record fuzzily matches to baikonur cosmodrome , kazakhstan . the sum of the flights record of these rows is 123 .'} | round_eq { sum { filter_eq { all_rows ; spaceport ; baikonur cosmodrome , kazakhstan } ; flights } ; 123 } = true | select the rows whose spaceport record fuzzily matches to baikonur cosmodrome , kazakhstan . the sum of the flights record of these rows is 123 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'spaceport_5': 5, 'baikonur cosmodrome , kazakhstan_6': 6, 'flights_7': 7, '123_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'spaceport_5': 'spaceport', 'baikonur cosmodrome , kazakhstan_6': 'baikonur cosmodrome , kazakhstan', 'flights_7': 'flights', '123_8': '123'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'spaceport_5': [0], 'baikonur cosmodrome , kazakhstan_6': [0], 'flights_7': [1], '123_8': [2]} | ['spaceport', 'launch complex', 'launcher', 'spacecraft', 'flights', 'years'] | [['baikonur cosmodrome , kazakhstan', 'site 1', 'vostok ( r )', 'vostok 1 - 6', '6 orbital', '1961 - 1963'], ['baikonur cosmodrome , kazakhstan', 'site 1', 'voskhod ( r )', 'voskhod 1 - 2', '2 orbital', '1964 - 1965'], ['baikonur cosmodrome , kazakhstan', 'site 1 , 31', 'soyuz ( r )', 'soyuz 1 - 40', '37 orbital', '1967 - 1981'], ['baikonur cosmodrome , kazakhstan', 'site 1 , 31', 'soyuz ( r )', 'soyuz - t 2 - 15', '14 orbital', '1980 - 1986'], ['baikonur cosmodrome , kazakhstan', 'site 1', 'soyuz ( r )', 'soyuz - tm 2 - 34', '33 orbital', '1987 - 2002'], ['baikonur cosmodrome , kazakhstan', 'site 1', 'soyuz ( r )', 'soyuz - tma 1 - 22', '22 orbital', '2002 - 2011'], ['baikonur cosmodrome , kazakhstan', 'site 1', 'soyuz ( r )', 'soyuz tma - m 1 - 9', '9 orbital', '2010 -'], ['cape canaveral afs , florida , usa', 'lc5', 'redstone', 'mercury 3 - 4', '2 sub - o', '1961 - 1961'], ['cape canaveral afs , florida , usa', 'lc14', 'atlas', 'mercury 6 - 9', '4 orbital', '1962 - 1963'], ['cape canaveral afs , florida , usa', 'lc19', 'titan ii', 'gemini 3 - 12', '10 orbital', '1965 - 1966'], ['cape canaveral afs , florida , usa', 'lc34', 'saturn ib', 'apollo 7', '1 orbital', '1968 - 1968'], ['kennedy space center , florida , usa', 'lc39', 'saturn v', 'apollo 8 - 17', '10 lun / or', '1968 - 1970'], ['kennedy space center , florida , usa', 'lc39', 'saturn ib', 'skylab 2 - 4', '3 orbital', '1973 - 1974'], ['kennedy space center , florida , usa', 'lc39', 'saturn ib', 'apollo - soyuz', '1 orbital', '1975 - 1975'], ['kennedy space center , florida , usa', 'lc39', 'sts 1 - 135', 'space shuttle', '134 orbital', '1981 - 2011']] |
1970 - 71 coupe de france | https://en.wikipedia.org/wiki/1970%E2%80%9371_Coupe_de_France | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16305580-2.html.csv | count | a total of three games in the 1970 - 71 coupe de france 1st round endded with a score of 2 - 0 . | {'scope': 'all', 'criterion': 'equal', 'value': '2 - 0', 'result': '3', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '1st round', '2 - 0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 1st round record fuzzily matches to 2 - 0 .', 'tostr': 'filter_eq { all_rows ; 1st round ; 2 - 0 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; 1st round ; 2 - 0 } }', 'tointer': 'select the rows whose 1st round record fuzzily matches to 2 - 0 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; 1st round ; 2 - 0 } } ; 3 } = true', 'tointer': 'select the rows whose 1st round record fuzzily matches to 2 - 0 . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; 1st round ; 2 - 0 } } ; 3 } = true | select the rows whose 1st round record fuzzily matches to 2 - 0 . 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, '1st round_5': 5, '2 - 0_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', '1st round_5': '1st round', '2 - 0_6': '2 - 0', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], '1st round_5': [0], '2 - 0_6': [0], '3_7': [2]} | ['team 1', 'score', 'team 2', '1st round', '2nd round'] | [['girondins de bordeaux ( d1 )', '3 - 2', 'as nancy ( d1 )', '2 - 0', '1 - 2'], ['fc sochaux - montbéliard ( d1 )', '3 - 1', 'fc nantes ( d1 )', '2 - 0', '1 - 1'], ['olympique de marseille ( d1 )', '2 - 0', 'red star ( d1 )', '1 - 0', '1 - 0'], ['as saint - étienne ( d1 )', '2 - 3', 'olympique lyonnais ( d1 )', '2 - 0', '0 - 3'], ['stade rennais ( d1 )', '2 - 1', 'fc mantes ( d3 )', '1 - 0', '1 - 1'], ['montpellier hsc ( d2 )', '2 - 5', 'as monaco ( d2 )', '1 - 4', '1 - 1'], ['rc joinville ( d2 )', '1 - 6', 'aaj blois ( d2 )', '0 - 1', '1 - 5'], ['rapid de menton ( d3 )', '2 - 3', 'usl dunkerque ( d2 )', '2 - 1', '0 - 2']] |
gold coast titans | https://en.wikipedia.org/wiki/Gold_Coast_Titans | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1613020-1.html.csv | unique | the 2013 nrl season was the only competition that the gold coast titans had a 9/16 ladder position . | {'scope': 'all', 'row': '7', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': '9 / 16', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'ladder position', '9 / 16'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose ladder position record fuzzily matches to 9 / 16 .', 'tostr': 'filter_eq { all_rows ; ladder position ; 9 / 16 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; ladder position ; 9 / 16 } }', 'tointer': 'select the rows whose ladder position record fuzzily matches to 9 / 16 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'ladder position', '9 / 16'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose ladder position record fuzzily matches to 9 / 16 .', 'tostr': 'filter_eq { all_rows ; ladder position ; 9 / 16 }'}, 'competition'], 'result': '2013 nrl season', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; ladder position ; 9 / 16 } ; competition }'}, '2013 nrl season'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; ladder position ; 9 / 16 } ; competition } ; 2013 nrl season }', 'tointer': 'the competition record of this unqiue row is 2013 nrl season .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; ladder position ; 9 / 16 } } ; eq { hop { filter_eq { all_rows ; ladder position ; 9 / 16 } ; competition } ; 2013 nrl season } } = true', 'tointer': 'select the rows whose ladder position record fuzzily matches to 9 / 16 . there is only one such row in the table . the competition record of this unqiue row is 2013 nrl season .'} | and { only { filter_eq { all_rows ; ladder position ; 9 / 16 } } ; eq { hop { filter_eq { all_rows ; ladder position ; 9 / 16 } ; competition } ; 2013 nrl season } } = true | select the rows whose ladder position record fuzzily matches to 9 / 16 . there is only one such row in the table . the competition record of this unqiue row is 2013 nrl season . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'ladder position_7': 7, '9 / 16_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'competition_9': 9, '2013 nrl season_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'ladder position_7': 'ladder position', '9 / 16_8': '9 / 16', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'competition_9': 'competition', '2013 nrl season_10': '2013 nrl season'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'ladder position_7': [0], '9 / 16_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'competition_9': [2], '2013 nrl season_10': [3]} | ['competition', 'ladder position', 'coach', 'captain ( s )', 'details'] | [['2007 nrl season', '12 / 16', 'john cartwright', 'luke bailey scott prince', '2007 gold coast titans season'], ['2008 nrl season', '13 / 16', 'john cartwright', 'luke bailey scott prince', '2008 gold coast titans season'], ['2009 nrl season', '3 / 16', 'john cartwright', 'luke bailey scott prince', '2009 gold coast titans season'], ['2010 nrl season', '4 / 16', 'john cartwright', 'scott prince', '2010 gold coast titans season'], ['2011 nrl season', '16 / 16', 'john cartwright', 'scott prince', '2011 gold coast titans season'], ['2012 nrl season', '11 / 16', 'john cartwright', 'scott prince', '2012 gold coast titans season'], ['2013 nrl season', '9 / 16', 'john cartwright', 'greg bird nate myles', '2013 gold coast titans season'], ['2014 nrl season', '16', 'john cartwright', 'greg bird nate myles', '2014 gold coast titans season']] |
1941 vfl season | https://en.wikipedia.org/wiki/1941_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10807673-8.html.csv | majority | most of the games in round 8 of the 1941 victorian football season season had crowds above 3,000 people . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '3,000', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'crowd', '3,000'], 'result': True, 'ind': 0, 'tointer': 'for the crowd records of all rows , most of them are greater than 3,000 .', 'tostr': 'most_greater { all_rows ; crowd ; 3,000 } = true'} | most_greater { all_rows ; crowd ; 3,000 } = true | for the crowd records of all rows , most of them are greater than 3,000 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'crowd_3': 3, '3,000_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'crowd_3': 'crowd', '3,000_4': '3,000'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'crowd_3': [0], '3,000_4': [0]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['richmond', '10.13 ( 73 )', 'st kilda', '6.11 ( 47 )', 'punt road oval', '6000', '21 june 1941'], ['hawthorn', '6.8 ( 44 )', 'melbourne', '12.12 ( 84 )', 'glenferrie oval', '2000', '21 june 1941'], ['collingwood', '8.12 ( 60 )', 'essendon', '7.10 ( 52 )', 'victoria park', '6000', '21 june 1941'], ['carlton', '10.17 ( 77 )', 'fitzroy', '12.13 ( 85 )', 'princes park', '4000', '21 june 1941'], ['south melbourne', '8.16 ( 64 )', 'north melbourne', '6.6 ( 42 )', 'lake oval', '5000', '21 june 1941'], ['geelong', '10.18 ( 78 )', 'footscray', '13.15 ( 93 )', 'kardinia park', '5000', '21 june 1941']] |
california legislative lgbt caucus | https://en.wikipedia.org/wiki/California_Legislative_LGBT_Caucus | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17769516-1.html.csv | count | for the california legislative lgbt caucus , when the years in assembly include 2008 , there were two times the residence was san francisco . | {'scope': 'subset', 'criterion': 'equal', 'value': 'san francisco', 'result': '2', 'col': '2', 'subset': {'col': '4', 'criterion': 'fuzzily_match', 'value': '2008'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'years in assembly', '2008'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; years in assembly ; 2008 }', 'tointer': 'select the rows whose years in assembly record fuzzily matches to 2008 .'}, 'residence', 'san francisco'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose years in assembly record fuzzily matches to 2008 . among these rows , select the rows whose residence record fuzzily matches to san francisco .', 'tostr': 'filter_eq { filter_eq { all_rows ; years in assembly ; 2008 } ; residence ; san francisco }'}], 'result': '2', 'ind': 2, 'tostr': 'count { filter_eq { filter_eq { all_rows ; years in assembly ; 2008 } ; residence ; san francisco } }', 'tointer': 'select the rows whose years in assembly record fuzzily matches to 2008 . among these rows , select the rows whose residence record fuzzily matches to san francisco . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_eq { all_rows ; years in assembly ; 2008 } ; residence ; san francisco } } ; 2 } = true', 'tointer': 'select the rows whose years in assembly record fuzzily matches to 2008 . among these rows , select the rows whose residence record fuzzily matches to san francisco . the number of such rows is 2 .'} | eq { count { filter_eq { filter_eq { all_rows ; years in assembly ; 2008 } ; residence ; san francisco } } ; 2 } = true | select the rows whose years in assembly record fuzzily matches to 2008 . among these rows , select the rows whose residence record fuzzily matches to san francisco . 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, 'years in assembly_6': 6, '2008_7': 7, 'residence_8': 8, 'san francisco_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', 'years in assembly_6': 'years in assembly', '2008_7': '2008', 'residence_8': 'residence', 'san francisco_9': 'san francisco', '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], 'years in assembly_6': [0], '2008_7': [0], 'residence_8': [1], 'san francisco_9': [1], '2_10': [3]} | ['name', 'residence', 'party', 'years in assembly', 'years in senate'] | [['mark leno', 'san francisco', 'democratic', '2002 - 2008', '2008 - present'], ['cathleen galgiani', 'livingston', 'democratic', '2006 - 2012 galgiani came out in november 2011', '2012 - present'], ['tom ammiano', 'san francisco', 'democratic', '2008 - present', '-'], ['john pérez', 'los angeles', 'democratic', '2008 - present', '-'], ['toni atkins', 'san diego', 'democratic', '2010 - present', '-'], ['rich gordon', 'menlo park', 'democratic', '2010 - present', '-'], ['ricardo lara', 'bell gardens', 'democratic', '2010 - 2012', '2012 - present'], ['susan eggman', 'stockton', 'democratic', '2012 - present', '-']] |
taylor dent | https://en.wikipedia.org/wiki/Taylor_Dent | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1551815-5.html.csv | unique | of all of the tournaments that taylor dent participated in , the only one on a grass surface was on july 2nd , 2002 . | {'scope': 'all', 'row': '1', 'col': '4', 'col_other': '2', 'criterion': 'fuzzily_match', 'value': 'grass', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'surface', 'grass'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose surface record fuzzily matches to grass .', 'tostr': 'filter_eq { all_rows ; surface ; grass }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; surface ; grass } }', 'tointer': 'select the rows whose surface record fuzzily matches to grass . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'surface', 'grass'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose surface record fuzzily matches to grass .', 'tostr': 'filter_eq { all_rows ; surface ; grass }'}, 'date ( final )'], 'result': 'july 7 2002', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; surface ; grass } ; date ( final ) }'}, 'july 7 2002'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; surface ; grass } ; date ( final ) } ; july 7 2002 }', 'tointer': 'the date ( final ) record of this unqiue row is july 7 2002 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; surface ; grass } } ; eq { hop { filter_eq { all_rows ; surface ; grass } ; date ( final ) } ; july 7 2002 } } = true', 'tointer': 'select the rows whose surface record fuzzily matches to grass . there is only one such row in the table . the date ( final ) record of this unqiue row is july 7 2002 .'} | and { only { filter_eq { all_rows ; surface ; grass } } ; eq { hop { filter_eq { all_rows ; surface ; grass } ; date ( final ) } ; july 7 2002 } } = true | select the rows whose surface record fuzzily matches to grass . there is only one such row in the table . the date ( final ) record of this unqiue row is july 7 2002 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'surface_7': 7, 'grass_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date (final)_9': 9, 'july 7 2002_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'surface_7': 'surface', 'grass_8': 'grass', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date (final)_9': 'date ( final )', 'july 7 2002_10': 'july 7 2002'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'surface_7': [0], 'grass_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date (final)_9': [2], 'july 7 2002_10': [3]} | ['outcome', 'date ( final )', 'tournament', 'surface', 'opponent in the final', 'score'] | [['winner', 'july 7 2002', 'newport , united states', 'grass', 'james blake', '6 - 1 , 4 - 6 , 6 - 4'], ['winner', 'february 17 , 2003', 'memphis , united states', 'hard ( i )', 'andy roddick', '6 - 1 , 6 - 4'], ['winner', 'september 22 , 2003', 'bangkok , thailand', 'hard ( i )', 'juan carlos ferrero', '6 - 3 , 7 - 6 ( 7 - 5 )'], ['winner', 'september 29 2003', 'moscow , russia', 'carpet ( i )', 'sargis sargsian', '7 - 6 ( 7 - 5 ) , 6 - 4'], ['runner - up', 'october 10 , 2004', 'tokyo , japan', 'hard', 'jiří novák', '7 - 5 , 1 - 6 , 3 - 6'], ['runner - up', 'january 9 2005', 'adelaide , australia', 'hard', 'joachim johansson', '5 - 7 , 3 - 6'], ['runner - up', 'july 24 2005', 'indianapolis , united states', 'hard', 'robby ginepri', '6 - 4 , 3 - 6 , 0 - 3 , ret']] |
toronto raptors all - time roster | https://en.wikipedia.org/wiki/Toronto_Raptors_all-time_roster | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10015132-11.html.csv | majority | united states is the nationality of all players on the toronto raptors all - time roster . | {'scope': 'all', 'col': '2', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'united states', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'nationality', 'united states'], 'result': True, 'ind': 0, 'tointer': 'for the nationality records of all rows , all of them fuzzily match to united states .', 'tostr': 'all_eq { all_rows ; nationality ; united states } = true'} | all_eq { all_rows ; nationality ; united states } = true | for the nationality records of all rows , all of them fuzzily match to united states . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'nationality_3': 3, 'united states_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'nationality_3': 'nationality', 'united states_4': 'united states'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'nationality_3': [0], 'united states_4': [0]} | ['player', 'nationality', 'position', 'years in toronto', 'school / club team'] | [['antonio lang', 'united states', 'guard - forward', '1999 - 2000', 'duke'], ['voshon lenard', 'united states', 'guard', '2002 - 03', 'minnesota'], ['martin lewis', 'united states', 'guard - forward', '1996 - 97', 'butler cc ( ks )'], ['brad lohaus', 'united states', 'forward - center', '1996', 'iowa'], ['art long', 'united states', 'forward - center', '2002 - 03', 'cincinnati'], ['john long', 'united states', 'guard', '1996 - 97', 'detroit'], ['kyle lowry', 'united states', 'guard', '2012 - present', 'villanova'], ['john lucas iii', 'united states', 'guard', '2012 - 2013', 'oklahoma state']] |
2004 british grand prix | https://en.wikipedia.org/wiki/2004_British_Grand_Prix | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1123631-2.html.csv | superlative | at 2004 british grand prix , rubens barrichello was the slowest ferrari driver . | {'scope': 'subset', 'col_superlative': '4', 'row_superlative': '3', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1,2', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'ferrari'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'constructor', 'ferrari'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; constructor ; ferrari }', 'tointer': 'select the rows whose constructor record fuzzily matches to ferrari .'}, 'time / retired'], 'result': None, 'ind': 1, 'tostr': 'argmin { filter_eq { all_rows ; constructor ; ferrari } ; time / retired }'}, 'driver'], 'result': 'rubens barrichello', 'ind': 2, 'tostr': 'hop { argmin { filter_eq { all_rows ; constructor ; ferrari } ; time / retired } ; driver }'}, 'rubens barrichello'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { argmin { filter_eq { all_rows ; constructor ; ferrari } ; time / retired } ; driver } ; rubens barrichello } = true', 'tointer': 'select the rows whose constructor record fuzzily matches to ferrari . select the row whose time / retired record of these rows is minimum . the driver record of this row is rubens barrichello .'} | eq { hop { argmin { filter_eq { all_rows ; constructor ; ferrari } ; time / retired } ; driver } ; rubens barrichello } = true | select the rows whose constructor record fuzzily matches to ferrari . select the row whose time / retired record of these rows is minimum . the driver record of this row is rubens barrichello . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'argmin_1': 1, 'filter_str_eq_0': 0, 'all_rows_5': 5, 'constructor_6': 6, 'ferrari_7': 7, 'time / retired_8': 8, 'driver_9': 9, 'rubens barrichello_10': 10} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'argmin_1': 'argmin', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_5': 'all_rows', 'constructor_6': 'constructor', 'ferrari_7': 'ferrari', 'time / retired_8': 'time / retired', 'driver_9': 'driver', 'rubens barrichello_10': 'rubens barrichello'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'argmin_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'constructor_6': [0], 'ferrari_7': [0], 'time / retired_8': [1], 'driver_9': [2], 'rubens barrichello_10': [3]} | ['driver', 'constructor', 'laps', 'time / retired', 'grid'] | [['michael schumacher', 'ferrari', '60', '1:24:42.700', '4'], ['kimi räikkönen', 'mclaren - mercedes', '60', '+ 2.130', '1'], ['rubens barrichello', 'ferrari', '60', '+ 3.114', '2'], ['jenson button', 'bar - honda', '60', '+ 10.683', '3'], ['juan pablo montoya', 'williams - bmw', '60', '+ 12.173', '7'], ['giancarlo fisichella', 'sauber - petronas', '60', '+ 12.888', '20'], ['david coulthard', 'mclaren - mercedes', '60', '+ 19.668', '6'], ['mark webber', 'jaguar - cosworth', '60', '+ 23.701', '9'], ['felipe massa', 'sauber - petronas', '60', '+ 24.023', '10'], ['fernando alonso', 'renault', '60', '+ 24.835', '16'], ['takuma sato', 'bar - honda', '60', '+ 33.736', '8'], ['marc gené', 'williams - bmw', '60', '+ 34.303', '11'], ['cristiano da matta', 'toyota', '59', '+ 1 lap', '12'], ['christian klien', 'jaguar - cosworth', '59', '+ 1 lap', '13'], ['nick heidfeld', 'jordan - ford', '59', '+ 1 lap', '15'], ['gianmaria bruni', 'minardi - cosworth', '56', '+ 4 laps', '18'], ['giorgio pantano', 'jordan - ford', '47', 'spin', '14'], ['jarno trulli', 'renault', '39', 'suspension / accident', '5'], ['zsolt baumgartner', 'minardi - cosworth', '29', 'engine', '19'], ['olivier panis', 'toyota', '16', 'fire extinguisher', '17']] |
united states house of representatives elections , 1954 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1954 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342013-12.html.csv | unique | fred e busbey was the only illinois incumbent to lose re-election in the 1954 united states house of representatives elections . | {'scope': 'all', 'row': '2', 'col': '5', 'col_other': '2', 'criterion': 'equal', 'value': 'lost re - election democratic gain', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'lost re - election democratic gain'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to lost re - election democratic gain .', 'tostr': 'filter_eq { all_rows ; result ; lost re - election democratic gain }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; result ; lost re - election democratic gain } }', 'tointer': 'select the rows whose result record fuzzily matches to lost re - election democratic gain . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'lost re - election democratic gain'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to lost re - election democratic gain .', 'tostr': 'filter_eq { all_rows ; result ; lost re - election democratic gain }'}, 'incumbent'], 'result': 'fred e busbey', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; result ; lost re - election democratic gain } ; incumbent }'}, 'fred e busbey'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; result ; lost re - election democratic gain } ; incumbent } ; fred e busbey }', 'tointer': 'the incumbent record of this unqiue row is fred e busbey .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; result ; lost re - election democratic gain } } ; eq { hop { filter_eq { all_rows ; result ; lost re - election democratic gain } ; incumbent } ; fred e busbey } } = true', 'tointer': 'select the rows whose result record fuzzily matches to lost re - election democratic gain . there is only one such row in the table . the incumbent record of this unqiue row is fred e busbey .'} | and { only { filter_eq { all_rows ; result ; lost re - election democratic gain } } ; eq { hop { filter_eq { all_rows ; result ; lost re - election democratic gain } ; incumbent } ; fred e busbey } } = true | select the rows whose result record fuzzily matches to lost re - election democratic gain . there is only one such row in the table . the incumbent record of this unqiue row is fred e busbey . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'result_7': 7, 'lost re - election democratic gain_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'incumbent_9': 9, 'fred e busbey_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'result_7': 'result', 'lost re - election democratic gain_8': 'lost re - election democratic gain', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'incumbent_9': 'incumbent', 'fred e busbey_10': 'fred e busbey'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'result_7': [0], 'lost re - election democratic gain_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'incumbent_9': [2], 'fred e busbey_10': [3]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['illinois 2', "barratt o'hara", 'democratic', '1952', 're - elected', "barratt o'hara ( d ) 61.6 % richard b vail ( r ) 38.4 %"], ['illinois 3', 'fred e busbey', 'republican', '1950', 'lost re - election democratic gain', 'james c murray ( d ) 53.8 % fred e busbey ( r ) 46.2 %'], ['illinois 14', 'chauncey w reed', 'republican', '1934', 're - elected', 'chauncey w reed ( r ) 72.4 % richard plum ( d ) 27.6 %'], ['illinois 15', 'noah m mason', 'republican', '1936', 're - elected', 'noah m mason ( r ) 62.8 % richard a mohan ( d ) 37.2 %'], ['illinois 16', 'leo e allen', 'republican', '1932', 're - elected', 'leo e allen ( r ) unopposed'], ['illinois 20', 'sid simpson', 'republican', '1942', 're - elected', 'sid simpson ( r ) 62.9 % james a barry ( d ) 37.1 %'], ['illinois 24', 'melvin price', 'democratic', '1944', 're - elected', 'melvin price ( d ) 69.2 % john t thomas ( r ) 30.8 %']] |
sophus nielsen | https://en.wikipedia.org/wiki/Sophus_Nielsen | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1580245-1.html.csv | majority | sophus nielsen scored the majority of his international goals in the 1908 olympics competition . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': '1908 olympics', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'competition', '1908 olympics'], 'result': True, 'ind': 0, 'tointer': 'for the competition records of all rows , most of them fuzzily match to 1908 olympics .', 'tostr': 'most_eq { all_rows ; competition ; 1908 olympics } = true'} | most_eq { all_rows ; competition ; 1908 olympics } = true | for the competition records of all rows , most of them fuzzily match to 1908 olympics . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'competition_3': 3, '1908 olympics_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'competition_3': 'competition', '1908 olympics_4': '1908 olympics'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'competition_3': [0], '1908 olympics_4': [0]} | ['date', 'venue', 'score', 'result', 'competition'] | [['1908 - 10 - 19', 'london , england', '9 - 0', '9 - 0', '1908 olympics'], ['1908 - 10 - 22', 'london , england', '1 - 0', '17 - 1', '1908 olympics'], ['1908 - 10 - 22', 'london , england', '2 - 0', '17 - 1', '1908 olympics'], ['1908 - 10 - 22', 'london , england', '3 - 0', '17 - 1', '1908 olympics'], ['1908 - 10 - 22', 'london , england', '6 - 1', '17 - 1', '1908 olympics'], ['1908 - 10 - 22', 'london , england', '7 - 1', '17 - 1', '1908 olympics'], ['1908 - 10 - 22', 'london , england', '8 - 1', '17 - 1', '1908 olympics'], ['1908 - 10 - 22', 'london , england', '9 - 1', '17 - 1', '1908 olympics'], ['1908 - 10 - 22', 'london , england', '11 - 1', '17 - 1', '1908 olympics'], ['1908 - 10 - 22', 'london , england', '12 - 1', '17 - 1', '1908 olympics'], ['1908 - 10 - 22', 'london , england', '15 - 1', '17 - 1', '1908 olympics'], ['1912 - 06 - 30', 'stockholm , sweden', '6 - 0', '7 - 0', '1912 olympics'], ['1912 - 06 - 30', 'stockholm , sweden', '7 - 0', '7 - 0', '1912 olympics'], ['1914 - 05 - 17', 'copenhagen , denmark', '3 - 3', '4 - 3', 'friendly match'], ['1914 - 06 - 05', 'copenhagen , denmark', '2 - 0', '3 - 0', 'friendly match'], ['1917 - 06 - 03', 'copenhagen , denmark', '1 - 1', '1 - 1', 'friendly match']] |
yugoslavian motorcycle grand prix | https://en.wikipedia.org/wiki/Yugoslavian_motorcycle_Grand_Prix | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16801125-1.html.csv | count | in the yugoslavian motorcycle grand prix , when the year is before 1990 , there were three times the 250 cc was sito pons . | {'scope': 'subset', 'criterion': 'equal', 'value': 'sito pons', 'result': '3', 'col': '3', 'subset': {'col': '1', 'criterion': 'less_than', 'value': '1990'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'year', '1990'], 'result': None, 'ind': 0, 'tostr': 'filter_less { all_rows ; year ; 1990 }', 'tointer': 'select the rows whose year record is less than 1990 .'}, '250 cc', 'sito pons'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose year record is less than 1990 . among these rows , select the rows whose 250 cc record fuzzily matches to sito pons .', 'tostr': 'filter_eq { filter_less { all_rows ; year ; 1990 } ; 250 cc ; sito pons }'}], 'result': '3', 'ind': 2, 'tostr': 'count { filter_eq { filter_less { all_rows ; year ; 1990 } ; 250 cc ; sito pons } }', 'tointer': 'select the rows whose year record is less than 1990 . among these rows , select the rows whose 250 cc record fuzzily matches to sito pons . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_eq { filter_less { all_rows ; year ; 1990 } ; 250 cc ; sito pons } } ; 3 } = true', 'tointer': 'select the rows whose year record is less than 1990 . among these rows , select the rows whose 250 cc record fuzzily matches to sito pons . the number of such rows is 3 .'} | eq { count { filter_eq { filter_less { all_rows ; year ; 1990 } ; 250 cc ; sito pons } } ; 3 } = true | select the rows whose year record is less than 1990 . among these rows , select the rows whose 250 cc record fuzzily matches to sito pons . the number of such rows is 3 . | 4 | 4 | {'eq_3': 3, 'result_4': 4, 'count_2': 2, 'filter_str_eq_1': 1, 'filter_less_0': 0, 'all_rows_5': 5, 'year_6': 6, '1990_7': 7, '250 cc_8': 8, 'sito pons_9': 9, '3_10': 10} | {'eq_3': 'eq', 'result_4': 'true', 'count_2': 'count', 'filter_str_eq_1': 'filter_str_eq', 'filter_less_0': 'filter_less', 'all_rows_5': 'all_rows', 'year_6': 'year', '1990_7': '1990', '250 cc_8': '250 cc', 'sito pons_9': 'sito pons', '3_10': '3'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_str_eq_1': [2], 'filter_less_0': [1], 'all_rows_5': [0], 'year_6': [0], '1990_7': [0], '250 cc_8': [1], 'sito pons_9': [1], '3_10': [3]} | ['year', 'track', '250 cc', '500 cc', 'report'] | [['1990', 'rijeka', 'carlos cardãs', 'wayne rainey', 'report'], ['1989', 'rijeka', 'sito pons', 'kevin schwantz', 'report'], ['1988', 'rijeka', 'sito pons', 'wayne gardner', 'report'], ['1987', 'rijeka', 'carlos lavado', 'wayne gardner', 'report'], ['1986', 'rijeka', 'sito pons', 'eddie lawson', 'report'], ['1985', 'rijeka', 'freddie spencer', 'eddie lawson', 'report'], ['1984', 'rijeka', 'manfred herweh', 'freddie spencer', 'report']] |
1951 world wrestling championships | https://en.wikipedia.org/wiki/1951_World_Wrestling_Championships | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16853558-1.html.csv | superlative | in the 1951 world wrestling championships , turkey ranks the highest . | {'scope': 'all', 'col_superlative': '1', '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', 'rank'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; rank }'}, 'nation'], 'result': 'turkey', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; rank } ; nation }'}, 'turkey'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; rank } ; nation } ; turkey } = true', 'tointer': 'select the row whose rank record of all rows is minimum . the nation record of this row is turkey .'} | eq { hop { argmin { all_rows ; rank } ; nation } ; turkey } = true | select the row whose rank record of all rows is minimum . the nation record of this row is turkey . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'rank_5': 5, 'nation_6': 6, 'turkey_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'rank_5': 'rank', 'nation_6': 'nation', 'turkey_7': 'turkey'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'rank_5': [0], 'nation_6': [1], 'turkey_7': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'turkey', '6', '0', '1', '7'], ['2', 'sweden', '2', '1', '3', '6'], ['3', 'finland', '0', '4', '0', '4'], ['4', 'iran', '0', '2', '2', '4'], ['5', 'italy', '0', '1', '1', '2'], ['6', 'west germany', '0', '0', '1', '1'], ['total', 'total', '8', '8', '8', '24']] |
tony lema | https://en.wikipedia.org/wiki/Tony_Lema | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1570274-4.html.csv | unique | the open championship is the only time tony lema recorded a win . | {'scope': 'all', 'row': '3', 'col': '1', 'col_other': '2', 'criterion': 'equal', 'value': 'the open championship', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'the open championship'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tournament record fuzzily matches to the open championship .', 'tostr': 'filter_eq { all_rows ; tournament ; the open championship }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; tournament ; the open championship } }', 'tointer': 'select the rows whose tournament record fuzzily matches to the open championship . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tournament', 'the open championship'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tournament record fuzzily matches to the open championship .', 'tostr': 'filter_eq { all_rows ; tournament ; the open championship }'}, 'wins'], 'result': '1', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; tournament ; the open championship } ; wins }'}, '1'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; tournament ; the open championship } ; wins } ; 1 }', 'tointer': 'the wins record of this unqiue row is 1 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; tournament ; the open championship } } ; eq { hop { filter_eq { all_rows ; tournament ; the open championship } ; wins } ; 1 } } = true', 'tointer': 'select the rows whose tournament record fuzzily matches to the open championship . there is only one such row in the table . the wins record of this unqiue row is 1 .'} | and { only { filter_eq { all_rows ; tournament ; the open championship } } ; eq { hop { filter_eq { all_rows ; tournament ; the open championship } ; wins } ; 1 } } = true | select the rows whose tournament record fuzzily matches to the open championship . there is only one such row in the table . the wins record of this unqiue row is 1 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'tournament_7': 7, 'the open championship_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'wins_9': 9, '1_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'tournament_7': 'tournament', 'the open championship_8': 'the open championship', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'wins_9': 'wins', '1_10': '1'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'tournament_7': [0], 'the open championship_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'wins_9': [2], '1_10': [3]} | ['tournament', 'wins', 'top - 5', 'top - 10', 'top - 25', 'events', 'cuts made'] | [['masters tournament', '0', '1', '2', '4', '4', '4'], ['us open', '0', '2', '3', '4', '6', '5'], ['the open championship', '1', '2', '2', '2', '3', '3'], ['pga championship', '0', '0', '1', '2', '5', '4'], ['totals', '1', '5', '8', '12', '18', '16']] |
2010 - 11 dallas mavericks season | https://en.wikipedia.org/wiki/2010%E2%80%9311_Dallas_Mavericks_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27723526-17.html.csv | superlative | the dallas mavericks ' game on june 9 recorded the most attendance . | {'scope': 'all', 'col_superlative': '8', 'row_superlative': '5', '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', 'location attendance'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; location attendance }'}, 'date'], 'result': 'june 9', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; location attendance } ; date }'}, 'june 9'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; location attendance } ; date } ; june 9 } = true', 'tointer': 'select the row whose location attendance record of all rows is maximum . the date record of this row is june 9 .'} | eq { hop { argmax { all_rows ; location attendance } ; date } ; june 9 } = true | select the row whose location attendance record of all rows is maximum . the date record of this row is june 9 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'location attendance_5': 5, 'date_6': 6, 'june 9_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'location attendance_5': 'location attendance', 'date_6': 'date', 'june 9_7': 'june 9'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'location attendance_5': [0], 'date_6': [1], 'june 9_7': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'series'] | [['1', 'may 31', 'miami', 'l 84 - 92 ( ot )', 'dirk nowitzki ( 27 )', 'shawn marion ( 10 )', 'jason kidd ( 6 )', 'american airlines arena 20003', '0 - 1'], ['2', 'june 2', 'miami', 'w 95 - 93 ( ot )', 'dirk nowitzki ( 24 )', 'dirk nowitzki ( 11 )', 'jason kidd , jason terry ( 5 )', 'american airlines arena 20003', '1 - 1'], ['3', 'june 5', 'miami', 'l 86 - 88 ( ot )', 'dirk nowitzki ( 34 )', 'tyson chandler , dirk nowitzki ( 11 )', 'jason kidd ( 10 )', 'american airlines center 20340', '1 - 2'], ['4', 'june 7', 'miami', 'w 86 - 83 ( ot )', 'dirk nowitzki ( 21 )', 'tyson chandler ( 16 )', 'josé juan barea ( 4 )', 'american airlines center 20430', '2 - 2'], ['5', 'june 9', 'miami', 'w 112 - 103 ( ot )', 'dirk nowitzki ( 29 )', 'tyson chandler ( 7 )', 'jason kidd , jason terry ( 6 )', 'american airlines center 20433', '3 - 2']] |
thai clubs in the afc champions league | https://en.wikipedia.org/wiki/Thai_clubs_in_the_AFC_Champions_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16593799-8.html.csv | aggregation | the average team 1 score of these teams was approximately 2.3 . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '2.3', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'score'], 'result': '2.3', 'ind': 0, 'tostr': 'avg { all_rows ; score }'}, '2.3'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; score } ; 2.3 } = true', 'tointer': 'the average of the score record of all rows is 2.3 .'} | round_eq { avg { all_rows ; score } ; 2.3 } = true | the average of the score record of all rows is 2.3 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'score_4': 4, '2.3_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'score_4': 'score', '2.3_5': '2.3'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'score_4': [0], '2.3_5': [1]} | ['season', 'team 1', 'score', 'team 2', 'venue'] | [['2004', 'krung thai bank', '0 - 2', 'dalian shide', 'thai - japanese stadium , thailand'], ['2004', 'psm makassar', '2 - 3', 'krung thai bank', 'mattoangin stadium , indonesia'], ['2004', 'hoang anh gia lai', '0 - 1', 'krung thai bank', 'pleiku stadium , vietnam'], ['2004', 'krung thai bank', '2 - 2', 'hoang anh gia lai', 'suphachalasai stadium , thailand'], ['2004', 'dalian shide', '3 - 1', 'krung thai bank', "dalian people 's stadium , china pr"], ['2004', 'krung thai bank', '1 - 2', 'psm makassar', 'thai - japanese stadium , thailand'], ['2005', 'krung thai bank', '2 - 1', 'pisico bình ðinh', 'n / a'], ['2005', 'krung thai bank', '0 - 2', "busan i ' park", 'n / a'], ['2005', 'krung thai bank', '2 - 1', 'persebaya surabaya', 'n / a'], ['2005', 'krung thai bank', '0 - 1', 'pisico bình ðinh', 'n / a'], ['2005', 'krung thai bank', '0 - 4', "busan i ' park", 'n / a'], ['2005', 'krung thai bank', '1 - 0', 'persebaya surabaya', 'n / a'], ['2008', 'krung thai bank', '1 - 9', 'kashima antlers', 'chulalongkorn university sports stadium , thailand'], ['2008', 'beijing guoan', '4 - 2', 'krung thai bank', 'beijing fengtai stadium , china pr'], ['2008', 'krung thai bank', '9 - 1', 'nam dinh fc', 'chulalongkorn university sports stadium , thailand'], ['2008', 'nam dinh fc', '2 - 2', 'krung thai bank', 'my dinh national stadium , vietnam'], ['2008', 'kashima antlers', '8 - 1', 'krung thai bank', 'kashima soccer stadium , japan'], ['2008', 'krung thai bank', '5 - 3', 'beijing guoan', 'rajamangala stadium , thailand']] |
list of countries by electricity production from renewable sources | https://en.wikipedia.org/wiki/List_of_countries_by_electricity_production_from_renewable_sources | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17068413-1.html.csv | unique | russia was the only country that did n't produce any solar energy at all . | {'scope': 'all', 'row': '6', 'col': '7', 'col_other': '1', 'criterion': 'equal', 'value': '0', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'solar', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose solar record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; solar ; 0 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; solar ; 0 } }', 'tointer': 'select the rows whose solar record is equal to 0 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'solar', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose solar record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; solar ; 0 }'}, 'country'], 'result': 'russia', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; solar ; 0 } ; country }'}, 'russia'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; solar ; 0 } ; country } ; russia }', 'tointer': 'the country record of this unqiue row is russia .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; solar ; 0 } } ; eq { hop { filter_eq { all_rows ; solar ; 0 } ; country } ; russia } } = true', 'tointer': 'select the rows whose solar record is equal to 0 . there is only one such row in the table . the country record of this unqiue row is russia .'} | and { only { filter_eq { all_rows ; solar ; 0 } } ; eq { hop { filter_eq { all_rows ; solar ; 0 } ; country } ; russia } } = true | select the rows whose solar record is equal to 0 . there is only one such row in the table . the country record of this unqiue row is russia . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'solar_7': 7, '0_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'country_9': 9, 'russia_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'solar_7': 'solar', '0_8': '0', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'country_9': 'country', 'russia_10': 'russia'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'solar_7': [0], '0_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'country_9': [2], 'russia_10': [3]} | ['country', 'year', 'total', 'hydroelectricity', 'wind power', 'biomass and waste', 'solar'] | [['china', '2011', '797.4', '687.1', '73.2', '34', '3'], ['european union', '2010', '699.3', '397.7', '149.1', '123.3', '23.1'], ['united states', '2011', '520.1', '325.1', '119.7', '56.7', '1.81'], ['brazil', '2011', '459.2', '424.3', '2.71', '32.2', '0.0002'], ['canada', '2011', '399.1', '372.6', '19.7', '6.4', '0.43'], ['russia', '2010', '166.6', '163.3', '0.004', '2.8', '0'], ['india', '2011', '162', '131', '26', '4', '1'], ['germany', '2012', '136.1', '21.2', '45.3', '40.9', '28.0'], ['norway', '2011', '121.4', '119.6', '1.29', '0.48', '0.02'], ['japan', '2011', '116.4', '82.5', '4.35', '23.1', '3.80'], ['italy', '2012', '89.759', '43.256', '13.333', '9.281 ( 2010 )', '18.637']] |
1986 dallas cowboys season | https://en.wikipedia.org/wiki/1986_Dallas_Cowboys_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11309481-2.html.csv | majority | most of the dallas cowboys games in the 1986 season saw more than 50000 people in attendance . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '50000', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'attendance', '50000'], 'result': True, 'ind': 0, 'tointer': 'for the attendance records of all rows , most of them are greater than 50000 .', 'tostr': 'most_greater { all_rows ; attendance ; 50000 } = true'} | most_greater { all_rows ; attendance ; 50000 } = true | for the attendance records of all rows , most of them are greater than 50000 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'attendance_3': 3, '50000_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'attendance_3': 'attendance', '50000_4': '50000'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'attendance_3': [0], '50000_4': [0]} | ['week', 'date', 'opponent', 'result', 'game site', 'attendance'] | [['1', 'september 8 , 1986', 'new york giants', 'w 31 - 28', 'texas stadium', '59804'], ['2', 'september 14 , 1986', 'detroit lions', 'w 31 - 7', 'pontiac silverdome', '73812'], ['3', 'september 21 , 1986', 'atlanta falcons', 'l 35 - 37', 'texas stadium', '62880'], ['4', 'september 29 , 1986', 'st louis cardinals', 'w 31 - 7', 'busch memorial stadium', '49077'], ['5', 'october 5 , 1986', 'denver broncos', 'l 14 - 29', 'mile high stadium', '76082'], ['6', 'october 12 , 1986', 'washington redskins', 'w 30 - 6', 'texas stadium', '63264'], ['7', 'october 19 , 1986', 'philadelphia eagles', 'w 17 - 14', 'veterans stadium', '68572'], ['8', 'october 26 , 1986', 'st louis cardinals', 'w 37 - 6', 'texas stadium', '60756'], ['9', 'november 2 , 1986', 'new york giants', 'l 14 - 17', 'giants stadium', '74871'], ['10', 'november 9 , 1986', 'los angeles raiders', 'l 13 - 17', 'texas stadium', '61706'], ['11', 'november 16 , 1986', 'san diego chargers', 'w 24 - 21', 'jack murphy stadium', '55622'], ['12', 'november 23 , 1986', 'washington redskins', 'l 14 - 41', 'rfk stadium', '55642'], ['13', 'november 27 , 1986', 'seattle seahawks', 'l 14 - 31', 'texas stadium', '58020'], ['14', 'december 7 , 1986', 'los angeles rams', 'l 10 - 29', 'anaheim stadium', '64949'], ['15', 'december 14 , 1986', 'philadelphia eagles', 'l 21 - 23', 'texas stadium', '46117'], ['16', 'december 21 , 1986', 'chicago bears', 'l 10 - 24', 'texas stadium', '57256']] |
list of rampage killers | https://en.wikipedia.org/wiki/List_of_rampage_killers | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17794738-6.html.csv | count | there are a total of 6 perpetrators from the philippines . | {'scope': 'all', 'criterion': 'equal', 'value': 'philippines', 'result': '6', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'philippines'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to philippines .', 'tostr': 'filter_eq { all_rows ; country ; philippines }'}], 'result': '6', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; country ; philippines } }', 'tointer': 'select the rows whose country record fuzzily matches to philippines . the number of such rows is 6 .'}, '6'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; country ; philippines } } ; 6 } = true', 'tointer': 'select the rows whose country record fuzzily matches to philippines . the number of such rows is 6 .'} | eq { count { filter_eq { all_rows ; country ; philippines } } ; 6 } = true | select the rows whose country record fuzzily matches to philippines . 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, 'country_5': 5, 'philippines_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', 'country_5': 'country', 'philippines_6': 'philippines', '6_7': '6'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'country_5': [0], 'philippines_6': [0], '6_7': [2]} | ['perpetrator', 'location', 'country', 'killed', 'injured'] | [['bryant , martin john , 28', 'port arthur , tas', 'australia', '35', '23'], ['unknown', 'siquijor', 'philippines', '32', '0.0'], ['wirjo , 42', 'banjarsari', 'indonesia', '20', '12'], ['formentera , arsenio', 'palompon', 'philippines', '17', '0.0'], ['hodeng', 'kampong tankulu', 'indonesia', '16', '01 1'], ['gz', 'gz', 'iz', '100.9', '100.9'], ['salazar , domingo , 42', 'san nicolas', 'philippines', '16', '01 1'], ['unknown', 'ternate', 'indonesia', '15', '04 4'], ['basobas , florentino', 'quezon , palawan', 'philippines', '15', '04 4'], ['antakin', 'kaningow', 'malaysia', '15', '03 3'], ['pusok anak ngaik , 28', 'kampong bukit merah', 'malaysia', '14', '04 4'], ['sz', 'qz', 'nz', '100.9', '100.9'], ['unknown', 'borneo', 'indonesia', '14', '0.0'], ['unknown', 'gondang', 'indonesia', '13', '03 3'], ['gray , david malcolm , 33', 'aramoana', 'new zealand', '13', '03 3'], ['kalinga boli', 'tagan', 'philippines', '13', '0.0'], ['two unknown men', 'zamboanga', 'philippines', '12', '14']] |
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 | comparative | in the set of the highest-grossing bollywood films , agneepath has a lower lifetime india distributor share than dabangg 2 . | {'row_1': '9', 'row_2': '5', 'col': '5', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'movie', 'agneepath'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose movie record fuzzily matches to agneepath .', 'tostr': 'filter_eq { all_rows ; movie ; agneepath }'}, 'lifetime india distributor share'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; movie ; agneepath } ; lifetime india distributor share }', 'tointer': 'select the rows whose movie record fuzzily matches to agneepath . take the lifetime india distributor share record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'movie', 'dabangg 2'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose movie record fuzzily matches to dabangg 2 .', 'tostr': 'filter_eq { all_rows ; movie ; dabangg 2 }'}, 'lifetime india distributor share'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; movie ; dabangg 2 } ; lifetime india distributor share }', 'tointer': 'select the rows whose movie record fuzzily matches to dabangg 2 . take the lifetime india distributor share record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; movie ; agneepath } ; lifetime india distributor share } ; hop { filter_eq { all_rows ; movie ; dabangg 2 } ; lifetime india distributor share } } = true', 'tointer': 'select the rows whose movie record fuzzily matches to agneepath . take the lifetime india distributor share record of this row . select the rows whose movie record fuzzily matches to dabangg 2 . take the lifetime india distributor share record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; movie ; agneepath } ; lifetime india distributor share } ; hop { filter_eq { all_rows ; movie ; dabangg 2 } ; lifetime india distributor share } } = true | select the rows whose movie record fuzzily matches to agneepath . take the lifetime india distributor share record of this row . select the rows whose movie record fuzzily matches to dabangg 2 . take the lifetime india distributor share 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, 'movie_7': 7, 'agneepath_8': 8, 'lifetime india distributor share_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'movie_11': 11, 'dabangg 2_12': 12, 'lifetime india distributor share_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', 'movie_7': 'movie', 'agneepath_8': 'agneepath', 'lifetime india distributor share_9': 'lifetime india distributor share', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'movie_11': 'movie', 'dabangg 2_12': 'dabangg 2', 'lifetime india distributor share_13': 'lifetime india distributor share'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'movie_7': [0], 'agneepath_8': [0], 'lifetime india distributor share_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'movie_11': [1], 'dabangg 2_12': [1], 'lifetime india distributor share_13': [3]} | ['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']] |
2007 - 08 los angeles kings season | https://en.wikipedia.org/wiki/2007%E2%80%9308_Los_Angeles_Kings_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11821711-5.html.csv | majority | during the 2007 - 08 los angeles kings season most of their home games had an attendance of of 18118 . | {'scope': 'subset', 'col': '6', 'most_or_all': 'most', 'criterion': 'equal', 'value': '18118', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'los angeles'}} | {'func': 'most_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home', 'los angeles'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; home ; los angeles }', 'tointer': 'select the rows whose home record fuzzily matches to los angeles .'}, 'attendance', '18118'], 'result': True, 'ind': 1, 'tointer': 'select the rows whose home record fuzzily matches to los angeles . for the attendance records of these rows , most of them are equal to 18118 .', 'tostr': 'most_eq { filter_eq { all_rows ; home ; los angeles } ; attendance ; 18118 } = true'} | most_eq { filter_eq { all_rows ; home ; los angeles } ; attendance ; 18118 } = true | select the rows whose home record fuzzily matches to los angeles . for the attendance records of these rows , most of them are equal to 18118 . | 2 | 2 | {'most_eq_1': 1, 'result_2': 2, 'filter_str_eq_0': 0, 'all_rows_3': 3, 'home_4': 4, 'los angeles_5': 5, 'attendance_6': 6, '18118_7': 7} | {'most_eq_1': 'most_eq', 'result_2': 'true', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_3': 'all_rows', 'home_4': 'home', 'los angeles_5': 'los angeles', 'attendance_6': 'attendance', '18118_7': '18118'} | {'most_eq_1': [2], 'result_2': [], 'filter_str_eq_0': [1], 'all_rows_3': [0], 'home_4': [0], 'los angeles_5': [0], 'attendance_6': [1], '18118_7': [1]} | ['date', 'visitor', 'score', 'home', 'decision', 'attendance', 'record'] | [['november 2', 'los angeles', '5 - 2', 'san jose', 'aubin', '17496', '7 - 7 - 0'], ['november 3', 'san jose', '3 - 1', 'los angeles', 'labarbera', '18118', '7 - 8 - 0'], ['november 10', 'dallas', '5 - 6', 'los angeles', 'aubin', '18118', '8 - 8 - 0'], ['november 13', 'los angeles', '3 - 4', 'anaheim', 'labarbera', '17174', '8 - 8 - 1'], ['november 15', 'anaheim', '6 - 3', 'los angeles', 'aubin', '18118', '8 - 9 - 1'], ['november 17', 'phoenix', '1 - 0', 'los angeles', 'labarbera', '15659', '8 - 10 - 1'], ['november 19', 'los angeles', '0 - 3', 'dallas', 'labarbera', '17208', '8 - 11 - 1'], ['november 21', 'los angeles', '1 - 4', 'phoenix', 'labarbera', '12161', '8 - 12 - 1'], ['november 24', 'los angeles', '2 - 1', 'san jose', 'labarbera', '17496', '9 - 12 - 1'], ['november 25', 'los angeles', '2 - 3', 'anaheim', 'labarbera', '17174', '9 - 13 - 1'], ['november 28', 'los angeles', '3 - 2', 'san jose', 'labarbera', '17071', '10 - 13 - 1']] |
galatasaray s.k. ( superleague formula team ) | https://en.wikipedia.org/wiki/Galatasaray_S.K._%28Superleague_Formula_team%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23293785-3.html.csv | comparative | for galatasaray s.k. , the race in france took place one month before the one in belgium . | {'row_1': '1', 'row_2': '2', 'col': '4', 'col_other': '2', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'france'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose country record fuzzily matches to france .', 'tostr': 'filter_eq { all_rows ; country ; france }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; country ; france } ; date }', 'tointer': 'select the rows whose country record fuzzily matches to france . take the date record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'country', 'belgium'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose country record fuzzily matches to belgium .', 'tostr': 'filter_eq { all_rows ; country ; belgium }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; country ; belgium } ; date }', 'tointer': 'select the rows whose country record fuzzily matches to belgium . take the date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; country ; france } ; date } ; hop { filter_eq { all_rows ; country ; belgium } ; date } } = true', 'tointer': 'select the rows whose country record fuzzily matches to france . take the date record of this row . select the rows whose country record fuzzily matches to belgium . take the date record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; country ; france } ; date } ; hop { filter_eq { all_rows ; country ; belgium } ; date } } = true | select the rows whose country record fuzzily matches to france . take the date record of this row . select the rows whose country record fuzzily matches to belgium . 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, 'country_7': 7, 'france_8': 8, 'date_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'country_11': 11, 'belgium_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', 'country_7': 'country', 'france_8': 'france', 'date_9': 'date', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'country_11': 'country', 'belgium_12': 'belgium', 'date_13': 'date'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'country_7': [0], 'france_8': [0], 'date_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'country_11': [1], 'belgium_12': [1], 'date_13': [3]} | ['sf round', 'country', 'location', 'date', 'driver', 'race 1 ( pts )', 'race 2 ( pts )', 'race 3', 'race total ( pts )'] | [['1', 'france', 'circuit de nevers magny - cours', '28 june 2009', 'duncan tappy', '32', '16', 'dnq', '48'], ['2', 'belgium', 'zolder', '19 july 2009', 'duncan tappy', '20', '7', 'n / a', '75'], ['3', 'england', 'donington park', '2 august 2009', 'scott mansell', '12', '14', 'dnq', '101'], ['4', 'portugal', 'estoril circuit', '6 september 2009', 'ho pin tung', '17', '7', 'dnq', '133'], ['5', 'italy', 'autodromo nazionale monza', '4 october 2009', 'ho pin tung', '8', '7', 'n / a', '182']] |
lner thompson class b1 | https://en.wikipedia.org/wiki/LNER_Thompson_Class_B1 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2079664-3.html.csv | majority | the majority of lner thompson class b1 locomotive models were taken into deptal stock in 1963 . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': '1963', 'subset': None} | {'func': 'most_eq', 'args': ['all_rows', 'taken into deptal stock', '1963'], 'result': True, 'ind': 0, 'tointer': 'for the taken into deptal stock records of all rows , most of them are equal to 1963 .', 'tostr': 'most_eq { all_rows ; taken into deptal stock ; 1963 } = true'} | most_eq { all_rows ; taken into deptal stock ; 1963 } = true | for the taken into deptal stock records of all rows , most of them are equal to 1963 . | 1 | 1 | {'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'taken into deptal stock_3': 3, '1963_4': 4} | {'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'taken into deptal stock_3': 'taken into deptal stock', '1963_4': '1963'} | {'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'taken into deptal stock_3': [0], '1963_4': [0]} | ['number', 'previous br no', 'taken into deptal stock', 'withdrawn', 'disposal'] | [['17', '61059', '1963', '1966', 'scrapped ( 1966 )'], ['18', '61181', '1963', '1965', 'scrapped ( 1966 )'], ['19', '61204', '1963', '1966', 'scrapped ( 1966 )'], ['20', '61205', '1963', '1965', 'scrapped ( 1966 )'], ['21', '61233', '1963', '1966', 'scrapped ( 1966 )'], ['22', '61252', '1963', '1964', 'scrapped ( 1966 )'], ['23', '61300', '1963', '1965', 'scrapped ( 1966 )'], ['24 ( 1st )', '61323', '1963', '1963', 'scrapped ( 1964 )'], ['24 ( 2nd )', '61375', '1963', '1966', 'scrapped ( 1966 )'], ['25', '61272', '1965', '1966', 'scrapped ( 1966 )'], ['26', '61138', '1965', '1967', 'scrapped ( 1968 )'], ['27', '61105', '1965', '1966', 'scrapped ( 1966 )'], ['28', '61194', '1965', '1966', 'scrapped ( 1966 )'], ['29', '61264', '1965', '1967', 'woodham brothers , later preserved'], ['30', '61050', '1966', '1968', 'scrapped ( 1968 )'], ['31 ( 2nd )', '61051', '1966', '1966', 'scrapped ( 1966 )']] |
portugal in the eurovision song contest 1996 | https://en.wikipedia.org/wiki/Portugal_in_the_Eurovision_Song_Contest_1996 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18994360-1.html.csv | comparative | in the 1996 eurovision song contest , pedro miguéis scored 20 more points than joão portugal . | {'row_1': '9', 'row_2': '10', 'col': '4', 'col_other': '2', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '20', 'bigger': 'row1'}} | {'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'singer', 'pedro miguéis'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose singer record fuzzily matches to pedro miguéis .', 'tostr': 'filter_eq { all_rows ; singer ; pedro miguéis }'}, 'points'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; singer ; pedro miguéis } ; points }', 'tointer': 'select the rows whose singer record fuzzily matches to pedro miguéis . take the points record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'singer', 'joão portugal'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose singer record fuzzily matches to joão portugal .', 'tostr': 'filter_eq { all_rows ; singer ; joão portugal }'}, 'points'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; singer ; joão portugal } ; points }', 'tointer': 'select the rows whose singer record fuzzily matches to joão portugal . take the points record of this row .'}], 'result': '20', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; singer ; pedro miguéis } ; points } ; hop { filter_eq { all_rows ; singer ; joão portugal } ; points } }'}, '20'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; singer ; pedro miguéis } ; points } ; hop { filter_eq { all_rows ; singer ; joão portugal } ; points } } ; 20 } = true', 'tointer': 'select the rows whose singer record fuzzily matches to pedro miguéis . take the points record of this row . select the rows whose singer record fuzzily matches to joão portugal . take the points record of this row . the first record is 20 larger than the second record .'} | eq { diff { hop { filter_eq { all_rows ; singer ; pedro miguéis } ; points } ; hop { filter_eq { all_rows ; singer ; joão portugal } ; points } } ; 20 } = true | select the rows whose singer record fuzzily matches to pedro miguéis . take the points record of this row . select the rows whose singer record fuzzily matches to joão portugal . take the points record of this row . the first record is 20 larger than the second record . | 6 | 6 | {'eq_5': 5, 'result_6': 6, 'diff_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_7': 7, 'singer_8': 8, 'pedro miguéis_9': 9, 'points_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'singer_12': 12, 'joão portugal_13': 13, 'points_14': 14, '20_15': 15} | {'eq_5': 'eq', 'result_6': 'true', 'diff_4': 'diff', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_7': 'all_rows', 'singer_8': 'singer', 'pedro miguéis_9': 'pedro miguéis', 'points_10': 'points', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'singer_12': 'singer', 'joão portugal_13': 'joão portugal', 'points_14': 'points', '20_15': '20'} | {'eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'singer_8': [0], 'pedro miguéis_9': [0], 'points_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'singer_12': [1], 'joão portugal_13': [1], 'points_14': [3], '20_15': [5]} | ['draw', 'singer', 'song', 'points', 'place'] | [['1', 'vnia maroti', 'start stop', '33', '10'], ['2', 'tó leal', 'eu mesmo', '42', '8'], ['3', 'patricia antunes', 'canto em português', '91', '2'], ['4', 'barbara reis', 'a minha ilha', '43', '7'], ['5', 'elaisa', 'ai a noite', '49', '6'], ['6', 'somseis', 'a canção da paz', '76', '3'], ['7', 'cristina castro pereira', 'ganhamos o ceu', '63', '4'], ['8', 'lúcia moniz', 'o meu coração não tem cor', '95', '1'], ['9', 'pedro miguéis', 'prazer em conhecer', '54', '5'], ['10', 'joão portugal', 'top model', '34', '9']] |
united states house of representatives elections , 1942 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1942 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342256-5.html.csv | comparative | david d terry was first elected earlier than ezekiel c gathings to the united states house of representatives . | {'row_1': '5', 'row_2': '1', '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', 'david d terry'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose incumbent record fuzzily matches to david d terry .', 'tostr': 'filter_eq { all_rows ; incumbent ; david d terry }'}, 'first elected'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; david d terry } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to david d terry . take the first elected record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'incumbent', 'ezekiel c gathings'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose incumbent record fuzzily matches to ezekiel c gathings .', 'tostr': 'filter_eq { all_rows ; incumbent ; ezekiel c gathings }'}, 'first elected'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; incumbent ; ezekiel c gathings } ; first elected }', 'tointer': 'select the rows whose incumbent record fuzzily matches to ezekiel c gathings . take the first elected record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; incumbent ; david d terry } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; ezekiel c gathings } ; first elected } } = true', 'tointer': 'select the rows whose incumbent record fuzzily matches to david d terry . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to ezekiel c gathings . take the first elected record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; incumbent ; david d terry } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; ezekiel c gathings } ; first elected } } = true | select the rows whose incumbent record fuzzily matches to david d terry . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to ezekiel c gathings . 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, 'david d terry_8': 8, 'first elected_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'incumbent_11': 11, 'ezekiel c gathings_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', 'david d terry_8': 'david d terry', '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', 'ezekiel c gathings_12': 'ezekiel c gathings', '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], 'david d terry_8': [0], 'first elected_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'incumbent_11': [1], 'ezekiel c gathings_12': [1], 'first elected_13': [3]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['arkansas 1', 'ezekiel c gathings', 'democratic', '1938', 're - elected', 'ezekiel c gathings ( d ) unopposed'], ['arkansas 2', 'wilbur mills', 'democratic', '1938', 're - elected', 'wilbur mills ( d ) unopposed'], ['arkansas 3', 'clyde t ellis', 'democratic', '1938', 'retired to run for u s senate democratic hold', 'j william fulbright ( d ) unopposed'], ['arkansas 4', 'william fadjo cravens', 'democratic', '1939', 're - elected', 'william fadjo cravens ( d ) unopposed'], ['arkansas 5', 'david d terry', 'democratic', '1933', 'retired to run for u s senate democratic hold', 'brooks hays ( d ) unopposed'], ['arkansas 6', 'william f norrell', 'democratic', '1938', 're - elected', 'william f norrell ( d ) unopposed']] |
uefa club competition records and statistics | https://en.wikipedia.org/wiki/UEFA_club_competition_records_and_statistics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12307135-6.html.csv | unique | the only player to debut in europe in the uefa club competition in 1995 and play more than 160 games is raãl . | {'scope': 'subset', 'row': '2', 'col': '3', 'col_other': '2', 'criterion': 'greater_than', 'value': '160', 'subset': {'col': '6', 'criterion': 'equal', 'value': '1995'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'debut in europe', '1995'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; debut in europe ; 1995 }', 'tointer': 'select the rows whose debut in europe record is equal to 1995 .'}, 'games', '160'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose debut in europe record is equal to 1995 . among these rows , select the rows whose games record is greater than 160 .', 'tostr': 'filter_greater { filter_eq { all_rows ; debut in europe ; 1995 } ; games ; 160 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_greater { filter_eq { all_rows ; debut in europe ; 1995 } ; games ; 160 } }', 'tointer': 'select the rows whose debut in europe record is equal to 1995 . among these rows , select the rows whose games record is greater than 160 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'debut in europe', '1995'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; debut in europe ; 1995 }', 'tointer': 'select the rows whose debut in europe record is equal to 1995 .'}, 'games', '160'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose debut in europe record is equal to 1995 . among these rows , select the rows whose games record is greater than 160 .', 'tostr': 'filter_greater { filter_eq { all_rows ; debut in europe ; 1995 } ; games ; 160 }'}, 'player'], 'result': 'raãl', 'ind': 3, 'tostr': 'hop { filter_greater { filter_eq { all_rows ; debut in europe ; 1995 } ; games ; 160 } ; player }'}, 'raãl'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_greater { filter_eq { all_rows ; debut in europe ; 1995 } ; games ; 160 } ; player } ; raãl }', 'tointer': 'the player record of this unqiue row is raãl .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_greater { filter_eq { all_rows ; debut in europe ; 1995 } ; games ; 160 } } ; eq { hop { filter_greater { filter_eq { all_rows ; debut in europe ; 1995 } ; games ; 160 } ; player } ; raãl } } = true', 'tointer': 'select the rows whose debut in europe record is equal to 1995 . among these rows , select the rows whose games record is greater than 160 . there is only one such row in the table . the player record of this unqiue row is raãl .'} | and { only { filter_greater { filter_eq { all_rows ; debut in europe ; 1995 } ; games ; 160 } } ; eq { hop { filter_greater { filter_eq { all_rows ; debut in europe ; 1995 } ; games ; 160 } ; player } ; raãl } } = true | select the rows whose debut in europe record is equal to 1995 . among these rows , select the rows whose games record is greater than 160 . there is only one such row in the table . the player record of this unqiue row is raãl . | 8 | 6 | {'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_greater_1': 1, 'filter_eq_0': 0, 'all_rows_7': 7, 'debut in europe_8': 8, '1995_9': 9, 'games_10': 10, '160_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'player_12': 12, 'raãl_13': 13} | {'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_greater_1': 'filter_greater', 'filter_eq_0': 'filter_eq', 'all_rows_7': 'all_rows', 'debut in europe_8': 'debut in europe', '1995_9': '1995', 'games_10': 'games', '160_11': '160', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'player_12': 'player', 'raãl_13': 'raãl'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_greater_1': [2, 3], 'filter_eq_0': [1], 'all_rows_7': [0], 'debut in europe_8': [0], '1995_9': [0], 'games_10': [1], '160_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'player_12': [3], 'raãl_13': [4]} | ['rank', 'player', 'games', 'goals', 'goal ratio', 'debut in europe'] | [['1', 'paolo maldini', '173', '3', '0.02', '1985'], ['2', 'raãl', '161', '76', '0.46', '1995'], ['3', 'clarence seedorf', '161', '15', '0.09', '1992'], ['4', 'javier zanetti', '159', '5', '0.03', '1995'], ['5', 'xavi', '154', '12', '0.08', '1999'], ['6', 'ryan giggs', '151', '29', '0.19', '1991'], ['7', 'jamie carragher', '150', '1', '0.01', '1997'], ['8', 'edwin van der sar', '142', '0', '0.00', '1993'], ['9', 'andriy shevchenko', '142', '67', '0.47', '1994'], ['10', 'roberto carlos', '141', '20', '0.14', '1995']] |
1973 - 74 football league cup | https://en.wikipedia.org/wiki/1973%E2%80%9374_Football_League_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24887326-6.html.csv | majority | most games of the 1973 - 74 football league cup were played in the month of october . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': '10', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'date', '10'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , most of them fuzzily match to 10 .', 'tostr': 'most_eq { all_rows ; date ; 10 } = true'} | most_eq { all_rows ; date ; 10 } = true | for the date records of all rows , most of them fuzzily match to 10 . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, '10_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', '10_4': '10'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], '10_4': [0]} | ['tie no', 'home team', 'score 1', 'away team', 'attendance', 'date'] | [['1', 'hull city', '4 - 1', 'stockport county', '13753', '06 - 11 - 1973'], ['2', 'birmingham city', '2 - 2', 'newcastle united', '13025', '30 - 10 - 1973'], ['3', 'southampton', '3 - 0', 'chesterfield', '13663', '30 - 10 - 1973'], ['4', 'stoke city', '1 - 1', 'middlesbrough', '19194', '31 - 10 - 1973'], ['5', 'everton', '0 - 1', 'norwich city', '22046', '30 - 10 - 1973'], ['6', 'millwall', '1 - 1', 'bolton wanderers', '9281', '31 - 10 - 1973'], ['7', 'fulham', '2 - 2', 'ipswich town', '8964', '31 - 10 - 1973'], ['8', 'tranmere rovers', '1 - 1', 'wolverhampton wanderers', '14442', '31 - 10 - 1973'], ['9', 'orient', '1 - 1', 'york city', '12061', '31 - 10 - 1973'], ['10', 'carlisle united', '0 - 1', 'manchester city', '14472', '06 - 11 - 1973'], ['11', 'bristol city', '2 - 2', 'coventry city', '19129', '30 - 10 - 1973'], ['12', 'queens park rangers', '8 - 2', 'sheffield wednesday', '16043', '06 - 11 - 1973'], ['13', 'burnley', '1 - 2', 'plymouth argyle', '11150', '30 - 10 - 1973'], ['14', 'sunderland', '0 - 2', 'liverpool', '36208', '21 - 11 - 1973'], ['15', 'west bromwich albion', '1 - 3', 'exeter city', '10783', '31 - 10 - 1973']] |
1956 baltimore colts season | https://en.wikipedia.org/wiki/1956_Baltimore_Colts_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14984039-1.html.csv | aggregation | there were a total of 471,075 attendees during the 1956 baltimore colts season . | {'scope': 'all', 'col': '7', 'type': 'sum', 'result': '471,075', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'attendance'], 'result': '471,075', 'ind': 0, 'tostr': 'sum { all_rows ; attendance }'}, '471,075'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; attendance } ; 471,075 } = true', 'tointer': 'the sum of the attendance record of all rows is 471,075 .'} | round_eq { sum { all_rows ; attendance } ; 471,075 } = true | the sum of the attendance record of all rows is 471,075 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '471,075_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '471,075_5': '471,075'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '471,075_5': [1]} | ['week', 'date', 'opponent', 'result', 'record', 'game site', 'attendance'] | [['1', 'september 30 , 1956', 'chicago bears', 'w 28 - 21', '1 - 0', 'memorial stadium', '45221'], ['2', 'october 6 , 1956', 'detroit lions', 'l 14 - 31', '1 - 1', 'memorial stadium', '42622'], ['3', 'october 14 , 1956', 'green bay packers', 'l 33 - 38', '1 - 2', 'milwaukee county stadium', '24214'], ['4', 'october 21 , 1956', 'chicago bears', 'l 27 - 58', '1 - 3', 'wrigley field', '48364'], ['5', 'october 28 , 1956', 'green bay packers', 'w 28 - 21', '2 - 3', 'memorial stadium', '40086'], ['6', 'november 11 , 1956', 'cleveland browns', 'w 21 - 7', '3 - 3', 'cleveland stadium', '42404'], ['7', 'november 18 , 1956', 'detroit lions', 'l 3 - 27', '3 - 4', 'tiger stadium', '55788'], ['8', 'november 25 , 1956', 'los angeles rams', 'w 56 - 21', '4 - 4', 'memorial stadium', '40321'], ['9', 'december 2 , 1956', 'san francisco 49ers', 'l 17 - 20', '4 - 5', 'memorial stadium', '37227'], ['10', 'december 9 , 1956', 'los angeles rams', 'l 7 - 31', '4 - 6', 'los angeles memorial coliseum', '51037'], ['11', 'december 16 , 1956', 'san francisco 49ers', 'l 17 - 30', '4 - 7', 'kezar stadium', '43791']] |
fringe ( season 1 ) | https://en.wikipedia.org/wiki/Fringe_%28season_1%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24648983-1.html.csv | unique | the only episode to have more than 12 million viewers was the transformation . | {'scope': 'all', 'row': '11', 'col': '7', 'col_other': '2', 'criterion': 'greater_than', 'value': '12', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'us viewers ( million )', '12'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose us viewers ( million ) record is greater than 12 .', 'tostr': 'filter_greater { all_rows ; us viewers ( million ) ; 12 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; us viewers ( million ) ; 12 } }', 'tointer': 'select the rows whose us viewers ( million ) record is greater than 12 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'us viewers ( million )', '12'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose us viewers ( million ) record is greater than 12 .', 'tostr': 'filter_greater { all_rows ; us viewers ( million ) ; 12 }'}, 'title'], 'result': 'the transformation', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; us viewers ( million ) ; 12 } ; title }'}, 'the transformation'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; us viewers ( million ) ; 12 } ; title } ; the transformation }', 'tointer': 'the title record of this unqiue row is the transformation .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; us viewers ( million ) ; 12 } } ; eq { hop { filter_greater { all_rows ; us viewers ( million ) ; 12 } ; title } ; the transformation } } = true', 'tointer': 'select the rows whose us viewers ( million ) record is greater than 12 . there is only one such row in the table . the title record of this unqiue row is the transformation .'} | and { only { filter_greater { all_rows ; us viewers ( million ) ; 12 } } ; eq { hop { filter_greater { all_rows ; us viewers ( million ) ; 12 } ; title } ; the transformation } } = true | select the rows whose us viewers ( million ) record is greater than 12 . there is only one such row in the table . the title record of this unqiue row is the transformation . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'us viewers (million)_7': 7, '12_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'title_9': 9, 'the transformation_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'us viewers (million)_7': 'us viewers ( million )', '12_8': '12', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'title_9': 'title', 'the transformation_10': 'the transformation'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'us viewers (million)_7': [0], '12_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'title_9': [2], 'the transformation_10': [3]} | ['-', 'title', 'directed by', 'written by', 'original air date', 'production code', 'us viewers ( million )'] | [['1', 'pilot', 'alex graves', 'j j abrams & alex kurtzman & roberto orci', 'september 9 , 2008', '276038', '9.13'], ['3', 'the ghost network', 'frederick e o toye', 'david h goodman & j r orci', 'september 23 , 2008', '3t7652', '9.42'], ['4', 'the arrival', 'paul edwards', 'j j abrams & jeff pinkner', 'september 30 , 2008', '3t7653', '9.91'], ['5', 'power hungry', 'christopher misiano', 'jason cahill & julia cho', 'october 14 , 2008', '3t7654', '9.16'], ['6', 'the cure', 'bill eagles', 'felicia d henderson & brad caleb kane', 'october 21 , 2008', '3t7655', '8.91'], ['7', 'in which we meet mr jones', 'brad anderson', 'j j abrams & jeff pinkner', 'november 11 , 2008', '3t7656', '8.61'], ['8', 'the equation', 'gwyneth horder - payton', 'j r orci & david h goodman', 'november 18 , 2008', '3t7657', '9.18'], ['9', 'the dreamscape', 'frederick e o toye', 'zack whedon & julia cho', 'november 25 , 2008', '3t7658', '7.70'], ['10', 'safe', 'michael zinberg', 'david h goodman & jason cahill', 'december 2 , 2008', '3t7659', '8.54'], ['12', 'the no - brainer', 'john polson', 'david h goodman & brad caleb kane', 'january 27 , 2009', '3t7661', '11.62'], ['13', 'the transformation', 'brad anderson', 'zack whedon & j r orci', 'february 3 , 2009', '3t7662', '12.78'], ['15', 'inner child', 'frederick e o toye', 'brad caleb kane & julia cho', 'april 7 , 2009', '3t7664', '9.88'], ['16', 'unleashed', 'brad anderson', 'zack whedon & j r orci', 'april 14 , 2009', '3t7665', '10.15'], ['17', 'bad dreams', 'akiva goldsman', 'akiva goldsman', 'april 21 , 2009', '3t7666', '9.89']] |
lamine ouahab | https://en.wikipedia.org/wiki/Lamine_Ouahab | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16981551-2.html.csv | majority | most of the tournaments that lamine ouahab participated in were on a clay surface . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'clay', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'surface', 'clay'], 'result': True, 'ind': 0, 'tointer': 'for the surface records of all rows , most of them fuzzily match to clay .', 'tostr': 'most_eq { all_rows ; surface ; clay } = true'} | most_eq { all_rows ; surface ; clay } = true | for the surface records of all rows , most of them fuzzily match to clay . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'surface_3': 3, 'clay_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'surface_3': 'surface', 'clay_4': 'clay'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'surface_3': [0], 'clay_4': [0]} | ['date', 'tournament', 'surface', 'opponent in the final', 'score'] | [['11 may 2003', 'sidi fredj', 'clay', 'sasa tuksar', '6 - 4 , 6 - 2'], ['21 december 2003', 'kish island', 'clay', 'sebastian fitz', '6 - 4 , 5 - 7 , 6 - 1'], ['4 april 2004', 'syros', 'hard', 'pavel šnobel', '6 - 4 , 6 - 4'], ['21 may 2005', 'agadir', 'clay', 'tres davis', '6 - 1 , 6 - 2'], ['28 may 2005', 'marrakech', 'clay', 'lukáš lacko', '4 - 6 , 6 - 3 , 6 - 2'], ['4 june 2005', 'khemisset', 'clay', 'talal ouahabi', '7 - 6 , 6 - 1'], ['9 september 2005', 'algiers', 'clay', 'filip polášek', '6 - 3 , 6 - 0'], ['16 september 2005', 'algiers', 'clay', 'slimane saoudi', '6 - 4 , 6 - 3'], ['22 april 2006', 'rabat', 'clay', 'frederico gil', '6 - 4 , 6 - 3'], ['7 may 2006', 'tunis', 'clay', 'younes el aynaoui', 'w / o'], ['9 july 2006', 'montauban', 'clay', 'marc gicquel', '7 - 5 , 3 - 6 , 7 - 6'], ['19 may 2007', 'algiers', 'clay', 'reda el amrani', '6 - 4 , 6 - 3'], ['11 october 2008', 'khemisset', 'clay', 'jan mertl', '6 - 4 , 6 - 4'], ['18 october 2008', 'casablanca', 'clay', 'jonathan dasnières de veigy', '6 - 4 , 6 - 3'], ['31 january 2009', 'casablanca', 'clay', 'éric prodon', '6 - 3 , 6 - 1'], ['7 february 2009', 'rabat', 'clay', 'éric prodon', '7 - 5 , 7 - 5'], ['1 february 2010', 'rabat', 'clay', 'laurent rochette', '6 - 3 , 6 - 3'], ['28 may 2012', 'rabat', 'clay', 'yannik reuter', '6 - 2 , 6 - 3'], ['4 june 2012', 'casablanca', 'clay', 'mehdi ziadi', '6 - 0 , 6 - 2']] |
united states house of representatives elections , 1946 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1946 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1342233-3.html.csv | majority | all of the incumbents in the election of 1946 for united states house of representatives , were from the democratic party . | {'scope': 'all', 'col': '3', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'democratic', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'party', 'democratic'], 'result': True, 'ind': 0, 'tointer': 'for the party records of all rows , all of them fuzzily match to democratic .', 'tostr': 'all_eq { all_rows ; party ; democratic } = true'} | all_eq { all_rows ; party ; democratic } = true | for the party records of all rows , all of them fuzzily match to democratic . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'party_3': 3, 'democratic_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'party_3': 'party', 'democratic_4': 'democratic'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'party_3': [0], 'democratic_4': [0]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['alabama 1', 'frank w boykin', 'democratic', '1935', 're - elected', 'frank w boykin ( d ) unopposed'], ['alabama 2', 'george m grant', 'democratic', '1938', 're - elected', 'george m grant ( d ) unopposed'], ['alabama 3', 'george w andrews', 'democratic', '1944', 're - elected', 'george w andrews ( d ) unopposed'], ['alabama 4', 'sam hobbs', 'democratic', '1934', 're - elected', 'sam hobbs ( d ) 88.1 % roger s bingham ( r ) 11.9 %'], ['alabama 5', 'albert rains', 'democratic', '1944', 're - elected', 'albert rains ( d ) unopposed'], ['alabama 6', 'pete jarman', 'democratic', '1936', 're - elected', 'pete jarman ( d ) unopposed'], ['alabama 7', 'carter manasco', 'democratic', '1941', 're - elected', 'carter manasco ( d ) 72.7 % m h woodward ( r ) 27.3 %'], ['alabama 8', 'john sparkman', 'democratic', '1936', 're - elected elected simultaneously to u s senate', 'john sparkman ( d ) 92.4 % arthur south ( r ) 7.6 %']] |
durham county cricket club | https://en.wikipedia.org/wiki/Durham_County_Cricket_Club | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1176371-1.html.csv | aggregation | the durham county cricket club played a total of 15 t20 matches . | {'scope': 'all', 'col': '6', 'type': 'sum', 'result': '15', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 't20 matches'], 'result': '15', 'ind': 0, 'tostr': 'sum { all_rows ; t20 matches }'}, '15'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; t20 matches } ; 15 } = true', 'tointer': 'the sum of the t20 matches record of all rows is 15 .'} | round_eq { sum { all_rows ; t20 matches } ; 15 } = true | the sum of the t20 matches record of all rows is 15 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 't20 matches_4': 4, '15_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 't20 matches_4': 't20 matches', '15_5': '15'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 't20 matches_4': [0], '15_5': [1]} | ['name of ground', 'location', 'year', 'fc matches', 'la matches', 't20 matches', 'total'] | [['riverside ground', 'chester - le - street', '1995 - present', '102', '124', '15', '241'], ['feethams', 'darlington', '1964 - 2003', '10', '14', '0', '24'], ['grangefield road', 'stockton - on - tees', '1992 - 2006', '12', '11', '0', '23'], ['the racecourse', 'durham city', '1992 - 1994', '11', '7', '0', '18'], ['park drive', 'hartlepool', '1992 - 2000', '8', '9', '0', '17'], ['ropery lane', 'chester - le - street', '1967 - 1994', '3', '7', '0', '10'], ['eastwood gardens', 'gateshead fell', '1992 - 1994', '4', '2', '0', '6'], ['green lane', 'durham city', '1979', '0', '1', '0', '1']] |
1994 foster 's cup | https://en.wikipedia.org/wiki/1994_Foster%27s_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16387953-1.html.csv | superlative | of all the games played in the first round of the 1994 foster 's cup , sydney acquired the highest points in their game against footscray . | {'scope': 'all', 'col_superlative': '2', 'row_superlative': '5', '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', 'home team score'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; home team score }'}, 'home team'], 'result': 'sydney', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; home team score } ; home team }'}, 'sydney'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; home team score } ; home team } ; sydney } = true', 'tointer': 'select the row whose home team score record of all rows is maximum . the home team record of this row is sydney .'} | eq { hop { argmax { all_rows ; home team score } ; home team } ; sydney } = true | select the row whose home team score record of all rows is maximum . the home team record of this row is sydney . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'home team score_5': 5, 'home team_6': 6, 'sydney_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'home team score_5': 'home team score', 'home team_6': 'home team', 'sydney_7': 'sydney'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'home team score_5': [0], 'home team_6': [1], 'sydney_7': [2]} | ['home team', 'home team score', 'away team', 'away team score', 'ground', 'crowd', 'date', 'time'] | [['collingwood', '13.14 ( 92 )', 'north melbourne', '13.13 ( 91 )', 'waverley park', '25708', 'saturday , 19 february 1994', '8:00 pm'], ['st kilda', '14.12 ( 96 )', 'richmond', '17.14 ( 116 )', 'waverley park', '18662', 'monday , 21 february 1994', '8:00 pm'], ['adelaide', '16.17 ( 113 )', 'west coast', '14.10 ( 94 )', 'football park', '28776', 'wednesday 23 february 1994', '8:00 pm'], ['fitzroy', '12.13 ( 85 )', 'geelong', '10.11 ( 71 )', 'waverley park', '9080', 'wednesday 23 february 1994', '8:00 pm'], ['sydney', '18.11 ( 119 )', 'footscray', '16.10 ( 106 )', 'robertson oval , wagga wagga', '5525', 'saturday , 26 february 1994', '2:00 pm'], ['carlton', '11.18 ( 84 )', 'hawthorn', '14.15 ( 99 )', 'waverley park', '26117', 'saturday , 26 february 1994', '8:00 pm']] |
1990 - 91 seattle supersonics season | https://en.wikipedia.org/wiki/1990%E2%80%9391_Seattle_SuperSonics_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17382360-6.html.csv | count | m cage had the high rebounds on three occasions . | {'scope': 'all', 'criterion': 'equal', 'value': 'm cage', 'result': '3', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'high rebounds', 'm cage'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose high rebounds record fuzzily matches to m cage .', 'tostr': 'filter_eq { all_rows ; high rebounds ; m cage }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; high rebounds ; m cage } }', 'tointer': 'select the rows whose high rebounds record fuzzily matches to m cage . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; high rebounds ; m cage } } ; 3 } = true', 'tointer': 'select the rows whose high rebounds record fuzzily matches to m cage . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; high rebounds ; m cage } } ; 3 } = true | select the rows whose high rebounds record fuzzily matches to m cage . 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, 'high rebounds_5': 5, 'm cage_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', 'high rebounds_5': 'high rebounds', 'm cage_6': 'm cage', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'high rebounds_5': [0], 'm cage_6': [0], '3_7': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['28', 'january 3', 'philadelphia 76ers', 'w 127 - 99', 'd mckey ( 24 )', 'm cage ( 12 )', 'g payton ( 11 )', 'seattle center coliseum 13048', '13 - 15'], ['29', 'january 4', 'miami heat', 'w 112 - 86', 's threatt ( 30 )', 'm cage ( 13 )', 'g payton ( 12 )', 'seattle center coliseum 12074', '14 - 15'], ['30', 'january 6', 'portland trail blazers', 'l 111 - 114', 's kemp ( 25 )', 's kemp ( 9 )', 'g payton ( 7 )', 'memorial coliseum 12884', '14 - 16'], ['31', 'january 8', 'los angeles lakers', 'w 96 - 88', 'd mckey ( 29 )', 'o polynice ( 11 )', 'n mcmillan ( 10 )', 'seattle center coliseum 14441', '15 - 16'], ['32', 'january 10', 'golden state warriors', 'l 103 - 113', 'd mckey ( 19 )', 's kemp , o polynice ( 12 )', 'n mcmillan ( 7 )', 'seattle center coliseum 10813', '15 - 17'], ['33', 'january 12', 'sacramento kings', 'l 85 - 101', 'd mckey ( 20 )', 'o polynice ( 14 )', 'g payton ( 9 )', 'arco arena 17014', '15 - 18'], ['34', 'january 15', 'denver nuggets', 'w 146 - 99', 'd barros , d ellis ( 22 )', 's kemp ( 12 )', 'n mcmillan ( 9 )', 'seattle center coliseum 9618', '16 - 18'], ['35', 'january 18', 'los angeles lakers', 'l 96 - 105', 'd mckey ( 24 )', 's kemp ( 8 )', 'g payton ( 11 )', 'great western forum 17505', '16 - 19'], ['36', 'january 19', 'washington bullets', 'w 111 - 89', 'o polynice ( 27 )', 's kemp ( 13 )', 'n mcmillan ( 8 )', 'seattle center coliseum 13369', '17 - 19'], ['37', 'january 22', 'milwaukee bucks', 'w 132 - 101', 'e johnson ( 29 )', 'm cage ( 9 )', 'g payton ( 9 )', 'seattle center coliseum 9469', '18 - 19'], ['38', 'january 25', 'phoenix suns', 'l 113 - 128', 'e johnson ( 25 )', 's kemp ( 13 )', 'n mcmillan ( 7 )', 'arizona veterans memorial coliseum 14487', '18 - 20'], ['39', 'january 26', 'atlanta hawks', 'w 103 - 102', 'd mckey ( 23 )', 'd mckey ( 8 )', 'n mcmillan , g payton ( 9 )', 'seattle center coliseum 12792', '19 - 20'], ['40', 'january 28', 'san antonio spurs', 'l 107 - 119', 'e johnson ( 21 )', 'd mckey ( 14 )', 'g payton ( 11 )', 'hemisfair arena 15908', '19 - 21'], ['41', 'january 29', 'dallas mavericks', 'l 112 - 117', 'd mckey ( 24 )', 'o polynice ( 6 )', 'n mcmillan ( 8 )', 'reunion arena 15820', '19 - 22'], ['42', 'january 31', 'houston rockets', 'w 97 - 94', 's threatt ( 18 )', 's kemp ( 17 )', 'd mckey , d mckey ( 6 )', 'the summit 14659', '20 - 22']] |
list of space telescopes | https://en.wikipedia.org/wiki/List_of_space_telescopes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15951109-4.html.csv | count | of all of the space telescopes , there are 3 whose space agency is nasa . | {'scope': 'all', 'criterion': 'equal', 'value': 'nasa', 'result': '3', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'space agency', 'nasa'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose space agency record fuzzily matches to nasa .', 'tostr': 'filter_eq { all_rows ; space agency ; nasa }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; space agency ; nasa } }', 'tointer': 'select the rows whose space agency record fuzzily matches to nasa . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; space agency ; nasa } } ; 3 } = true', 'tointer': 'select the rows whose space agency record fuzzily matches to nasa . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; space agency ; nasa } } ; 3 } = true | select the rows whose space agency record fuzzily matches to nasa . 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, 'space agency_5': 5, 'nasa_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', 'space agency_5': 'space agency', 'nasa_6': 'nasa', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'space agency_5': [0], 'nasa_6': [0], '3_7': [2]} | ['name', 'space agency', 'launch date', 'terminated', 'location'] | [['corot', 'cnes & esa', '27 december 2006', '2013', 'earth orbit ( 872 - 884 km )'], ['hipparcos', 'esa', '8 august 1989', 'march 1993', 'earth orbit ( 223 - 35632 km )'], ['hubble space telescope', 'nasa', '24 april 1990', '-', 'earth orbit ( 586.47 - 610.44 km )'], ['kepler mission', 'nasa', '6 march 2009', '-', 'earth - trailing heliocentric orbit'], ['most', 'csa', '30 june 2003', '-', 'earth orbit ( 819 - 832 km )'], ['swift gamma ray burst explorer', 'nasa', '20 november 2004', '-', 'earth orbit ( 585 - 604 km )']] |
2007 - 08 scottish second division | https://en.wikipedia.org/wiki/2007%E2%80%9308_Scottish_Second_Division | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11206787-5.html.csv | ordinal | somerset park has the second highest seating capacity of stadiums in the 2007 - 08 scottish second division . | {'row': '3', 'col': '3', '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', 'capacity', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; capacity ; 2 }'}, 'stadium'], 'result': 'somerset park', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; capacity ; 2 } ; stadium }'}, 'somerset park'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; capacity ; 2 } ; stadium } ; somerset park } = true', 'tointer': 'select the row whose capacity record of all rows is 2nd maximum . the stadium record of this row is somerset park .'} | eq { hop { nth_argmax { all_rows ; capacity ; 2 } ; stadium } ; somerset park } = true | select the row whose capacity record of all rows is 2nd maximum . the stadium record of this row is somerset park . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'capacity_5': 5, '2_6': 6, 'stadium_7': 7, 'somerset park_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', 'capacity_5': 'capacity', '2_6': '2', 'stadium_7': 'stadium', 'somerset park_8': 'somerset park'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'capacity_5': [0], '2_6': [0], 'stadium_7': [1], 'somerset park_8': [2]} | ['team', 'stadium', 'capacity', 'highest', 'lowest', 'average'] | [['ross county', 'victoria park', '6700', '3716', '1511', '2247'], ['raith rovers', "stark 's park", '10104', '2357', '1349', '1759'], ['ayr united', 'somerset park', '11998', '1345', '971', '1137'], ['airdrie united', 'new broomfield', '10171', '1645', '611', '981'], ["queen 's park", 'hampden park', '52500', '1211', '431', '712'], ['peterhead', 'balmoor', '4000', '926', '462', '694'], ['alloa athletic', 'recreation park', '3100', '1053', '441', '602'], ['cowdenbeath', 'central park', '4370', '1953', '244', '519'], ['brechin city', 'glebe park', '3960', '669', '345', '489']] |
big brother ( albania ) | https://en.wikipedia.org/wiki/Big_Brother_%28Albania%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15984770-1.html.csv | unique | season 3 was the only season to last a total of more than 110 days from start to finish . | {'scope': 'all', 'row': '3', 'col': '5', 'col_other': '1', 'criterion': 'greater_than', 'value': '110', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'days', '110'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose days record is greater than 110 .', 'tostr': 'filter_greater { all_rows ; days ; 110 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; days ; 110 } }', 'tointer': 'select the rows whose days record is greater than 110 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'days', '110'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose days record is greater than 110 .', 'tostr': 'filter_greater { all_rows ; days ; 110 }'}, 'series'], 'result': 'season 3', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; days ; 110 } ; series }'}, 'season 3'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; days ; 110 } ; series } ; season 3 }', 'tointer': 'the series record of this unqiue row is season 3 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; days ; 110 } } ; eq { hop { filter_greater { all_rows ; days ; 110 } ; series } ; season 3 } } = true', 'tointer': 'select the rows whose days record is greater than 110 . there is only one such row in the table . the series record of this unqiue row is season 3 .'} | and { only { filter_greater { all_rows ; days ; 110 } } ; eq { hop { filter_greater { all_rows ; days ; 110 } ; series } ; season 3 } } = true | select the rows whose days record is greater than 110 . there is only one such row in the table . the series record of this unqiue row is season 3 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'days_7': 7, '110_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'series_9': 9, 'season 3_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'days_7': 'days', '110_8': '110', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'series_9': 'series', 'season 3_10': 'season 3'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'days_7': [0], '110_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'series_9': [2], 'season 3_10': [3]} | ['series', 'host', 'launch date', 'finale date', 'days', 'winner', 'prize'] | [['season 1', 'arbana osmani', '23 february 2008', '31 may 2008', '100', 'arbër çepani', '50000'], ['season 2', 'arbana osmani', '7 february 2009', '16 may 2009', '99', 'qetsor ferunaj', '70000'], ['season 3', 'arbana osmani', '23 january 2010', '15 may 2010', '113', 'jetmir salaj', '75000'], ['season 4', 'arbana osmani', '25 december 2010', '2 april 2011', '99', 'ermela mezuraj', '75000'], ['season 5', 'arbana osmani', '18 february 2012', '26 may 2012', '99', 'arbër zeka', '100000'], ['season 6', 'arbana osmani', '23 february 2013', '1 june 2013', '99', 'anaid kaloti', '100000']] |
sebastian prödl | https://en.wikipedia.org/wiki/Sebastian_Pr%C3%B6dl | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12253254-1.html.csv | aggregation | the average score in sebastion prödl 's cometitions is about 2-0 . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '2-0', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'score'], 'result': '2-0', 'ind': 0, 'tostr': 'avg { all_rows ; score }'}, '2-0'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; score } ; 2-0 } = true', 'tointer': 'the average of the score record of all rows is 2-0 .'} | round_eq { avg { all_rows ; score } ; 2-0 } = true | the average of the score record of all rows is 2-0 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'score_4': 4, '2-0_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'score_4': 'score', '2-0_5': '2-0'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'score_4': [0], '2-0_5': [1]} | ['date', 'venue', 'score', 'result', 'competition'] | [['26 march 2008', 'ernst - happel - stadion , vienna , austria', '2 - 0', '3 - 4', 'friendly'], ['26 march 2008', 'ernst - happel - stadion , vienna , austria', '3 - 0', '3 - 4', 'friendly'], ['8 october 2010', 'ernst - happel - stadion , vienna , austria', '1 - 0', '3 - 0', 'uefa euro 2012 qualifying'], ['15 october 2013', 'tórsvøllur , tórshavn , faroe islands', '2 - 0', '3 - 0', '2014 fifa world cup qualification']] |
united states house of representatives elections in georgia , 1998 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections_in_Georgia%2C_1998 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27021001-1.html.csv | superlative | the person in the georgia house of representatives to be elected the earliest was john lewis . | {'scope': 'all', 'col_superlative': '4', 'row_superlative': '5', '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', 'elected'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; elected }'}, 'incumbent'], 'result': 'john lewis', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; elected } ; incumbent }'}, 'john lewis'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; elected } ; incumbent } ; john lewis } = true', 'tointer': 'select the row whose elected record of all rows is minimum . the incumbent record of this row is john lewis .'} | eq { hop { argmin { all_rows ; elected } ; incumbent } ; john lewis } = true | select the row whose elected record of all rows is minimum . the incumbent record of this row is john lewis . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'elected_5': 5, 'incumbent_6': 6, 'john lewis_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'elected_5': 'elected', 'incumbent_6': 'incumbent', 'john lewis_7': 'john lewis'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'elected_5': [0], 'incumbent_6': [1], 'john lewis_7': [2]} | ['district', 'incumbent', 'party', 'elected', 'status', 'result'] | [["georgia 's 1st", 'jack kingston', 'republican', '1992', 're - elected', 'jack kingston ( r ) unopposed'], ["georgia 's 2nd", 'sanford bishop', 'democratic', '1992', 're - elected', 'sanford bishop ( d ) 57 % joseph mccormick ( r ) 43 %'], ["georgia 's 3rd", 'mac collins', 'republican', '1992', 're - elected', 'mac collins ( r ) unopposed'], ["georgia 's 4th", 'cynthia mckinney', 'democratic', '1992', 're - elected', 'cynthia mckinney ( d ) 61 % sunny warren ( r ) 39 %'], ["georgia 's 5th", 'john lewis', 'democratic', '1986', 're - elected', 'john lewis ( d ) 79 % john lewis sr ( r ) 21 %'], ["georgia 's 6th", 'johnny isakson', 'republican', '1999', 're - elected', 'newt gingrich ( r ) 71 % gary pelphrey ( d ) 29 %'], ["georgia 's 7th", 'bob barr', 'republican', '1994', 're - elected', 'bob barr ( r ) 55 % james williams ( d ) 45 %'], ["georgia 's 8th", 'saxby chambliss', 'republican', '1994', 're - elected', 'saxby chambliss ( r ) 62 % ronald cain ( d ) 38 %'], ["georgia 's 9th", 'nathan deal', 'republican', '1992', 're - elected', 'nathan deal ( r ) unopposed'], ["georgia 's 10th", 'charlie norwood', 'republican', '1994', 're - elected', 'charlie norwood ( r ) 59 % marion freeman ( d ) 41 %']] |
1955 washington redskins season | https://en.wikipedia.org/wiki/1955_Washington_Redskins_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15123196-1.html.csv | unique | during the washington redskins ' 1955 season , the only game they lost after november 1 was on december 4 . | {'scope': 'subset', 'row': '11', 'col': '4', 'col_other': '2', 'criterion': 'fuzzily_match', 'value': 'l', 'subset': {'col': '2', 'criterion': 'greater_than', 'value': 'november 1 , 1955'}} | {'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'date', 'november 1 , 1955'], 'result': None, 'ind': 0, 'tostr': 'filter_greater { all_rows ; date ; november 1 , 1955 }', 'tointer': 'select the rows whose date record is greater than november 1 , 1955 .'}, 'result', 'l'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record is greater than november 1 , 1955 . among these rows , select the rows whose result record fuzzily matches to l .', 'tostr': 'filter_eq { filter_greater { all_rows ; date ; november 1 , 1955 } ; result ; l }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_greater { all_rows ; date ; november 1 , 1955 } ; result ; l } } = true', 'tointer': 'select the rows whose date record is greater than november 1 , 1955 . among these rows , select the rows whose result record fuzzily matches to l . there is only one such row in the table .'} | only { filter_eq { filter_greater { all_rows ; date ; november 1 , 1955 } ; result ; l } } = true | select the rows whose date record is greater than november 1 , 1955 . among these rows , select the rows whose result record fuzzily matches to l . there is only one such row in the table . | 3 | 3 | {'only_2': 2, 'result_3': 3, 'filter_str_eq_1': 1, 'filter_greater_0': 0, 'all_rows_4': 4, 'date_5': 5, 'november 1, 1955_6': 6, 'result_7': 7, 'l_8': 8} | {'only_2': 'only', 'result_3': 'true', 'filter_str_eq_1': 'filter_str_eq', 'filter_greater_0': 'filter_greater', 'all_rows_4': 'all_rows', 'date_5': 'date', 'november 1, 1955_6': 'november 1 , 1955', 'result_7': 'result', 'l_8': 'l'} | {'only_2': [3], 'result_3': [], 'filter_str_eq_1': [2], 'filter_greater_0': [1], 'all_rows_4': [0], 'date_5': [0], 'november 1, 1955_6': [0], 'result_7': [1], 'l_8': [1]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 25 , 1955', 'cleveland browns', 'w 27 - 17', '30041'], ['2', 'october 1 , 1955', 'philadelphia eagles', 'w 31 - 30', '31891'], ['3', 'october 9 , 1955', 'chicago cardinals', 'l 24 - 10', '26337'], ['4', 'october 16 , 1955', 'cleveland browns', 'l 24 - 14', '29168'], ['5', 'october 23 , 1955', 'baltimore colts', 'w 14 - 13', '51387'], ['6', 'october 30 , 1955', 'new york giants', 'l 35 - 7', '17402'], ['7', 'november 6 , 1955', 'philadelphia eagles', 'w 34 - 21', '25741'], ['8', 'november 13 , 1955', 'san francisco 49ers', 'w 7 - 0', '25112'], ['9', 'november 20 , 1955', 'chicago cardinals', 'w 31 - 0', '16901'], ['10', 'november 27 , 1955', 'pittsburgh steelers', 'w 23 - 14', '21760'], ['11', 'december 4 , 1955', 'new york giants', 'l 27 - 20', '28556'], ['12', 'december 11 , 1955', 'pittsburgh steelers', 'w 28 - 17', '20547']] |
savannah braves | https://en.wikipedia.org/wiki/Savannah_Braves | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18893381-2.html.csv | majority | the savannah braves were for the most part not eligible for the playoffs . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'not eligible', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'playoffs', 'not eligible'], 'result': True, 'ind': 0, 'tointer': 'for the playoffs records of all rows , most of them fuzzily match to not eligible .', 'tostr': 'most_eq { all_rows ; playoffs ; not eligible } = true'} | most_eq { all_rows ; playoffs ; not eligible } = true | for the playoffs records of all rows , most of them fuzzily match to not eligible . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'playoffs_3': 3, 'not eligible_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'playoffs_3': 'playoffs', 'not eligible_4': 'not eligible'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'playoffs_3': [0], 'not eligible_4': [0]} | ['year', 'record', 'finish', 'manager', 'playoffs'] | [['1971', '57 - 84', '5th', 'eddie haas', 'not eligible'], ['1972', '80 - 59', '2nd', 'clint courtney', 'not eligible'], ['1973', '71 - 68', '3rd', 'clint courtney ( 34 - 23 ) / tommie aaron ( 37 - 45 )', 'not eligible'], ['1974', '73 - 65', '4th', 'tommie aaron', 'not eligible'], ['1975', '70 - 64', '3rd ( t )', 'tommie aaron', 'not eligible'], ['1976', '69 - 71', '5th', 'tommie aaron', 'not eligible'], ['1977', '77 - 63', '3rd', 'gene hassell', 'lost in 1st round'], ['1978', '72 - 72', '4th', 'bobby dews', 'lost league finals'], ['1979', '60 - 83', '10th', 'eddie haas', 'not eligible'], ['1980', '77 - 67', '3rd', 'eddie haas', 'lost in 1st round'], ['1981', '70 - 70', '5th', 'andy gilbert', 'lost in 1st round'], ['1982', '69 - 75', '8th', 'andy gilbert', 'not eligible'], ['1983', '81 - 64', '3rd', 'bobby dews', 'lost in 1st round']] |
2008 pga tour | https://en.wikipedia.org/wiki/2008_PGA_Tour | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14473512-2.html.csv | comparative | phil mickelson played in more events of the 2008 pga tour than tiger woods . | {'row_1': '3', 'row_2': '2', 'col': '4', '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', 'player', 'phil mickelson'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to phil mickelson .', 'tostr': 'filter_eq { all_rows ; player ; phil mickelson }'}, 'events'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; phil mickelson } ; events }', 'tointer': 'select the rows whose player record fuzzily matches to phil mickelson . take the events record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'tiger woods'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to tiger woods .', 'tostr': 'filter_eq { all_rows ; player ; tiger woods }'}, 'events'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; tiger woods } ; events }', 'tointer': 'select the rows whose player record fuzzily matches to tiger woods . take the events record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; player ; phil mickelson } ; events } ; hop { filter_eq { all_rows ; player ; tiger woods } ; events } } = true', 'tointer': 'select the rows whose player record fuzzily matches to phil mickelson . take the events record of this row . select the rows whose player record fuzzily matches to tiger woods . take the events record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; player ; phil mickelson } ; events } ; hop { filter_eq { all_rows ; player ; tiger woods } ; events } } = true | select the rows whose player record fuzzily matches to phil mickelson . take the events record of this row . select the rows whose player record fuzzily matches to tiger woods . take the events 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, 'player_7': 7, 'phil mickelson_8': 8, 'events_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'tiger woods_12': 12, 'events_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', 'player_7': 'player', 'phil mickelson_8': 'phil mickelson', 'events_9': 'events', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'tiger woods_12': 'tiger woods', 'events_13': 'events'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'phil mickelson_8': [0], 'events_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'tiger woods_12': [1], 'events_13': [3]} | ['rank', 'player', 'country', 'events', 'prize money'] | [['1', 'vijay singh', 'fiji', '23', '6601094'], ['2', 'tiger woods', 'united states', '6', '5775000'], ['3', 'phil mickelson', 'united states', '21', '5118875'], ['4', 'sergio garcía', 'spain', '19', '4858224'], ['5', 'kenny perry', 'united states', '26', '4663794'], ['6', 'anthony kim', 'united states', '22', '4656265'], ['7', 'camilo villegas', 'colombia', '22', '4422641'], ['8', 'pádraig harrington', 'ireland', '15', '4313551'], ['9', 'stewart cink', 'united states', '22', '3963661'], ['10', 'justin leonard', 'united states', '25', '3943542']] |
2009 - 10 new york knicks season | https://en.wikipedia.org/wiki/2009%E2%80%9310_New_York_Knicks_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23248869-6.html.csv | majority | david lee recorded the majority of high rebounds performances for the new york knicks . | {'scope': 'all', 'col': '6', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'david lee', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'high rebounds', 'david lee'], 'result': True, 'ind': 0, 'tointer': 'for the high rebounds records of all rows , most of them fuzzily match to david lee .', 'tostr': 'most_eq { all_rows ; high rebounds ; david lee } = true'} | most_eq { all_rows ; high rebounds ; david lee } = true | for the high rebounds records of all rows , most of them fuzzily match to david lee . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'high rebounds_3': 3, 'david lee_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'high rebounds_3': 'high rebounds', 'david lee_4': 'david lee'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'high rebounds_3': [0], 'david lee_4': [0]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['18', 'december 1', 'phoenix', 'w 126 - 99 ( ot )', 'danilo gallinari ( 27 )', 'danilo gallinari ( 10 )', 'larry hughes ( 12 )', 'madison square garden 19763', '4 - 14'], ['19', 'december 2', 'orlando', 'l 104 - 118 ( ot )', 'wilson chandler ( 24 )', 'danilo gallinari ( 7 )', 'danilo gallinari , larry hughes ( 3 )', 'amway arena 17461', '4 - 15'], ['20', 'december 4', 'atlanta', 'w 114 - 107 ( ot )', 'al harrington ( 27 )', 'david lee ( 17 )', 'chris duhon ( 10 )', 'philips arena 17165', '5 - 15'], ['21', 'december 6', 'new jersey', 'w 106 - 97 ( ot )', 'al harrington ( 26 )', 'al harrington ( 14 )', 'al harrington , chris duhon ( 5 )', 'madison square garden 19602', '6 - 15'], ['22', 'december 7', 'portland', 'w 93 - 84 ( ot )', 'larry hughes ( 21 )', 'david lee ( 10 )', 'chris duhon ( 9 )', 'madison square garden 19763', '7 - 15'], ['23', 'december 11', 'new orleans', 'w 113 - 96 ( ot )', 'al harrington ( 28 )', 'david lee ( 14 )', 'chris duhon ( 9 )', 'new orleans arena 15569', '8 - 15'], ['24', 'december 15', 'charlotte', 'l 87 - 94 ( ot )', 'chris duhon ( 18 )', 'david lee ( 8 )', 'chris duhon ( 6 )', 'time warner cable arena 13606', '8 - 16'], ['26', 'december 18', 'la clippers', 'w 95 - 91 ( ot )', 'david lee ( 25 )', 'david lee ( 11 )', 'chris duhon ( 10 )', 'madison square garden 19763', '9 - 17'], ['27', 'december 20', 'charlotte', 'w 98 - 94 ( ot )', 'wilson chandler ( 26 )', 'david lee ( 15 )', 'david lee ( 7 )', 'madison square garden 18767', '10 - 17'], ['28', 'december 22', 'chicago', 'w 88 - 81 ( ot )', 'al harrington ( 20 )', 'david lee ( 21 )', 'david lee ( 5 )', 'madison square garden 19763', '11 - 17'], ['29', 'december 25', 'miami', 'l 87 - 93 ( ot )', 'danilo gallinari ( 26 )', 'david lee ( 16 )', 'danilo gallinari , chris duhon ( 3 )', 'madison square garden 19763', '11 - 18'], ['30', 'december 27', 'san antonio', 'l 88 - 95 ( ot )', 'david lee ( 28 )', 'david lee ( 10 )', 'chris duhon ( 13 )', 'madison square garden 19763', '11 - 19'], ['31', 'december 29', 'detroit', 'w 104 - 87 ( ot )', 'david lee ( 30 )', 'david lee ( 12 )', 'chris duhon ( 7 )', 'the palace of auburn hills 22076', '12 - 19']] |
adriano buzaid | https://en.wikipedia.org/wiki/Adriano_Buzaid | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23128286-1.html.csv | aggregation | adriano buzaid recorded a total number of 18 podium finishes in his races . | {'scope': 'all', 'col': '8', 'type': 'sum', 'result': '18', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'podiums'], 'result': '18', 'ind': 0, 'tostr': 'sum { all_rows ; podiums }'}, '18'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; podiums } ; 18 } = true', 'tointer': 'the sum of the podiums record of all rows is 18 .'} | round_eq { sum { all_rows ; podiums } ; 18 } = true | the sum of the podiums record of all rows is 18 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'podiums_4': 4, '18_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'podiums_4': 'podiums', '18_5': '18'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'podiums_4': [0], '18_5': [1]} | ['season', 'series', 'team name', 'races', 'wins', 'poles', 'flaps', 'podiums', 'points', 'final placing'] | [['2006', 'formula ford uk', 'eau rouge motorsport', '17', '1', '0', '1', '1', '186', '13th'], ['2006', 'formula renault uk winter series', 'aka lemac', '4', '0', '0', '0', '1', '58', '7th'], ['2007', 'formula renault uk', 'eucatex', '20', '0', '0', '0', '2', '166', '13th'], ['2007', 'formula renault uk winter series', 'fortec motorsport', '4', '1', '1', '0', '1', '56', '7th'], ['2008', 'formula renault uk', 'fortec motorsport', '20', '5', '6', '5', '7', '418', '3rd'], ['2008', 'formula renault 2.0 italia winter cup', 'bvm minardi team', '2', '0', '0', '0', '0', '18', '10th'], ['2008', 'british formula three national class', 'carlin motorsport', '2', '0', '0', '0', '1', '22', '13th'], ['2009', 'british formula three', 't - sport', '20', '1', '1', '0', '5', '109', '6th']] |
serbia national football team | https://en.wikipedia.org/wiki/Serbia_national_football_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1011001-10.html.csv | aggregation | the serbian national football teams average attendance was 19,807 for its belgrade contests . | {'scope': 'subset', 'col': '8', 'type': 'average', 'result': '19,807', 'subset': {'col': '2', 'criterion': 'equal', 'value': 'belgrade'}} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'city', 'belgrade'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; city ; belgrade }', 'tointer': 'select the rows whose city record fuzzily matches to belgrade .'}, 'average attendance'], 'result': '19,807', 'ind': 1, 'tostr': 'avg { filter_eq { all_rows ; city ; belgrade } ; average attendance }'}, '19,807'], 'result': True, 'ind': 2, 'tostr': 'round_eq { avg { filter_eq { all_rows ; city ; belgrade } ; average attendance } ; 19,807 } = true', 'tointer': 'select the rows whose city record fuzzily matches to belgrade . the average of the average attendance record of these rows is 19,807 .'} | round_eq { avg { filter_eq { all_rows ; city ; belgrade } ; average attendance } ; 19,807 } = true | select the rows whose city record fuzzily matches to belgrade . the average of the average attendance record of these rows is 19,807 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'avg_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'city_5': 5, 'belgrade_6': 6, 'average attendance_7': 7, '19,807_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'avg_1': 'avg', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'city_5': 'city', 'belgrade_6': 'belgrade', 'average attendance_7': 'average attendance', '19,807_8': '19,807'} | {'eq_2': [3], 'result_3': [], 'avg_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'city_5': [0], 'belgrade_6': [0], 'average attendance_7': [1], '19,807_8': [2]} | ['venue', 'city', 'first international', 'last international', 'played', 'draw', 'lost', 'average attendance'] | [['red star stadium', 'belgrade', '31 march 1995 1 - 0 vs', '6 september 2013 1 - 1 vs', '44', '16', '4', '26222'], ['partizan stadium', 'belgrade', '5 september 1999 3 - 1 vs', '6 september 2011 3 - 1 vs', '12', '2', '2', '13393'], ['karađorđe stadium', 'novi sad', '11 september 2012 6 - 1 vs', '12 october 2013 2 - 0 vs', '3', '0', '0', '8830'], ['čair stadium', 'niš', '22 september 1998 1 - 1 vs', '22 september 1998 1 - 1 vs', '1', '1', '0', '16000'], ['smederevo city stadium', 'smederevo', '17 april 2002 4 - 1 vs', '17 april 2002 4 - 1 vs', '1', '0', '0', '15000'], ['mladost stadium', 'kruševac', '27 march 2003 1 - 2 vs', '27 march 2003 1 - 2 vs', '1', '0', '1', '10000'], ['podgorica city stadium', 'podgorica', '12 february 2003 2 - 2 vs', '12 february 2003 2 - 2 vs', '1', '1', '0', '7500'], ['jagodina city stadium', 'jagodina', '15 october 2013 5 - 1 vs', '15 october 2013 5 - 1 vs', '1', '0', '0', '8294']] |
carleton county , new brunswick | https://en.wikipedia.org/wiki/Carleton_County%2C_New_Brunswick | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-170961-2.html.csv | ordinal | in carleton county , new brunswick , the parish of woodstock has the third highest population among the county parishes . | {'row': '3', 'col': '4', 'order': '3', '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', 'population', '3'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; population ; 3 }'}, 'official name'], 'result': 'woodstock', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; population ; 3 } ; official name }'}, 'woodstock'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; population ; 3 } ; official name } ; woodstock } = true', 'tointer': 'select the row whose population record of all rows is 3rd maximum . the official name record of this row is woodstock .'} | eq { hop { nth_argmax { all_rows ; population ; 3 } ; official name } ; woodstock } = true | select the row whose population record of all rows is 3rd maximum . the official name record of this row is woodstock . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'population_5': 5, '3_6': 6, 'official name_7': 7, 'woodstock_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', 'population_5': 'population', '3_6': '3', 'official name_7': 'official name', 'woodstock_8': 'woodstock'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'population_5': [0], '3_6': [0], 'official name_7': [1], 'woodstock_8': [2]} | ['official name', 'status', 'area km 2', 'population', 'census ranking'] | [['wakefield', 'parish', '196.42', '2703', '1079 of 5008'], ['kent', 'parish', '839.79', '2361', '1184 of 5008'], ['woodstock', 'parish', '197.45', '2148', '1258 of 5008'], ['brighton', 'parish', '508.30', '1834', '1402 of 5008'], ['wicklow', 'parish', '195.50', '1753', '1441 of 5008'], ['northampton', 'parish', '243.31', '1599', '1537 of 5008'], ['richmond', 'parish', '258.82', '1414', '1666 of 5008'], ['peel', 'parish', '113.12', '1257', '1779 of 5008'], ['wilmot', 'parish', '191.43', '1143', '1888 of 5008'], ['aberdeen', 'parish', '447.91', '959', '2105 of 5008'], ['simonds', 'parish', '75.54', '489', '3044 of 5008']] |
united states intelligence budget | https://en.wikipedia.org/wiki/United_States_intelligence_budget | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17198719-1.html.csv | comparative | the cia spends more on data collection than the defense intelligence program does . | {'row_1': '1', 'row_2': '5', 'col': '3', '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', 'administrating agencies by nip funds only', '0 central intelligence agency program'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose administrating agencies by nip funds only record fuzzily matches to 0 central intelligence agency program .', 'tostr': 'filter_eq { all_rows ; administrating agencies by nip funds only ; 0 central intelligence agency program }'}, 'data collection'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; administrating agencies by nip funds only ; 0 central intelligence agency program } ; data collection }', 'tointer': 'select the rows whose administrating agencies by nip funds only record fuzzily matches to 0 central intelligence agency program . take the data collection record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'administrating agencies by nip funds only', '0 defense intelligence program'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose administrating agencies by nip funds only record fuzzily matches to 0 defense intelligence program .', 'tostr': 'filter_eq { all_rows ; administrating agencies by nip funds only ; 0 defense intelligence program }'}, 'data collection'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; administrating agencies by nip funds only ; 0 defense intelligence program } ; data collection }', 'tointer': 'select the rows whose administrating agencies by nip funds only record fuzzily matches to 0 defense intelligence program . take the data collection record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; administrating agencies by nip funds only ; 0 central intelligence agency program } ; data collection } ; hop { filter_eq { all_rows ; administrating agencies by nip funds only ; 0 defense intelligence program } ; data collection } } = true', 'tointer': 'select the rows whose administrating agencies by nip funds only record fuzzily matches to 0 central intelligence agency program . take the data collection record of this row . select the rows whose administrating agencies by nip funds only record fuzzily matches to 0 defense intelligence program . take the data collection record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; administrating agencies by nip funds only ; 0 central intelligence agency program } ; data collection } ; hop { filter_eq { all_rows ; administrating agencies by nip funds only ; 0 defense intelligence program } ; data collection } } = true | select the rows whose administrating agencies by nip funds only record fuzzily matches to 0 central intelligence agency program . take the data collection record of this row . select the rows whose administrating agencies by nip funds only record fuzzily matches to 0 defense intelligence program . take the data collection 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, 'administrating agencies by nip funds only_7': 7, '0 central intelligence agency program_8': 8, 'data collection_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'administrating agencies by nip funds only_11': 11, '0 defense intelligence program_12': 12, 'data collection_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', 'administrating agencies by nip funds only_7': 'administrating agencies by nip funds only', '0 central intelligence agency program_8': '0 central intelligence agency program', 'data collection_9': 'data collection', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'administrating agencies by nip funds only_11': 'administrating agencies by nip funds only', '0 defense intelligence program_12': '0 defense intelligence program', 'data collection_13': 'data collection'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'administrating agencies by nip funds only_7': [0], '0 central intelligence agency program_8': [0], 'data collection_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'administrating agencies by nip funds only_11': [1], '0 defense intelligence program_12': [1], 'data collection_13': [3]} | ['administrating agencies by nip funds only', 'management and support', 'data collection', 'data processing and exploitation', 'total'] | [['0 central intelligence agency program', '1 , 8', '11 , 5', '00 0387', '14787'], ['0 consolidated cryptologic program', '5 , 2', '0 2 , 5', '1 , 6', '10 , 8'], ['0 national reconnaissance program', '1 , 8', '0 6 , 0', '2 , 5', '10 , 3'], ['0 national geospatial - intelligence program', '2 , 0', '000 0537', '1 , 4', '4 , 91'], ['0 defense intelligence program', '1 , 7', '0 1 , 3', '00 0228', '4428'], ['total', '12 , 5', '21837', '6115', '45225']] |
1951 - 52 illinois fighting illini men 's basketball team | https://en.wikipedia.org/wiki/1951%E2%80%9352_Illinois_Fighting_Illini_men%27s_basketball_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22824312-1.html.csv | count | a total of three players on the 1951 - 52 illinois fighting illini men 's basketball team were in the freshman class . | {'scope': 'all', 'criterion': 'equal', 'value': 'freshman', 'result': '3', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'class', 'freshman'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose class record fuzzily matches to freshman .', 'tostr': 'filter_eq { all_rows ; class ; freshman }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; class ; freshman } }', 'tointer': 'select the rows whose class record fuzzily matches to freshman . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; class ; freshman } } ; 3 } = true', 'tointer': 'select the rows whose class record fuzzily matches to freshman . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; class ; freshman } } ; 3 } = true | select the rows whose class record fuzzily matches to freshman . 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, 'class_5': 5, 'freshman_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', 'class_5': 'class', 'freshman_6': 'freshman', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'class_5': [0], 'freshman_6': [0], '3_7': [2]} | ['no', 'player', 'position', 'height', 'weight', 'class', 'hometown'] | [['9', 'elmer plew', 'guard', '6 - 0', '170', 'freshman', 'paris , illinois'], ['11', 'jim dutcher', 'forward', '6 - 3', '185', 'freshman', 'downers grove , illinois'], ['16', 'jim wright', 'guard', '6 - 0', '160', 'sophomore', 'lawrenceville , illinois'], ['19', 'james bredar', 'guard', '5 - 11', '167', 'junior', 'salem , illinois'], ['22', 'johnny kerr', 'center', '6 - 9', '205', 'sophomore', 'chicago , illinois / tilden high school'], ['24', 'ed makovsky', 'forward', '6 - 5', '194', 'freshman', 'cicero , illinois / morton high school'], ['25', 'robert peterson', 'center', '6 - 8', '235', 'junior', 'wayne , illinois'], ['26', 'irving bemoras', 'guard', '6 - 3 1 / 2', '185', 'junior', 'chicago , illinois / marshall high school'], ['27', 'jack follmer', 'center', '6 - 4', '200', 'senior', 'forrest , illinois'], ['33', 'clive follmer', 'forward', '6 - 4', '195', 'junior', 'forrest , illinois'], ['34', 'seymour gantman', 'guard', '5 - 7', '165', 'senior', 'chicago , illinois / marshall high school'], ['35', 'ren alde', 'guard', '6 - 2', '180', 'senior', 'pana , illinois'], ['37', 'rod fletcher ( captain )', 'guard', '6 - 4', '194', 'senior', 'champaign , illinois'], ['38', 'herb gerecke', 'guard', '6 - 1', '180', 'senior', 'pekin , illinois'], ['41', 'max hooper', 'forward', '6 - 5', '200', 'sophomore', 'mt vernon , illinois']] |
neuza silva | https://en.wikipedia.org/wiki/Neuza_Silva | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16893837-4.html.csv | ordinal | neuza silva had their second match in 2003 on a clay surface . | {'row': '2', 'col': '3', 'order': '2', 'col_other': '6', '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', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; date ; 2 }'}, 'surface'], 'result': 'clay', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; date ; 2 } ; surface }'}, 'clay'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; date ; 2 } ; surface } ; clay } = true', 'tointer': 'select the row whose date record of all rows is 2nd minimum . the surface record of this row is clay .'} | eq { hop { nth_argmin { all_rows ; date ; 2 } ; surface } ; clay } = true | select the row whose date record of all rows is 2nd minimum . the surface record of this row is clay . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'date_5': 5, '2_6': 6, 'surface_7': 7, 'clay_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', '2_6': '2', 'surface_7': 'surface', 'clay_8': 'clay'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'date_5': [0], '2_6': [0], 'surface_7': [1], 'clay_8': [2]} | ['edition', 'round', 'date', 'partnering', 'against', 'surface', 'opponents', 'result'] | [['2002 fed cup europe / africa group i', 'rr', '26 april 2002', 'angela cardoso', 'georgia', 'clay', 'margalita chakhnashvili salome devidze', '4 - 6 , 3 - 6'], ['2003 fed cup europe / africa group ii', 'rr', '29 april - 1 may 2003', 'ana catarina nogueira', 'norway', 'clay', 'karoline borgersen ina sartz', '6 - 0 , 6 - 2'], ['2003 fed cup europe / africa group ii', 'rr', '29 april - 1 may 2003', 'frederica piedade', 'turkey', 'clay', 'pemra özgen ipek şenoğlu', '6 - 3 , 2 - 6 , 6 - 2'], ['2005 fed cup europe / africa group iii', 'rr', '29 april 2005', 'ana catarina nogueira', 'kenya', 'clay', 'caroline oduor tamara orwa', '6 - 0 , 6 - 0'], ['2005 fed cup europe / africa group iii', 'giii play - offs', '30 april 2005', 'ana catarina nogueira', 'bosnia and herzegovina', 'clay', 'selma babic sanja racic', '6 - 1 , 6 - 3'], ['2006 fed cup europe / africa group ii', 'rr', '26 - 28 april 2006', 'ana catarina nogueira', 'poland', 'clay', 'klaudia jans alicja rosolska', '2 - 6 , 2 - 6'], ['2006 fed cup europe / africa group ii', 'rr', '26 - 28 april 2006', 'ana catarina nogueira', 'latvia', 'clay', 'anastasija sevastova alise vaidere', '6 - 3 , 4 - 6 , 6 - 2'], ['2006 fed cup europe / africa group ii', 'rr', '26 - 28 april 2006', 'ana catarina nogueira', 'greece', 'clay', 'anna gerasimou anna koumantou', '6 - 2 , 3 - 6 , 6 - 4'], ['2009 fed cup europe / africa group ii', 'rr', '22 - 24 april 2009', 'frederica piedade', 'morocco', 'hard', 'fatima el allami nadia lalami', '6 - 0 , 6 - 1'], ['2009 fed cup europe / africa group ii', 'rr', '22 - 24 april 2009', 'frederica piedade', 'latvia', 'hard', 'līga dekmeijere anastasija sevastova', '1 - 6 , 1 - 6'], ['2010 fed cup europe / africa group i', 'rr', '4 - 5 february 2010', 'michelle larcher de brito', 'croatia', 'hard', 'jelena kostanić tošić silvia njirić', '7 - 5 , 6 - 4'], ['2010 fed cup europe / africa group i', 'rr', '4 - 5 february 2010', 'maria joão koehler', 'romania', 'hard', 'irina - camelia begu ioana raluca olaru', '7 - 5 , 7 - 5']] |
1956 cleveland browns season | https://en.wikipedia.org/wiki/1956_Cleveland_Browns_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10651573-1.html.csv | aggregation | the average attendance for games during the 1956 cleveland browns season was 43174 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '43174', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'attendance'], 'result': '43174', 'ind': 0, 'tostr': 'avg { all_rows ; attendance }'}, '43174'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; attendance } ; 43174 } = true', 'tointer': 'the average of the attendance record of all rows is 43174 .'} | round_eq { avg { all_rows ; attendance } ; 43174 } = true | the average of the attendance record of all rows is 43174 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'attendance_4': 4, '43174_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'attendance_4': 'attendance', '43174_5': '43174'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'attendance_4': [0], '43174_5': [1]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'august 10 , 1956', 'college all - stars at chicago', 'w 26 - 0', '75000'], ['2', 'august 19 , 1956', 'san francisco 49ers', 'l 28 - 17', '38741'], ['3', 'august 24 , 1956', 'los angeles rams', 'l 17 - 6', '40175'], ['4', 'september 1 , 1956', 'green bay packers', 'l 21 - 20', '15456'], ['5', 'september 7 , 1956', 'detroit lions', 'l 17 - 0', '48105'], ['6', 'september 15 , 1956', 'detroit lions at akron', 'l 31 - 14', '28201'], ['7', 'september 21 , 1956', 'chicago bears', 'w 24 - 14', '56543']] |
the chicago code | https://en.wikipedia.org/wiki/The_Chicago_Code | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27401228-1.html.csv | count | davey holmes wrote 2 episodes of the chicago code . | {'scope': 'all', 'criterion': 'equal', 'value': 'davey holmes', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'written by', 'davey holmes'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose written by record fuzzily matches to davey holmes .', 'tostr': 'filter_eq { all_rows ; written by ; davey holmes }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; written by ; davey holmes } }', 'tointer': 'select the rows whose written by record fuzzily matches to davey holmes . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; written by ; davey holmes } } ; 2 } = true', 'tointer': 'select the rows whose written by record fuzzily matches to davey holmes . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; written by ; davey holmes } } ; 2 } = true | select the rows whose written by record fuzzily matches to davey holmes . the number of such rows is 2 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'written by_5': 5, 'davey holmes_6': 6, '2_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'written by_5': 'written by', 'davey holmes_6': 'davey holmes', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'written by_5': [0], 'davey holmes_6': [0], '2_7': [2]} | ['no', 'title', 'directed by', 'written by', 'original air date', 'production code', 'us viewers ( million )'] | [['1', 'pilot', 'charles mcdougall', 'shawn ryan', 'february 7 , 2011', '1ata79', '9.43'], ['2', 'hog butcher', 'clark johnson', 'patrick massett & john zinman', 'february 14 , 2011', '1ata01', '7.35'], ['3', 'gillis , chase & babyface', 'guy ferland', 'davey holmes', 'february 21 , 2011', '1ata09', '7.87'], ['4', 'cabrini - green', 'jean de segonzac', 'tim minear & jon worley', 'february 28 , 2011', '1ata10', '8.04'], ['5', "o'leary 's cow", 'clark johnson', 'kevin townsley', 'march 7 , 2011', '1ata03', '7.46'], ['6', 'the gold coin kid', 'lesli linka glatter', 'heather mitchell', 'march 14 , 2011', '1ata02', '7.30'], ['7', 'black hand and the shotgun man', 'billy gierhart', 'davey holmes', 'march 21 , 2011', '1ata04', '6.16'], ['8', 'wild onions', 'adam arkin', 'virgil williams', 'april 11 , 2011', '1ata05', '5.94'], ['9', "st valentine 's day massacre", 'michael offer', 'christal henry', 'april 18 , 2011', '1ata06', '6.38'], ['10', 'bathhouse & hinky dink', "terrence o'hara", 'patrick massett & john zinman', 'may 2 , 2011', '1ata07', '5.60'], ['11', 'black sox', 'michael offer', 'heather mitchell & kevin townsley', 'may 9 , 2011', '1ata08', '5.67'], ['12', 'greylord & gambat', 'paris barclay', 'virgil williams', 'may 16 , 2011', '1ata11', '5.86']] |
1989 masters tournament | https://en.wikipedia.org/wiki/1989_Masters_Tournament | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16514242-1.html.csv | count | three players had a to par count of +5 . | {'scope': 'all', 'criterion': 'equal', 'value': '+5', 'result': '3', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'to par', '+5'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose to par record fuzzily matches to +5 .', 'tostr': 'filter_eq { all_rows ; to par ; +5 }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; to par ; +5 } }', 'tointer': 'select the rows whose to par record fuzzily matches to +5 . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; to par ; +5 } } ; 3 } = true', 'tointer': 'select the rows whose to par record fuzzily matches to +5 . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; to par ; +5 } } ; 3 } = true | select the rows whose to par record fuzzily matches to +5 . 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, 'to par_5': 5, '+5_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', 'to par_5': 'to par', '+5_6': '+5', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'to par_5': [0], '+5_6': [0], '3_7': [2]} | ['player', 'country', 'year ( s ) won', 'total', 'to par', 'finish'] | [['ben crenshaw', 'united states', '1984', '284', '- 4', 't3'], ['seve ballesteros', 'spain', '1980 , 1983', '285', '- 3', 't5'], ['tom watson', 'united states', '1977 , 1981', '290', '+ 2', 't14'], ['jack nicklaus', 'united states', '1963 , 1965 , 1966 , 1984 , 1975 , 1986', '291', '+ 3', '18'], ['bernhard langer', 'west germany', '1985', '293', '+ 5', 't26'], ['larry mize', 'united states', '1987', '293', '+ 5', 't26'], ['fuzzy zoeller', 'united states', '1979', '293', '+ 5', 't26'], ['tommy aaron', 'united states', '1973', '298', '+ 10', 't38'], ['charles coody', 'united states', '1971', '298', '+ 10', 't38'], ['raymond floyd', 'united states', '1976', '298', '+ 10', 't38'], ['george archer', 'united states', '1969', '298', '+ 12', 't43']] |
wwfm | https://en.wikipedia.org/wiki/WWFM | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12472016-2.html.csv | unique | w230aa is the only call sign with an erp w of 27 . | {'scope': 'all', 'row': '6', 'col': '4', 'col_other': '1', 'criterion': 'equal', 'value': '27', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'erp w', '27'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose erp w record is equal to 27 .', 'tostr': 'filter_eq { all_rows ; erp w ; 27 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; erp w ; 27 } }', 'tointer': 'select the rows whose erp w record is equal to 27 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'erp w', '27'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose erp w record is equal to 27 .', 'tostr': 'filter_eq { all_rows ; erp w ; 27 }'}, 'call sign'], 'result': 'w230aa', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; erp w ; 27 } ; call sign }'}, 'w230aa'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; erp w ; 27 } ; call sign } ; w230aa }', 'tointer': 'the call sign record of this unqiue row is w230aa .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; erp w ; 27 } } ; eq { hop { filter_eq { all_rows ; erp w ; 27 } ; call sign } ; w230aa } } = true', 'tointer': 'select the rows whose erp w record is equal to 27 . there is only one such row in the table . the call sign record of this unqiue row is w230aa .'} | and { only { filter_eq { all_rows ; erp w ; 27 } } ; eq { hop { filter_eq { all_rows ; erp w ; 27 } ; call sign } ; w230aa } } = true | select the rows whose erp w record is equal to 27 . there is only one such row in the table . the call sign record of this unqiue row is w230aa . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'erp w_7': 7, '27_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'call sign_9': 9, 'w230aa_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'erp w_7': 'erp w', '27_8': '27', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'call sign_9': 'call sign', 'w230aa_10': 'w230aa'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'erp w_7': [0], '27_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'call sign_9': [2], 'w230aa_10': [3]} | ['call sign', 'frequency mhz', 'city of license', 'erp w', 'class', 'fcc info'] | [['k216fw', '91.1 fm', 'steamboat springs , colorado', '10', 'd', 'fcc'], ['w224au', '92.7 fm', 'allentown , pennsylvania', '8', 'd', 'fcc'], ['w226aa', '93.1 fm', 'easton , pennsylvania', '150', 'd', 'fcc'], ['w245ac', '96.9 fm', 'harmony township , new jersey', '10', 'd', 'fcc'], ['w300ac', '107.9 fm', 'chatsworth , new jersey', '35', 'd', 'fcc'], ['w230aa', '93.9 fm', 'atlantic city , new jersey', '27', 'd', 'fcc'], ['w284bw', '104.7 fm', 'franklin township , somerset county , new jersey', '13', 'd', 'fcc']] |
alexander kudryavtsev | https://en.wikipedia.org/wiki/Alexander_Kudryavtsev | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18621753-7.html.csv | unique | the eckental tournament was the only one in which alexander kudryavtsev used a carpet surface . | {'scope': 'all', 'row': '8', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'carpet', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'surface', 'carpet'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose surface record fuzzily matches to carpet .', 'tostr': 'filter_eq { all_rows ; surface ; carpet }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; surface ; carpet } }', 'tointer': 'select the rows whose surface record fuzzily matches to carpet . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'surface', 'carpet'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose surface record fuzzily matches to carpet .', 'tostr': 'filter_eq { all_rows ; surface ; carpet }'}, 'tournament'], 'result': 'eckental , germany', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; surface ; carpet } ; tournament }'}, 'eckental , germany'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; surface ; carpet } ; tournament } ; eckental , germany }', 'tointer': 'the tournament record of this unqiue row is eckental , germany .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; surface ; carpet } } ; eq { hop { filter_eq { all_rows ; surface ; carpet } ; tournament } ; eckental , germany } } = true', 'tointer': 'select the rows whose surface record fuzzily matches to carpet . there is only one such row in the table . the tournament record of this unqiue row is eckental , germany .'} | and { only { filter_eq { all_rows ; surface ; carpet } } ; eq { hop { filter_eq { all_rows ; surface ; carpet } ; tournament } ; eckental , germany } } = true | select the rows whose surface record fuzzily matches to carpet . there is only one such row in the table . the tournament record of this unqiue row is eckental , germany . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'surface_7': 7, 'carpet_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'tournament_9': 9, 'eckental , germany_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'surface_7': 'surface', 'carpet_8': 'carpet', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'tournament_9': 'tournament', 'eckental , germany_10': 'eckental , germany'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'surface_7': [0], 'carpet_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'tournament_9': [2], 'eckental , germany_10': [3]} | ['date', 'tournament', 'surface', 'partner', 'opponent in final', 'score'] | [['11 july 2004', 'oberstaufen , germany', 'clay', 'vadim davletshin', 'valentino pest alexander waske', '4 - 6 , 6 - 3 , 7 - 6'], ['27 may 2006', 'kiev , ukraine', 'clay', 'alexander krasnorutskiy', 'andrei stoliarov aleksandr yarmola', '6 - 3 , 3 - 6 , 6 - 2'], ['4 june 2006', 'cherkasy , ukraine', 'clay', 'alexander krasnorutskiy', 'sergei bubka aleksandr nedovesov', '6 - 3 , 4 - 6 , 6 - 2'], ['25 june 2006', 'minsk , belarus', 'hard', 'alexander krasnorutskiy', 'alexander bury kyril harbatsiuk', '7 - 5 , 6 - 3'], ['27 august 2006', 'poznań , poland', 'clay', 'alexander krasnorutskiy', 'tomasz bednarek maciej dilaj', '2 - 6 , 7 - 5 , 6 - 1'], ['26 november 2006', 'mosrentgen , russia', 'hard', 'alexander krasnorutskiy', 'sarvar ikramov alexey tikhonov', '6 - 1 , 6 - 1'], ['20 august 2011', 'karshi , kazakhstan', 'hard', 'michail elgin', 'konstantin kravchuk denys molchanov', '3 - 6 , 6 - 3 ,'], ['6 november 2011', 'eckental , germany', 'carpet', 'andre begemann', 'james cerretani adil shamasdin', '6 - 3 , 3 - 6 ,']] |
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