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eurovision song contest 1985 | https://en.wikipedia.org/wiki/Eurovision_Song_Contest_1985 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-185276-2.html.csv | count | in the 1985 eurovision song contest , two of the songs were in the english language . | {'scope': 'all', 'criterion': 'equal', 'value': 'english', 'result': '2', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'language', 'english'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose language record fuzzily matches to english .', 'tostr': 'filter_eq { all_rows ; language ; english }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; language ; english } }', 'tointer': 'select the rows whose language record fuzzily matches to english . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; language ; english } } ; 2 } = true', 'tointer': 'select the rows whose language record fuzzily matches to english . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; language ; english } } ; 2 } = true | select the rows whose language record fuzzily matches to english . 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, 'language_5': 5, 'english_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', 'language_5': 'language', 'english_6': 'english', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'language_5': [0], 'english_6': [0], '2_7': [2]} | ['draw', 'language', 'song', 'english translation', 'place', 'points'] | [['01', 'english', 'wait until the weekend comes', '-', '6', '91'], ['02', 'finnish', 'eläköön elämä', 'long live life', '9', '58'], ['03', 'greek', 'to katalava arga ( το κατάλαβα αργά )', 'i realised it too late', '16', '15'], ['04', 'danish', "sku ' du spørg ' fra no'en", 'what business is it of yours', '11', '41'], ['05', 'spanish', 'la fiesta terminó', "the party 's over", '14', '36'], ['06', 'french', 'femme dans ses rêves aussi', 'woman in her dreams too', '10', '56'], ['07', 'turkish', 'didai didai dai', '-', '14', '36'], ['08', 'dutch', 'laat me nu gaan', 'let me go now', '19', '7'], ['09', 'portuguese', 'penso em ti , eu sei', 'thinking of you , i know', '18', '9'], ['10', 'german', 'für alle', 'for everyone', '2', '105'], ['11', 'hebrew', 'olé , olé ( עולה , עולה )', 'going up and up', '5', '93'], ['12', 'italian', 'magic oh magic', '-', '7', '78'], ['13', 'norwegian', 'la det swinge', 'let it swing', '1', '123'], ['14', 'english', 'love is', '-', '4', '100'], ['15', 'german', 'piano , piano', 'slowly , slowly', '12', '39'], ['16', 'swedish', 'bra vibrationer', 'good vibrations', '3', '103'], ['17', 'german', 'kinder dieser welt', 'children of this world', '8', '60'], ['18', 'french', 'children , kinder , enfants', 'children', '13', '37'], ['19', 'greek', 'miazoume ( μοιάζουμε )', 'we are alike', '16', '15']] |
loonie | https://en.wikipedia.org/wiki/Loonie | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18400-2.html.csv | count | the artist arnold nogy has been responsible for the art of three of the special loonies series . | {'scope': 'all', 'criterion': 'equal', 'value': 'arnold nogy', 'result': '3', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'artist', 'arnold nogy'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose artist record fuzzily matches to arnold nogy .', 'tostr': 'filter_eq { all_rows ; artist ; arnold nogy }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; artist ; arnold nogy } }', 'tointer': 'select the rows whose artist record fuzzily matches to arnold nogy . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; artist ; arnold nogy } } ; 3 } = true', 'tointer': 'select the rows whose artist record fuzzily matches to arnold nogy . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; artist ; arnold nogy } } ; 3 } = true | select the rows whose artist record fuzzily matches to arnold nogy . 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, 'artist_5': 5, 'arnold nogy_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', 'artist_5': 'artist', 'arnold nogy_6': 'arnold nogy', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'artist_5': [0], 'arnold nogy_6': [0], '3_7': [2]} | ['year', 'theme', 'artist', 'mintage', 'issue price'] | [['2002', '15th anniversary loonie', 'dora de pãdery - hunt', '67672', '39.95'], ['2004', 'jack miner bird sanctuary', 'susan taylor', '46493', '39.95'], ['2005', 'tufted puffin', 'n / a', '39818', '39.95'], ['2006', 'snowy owl', 'glen loates', '39935', '44.95'], ['2007', 'trumpeter swan', 'kerri burnett', '40000', '45.95'], ['2008', 'common eider', 'mark hobson', '40000', '47.95'], ['2009', 'great blue heron', 'chris jordison', '40000', '47.95'], ['2010', 'northern harrier', 'arnold nogy', '35000', '49.95'], ['2011', 'great gray owl', 'arnold nogy', '35000', '49.95'], ['2012', '25th anniversary loonie', 'arnold nogy', '35000', '49.95']] |
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 | unique | the 15 october 2013 competition is the only one for sebastian prödl that was a 2014 fifa world cup qualification . | {'scope': 'all', 'row': '4', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': '2014 fifa world cup qualification', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'competition', '2014 fifa world cup qualification'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose competition record fuzzily matches to 2014 fifa world cup qualification .', 'tostr': 'filter_eq { all_rows ; competition ; 2014 fifa world cup qualification }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; competition ; 2014 fifa world cup qualification } }', 'tointer': 'select the rows whose competition record fuzzily matches to 2014 fifa world cup qualification . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'competition', '2014 fifa world cup qualification'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose competition record fuzzily matches to 2014 fifa world cup qualification .', 'tostr': 'filter_eq { all_rows ; competition ; 2014 fifa world cup qualification }'}, 'date'], 'result': '15 october 2013', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; competition ; 2014 fifa world cup qualification } ; date }'}, '15 october 2013'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; competition ; 2014 fifa world cup qualification } ; date } ; 15 october 2013 }', 'tointer': 'the date record of this unqiue row is 15 october 2013 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; competition ; 2014 fifa world cup qualification } } ; eq { hop { filter_eq { all_rows ; competition ; 2014 fifa world cup qualification } ; date } ; 15 october 2013 } } = true', 'tointer': 'select the rows whose competition record fuzzily matches to 2014 fifa world cup qualification . there is only one such row in the table . the date record of this unqiue row is 15 october 2013 .'} | and { only { filter_eq { all_rows ; competition ; 2014 fifa world cup qualification } } ; eq { hop { filter_eq { all_rows ; competition ; 2014 fifa world cup qualification } ; date } ; 15 october 2013 } } = true | select the rows whose competition record fuzzily matches to 2014 fifa world cup qualification . there is only one such row in the table . the date record of this unqiue row is 15 october 2013 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'competition_7': 7, '2014 fifa world cup qualification_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, '15 october 2013_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'competition_7': 'competition', '2014 fifa world cup qualification_8': '2014 fifa world cup qualification', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', '15 october 2013_10': '15 october 2013'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'competition_7': [0], '2014 fifa world cup qualification_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], '15 october 2013_10': [3]} | ['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']] |
suburban league | https://en.wikipedia.org/wiki/Suburban_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-28051859-3.html.csv | comparative | mogadore 's tenure in the suburban league began before field 's tenure began . | {'row_1': '6', 'row_2': '3', 'col': '5', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'school', 'mogadore'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose school record fuzzily matches to mogadore .', 'tostr': 'filter_eq { all_rows ; school ; mogadore }'}, 'tenure'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; school ; mogadore } ; tenure }', 'tointer': 'select the rows whose school record fuzzily matches to mogadore . take the tenure record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'school', 'field'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose school record fuzzily matches to field .', 'tostr': 'filter_eq { all_rows ; school ; field }'}, 'tenure'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; school ; field } ; tenure }', 'tointer': 'select the rows whose school record fuzzily matches to field . take the tenure record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; school ; mogadore } ; tenure } ; hop { filter_eq { all_rows ; school ; field } ; tenure } } = true', 'tointer': 'select the rows whose school record fuzzily matches to mogadore . take the tenure record of this row . select the rows whose school record fuzzily matches to field . take the tenure record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; school ; mogadore } ; tenure } ; hop { filter_eq { all_rows ; school ; field } ; tenure } } = true | select the rows whose school record fuzzily matches to mogadore . take the tenure record of this row . select the rows whose school record fuzzily matches to field . take the tenure 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, 'school_7': 7, 'mogadore_8': 8, 'tenure_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'school_11': 11, 'field_12': 12, 'tenure_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', 'school_7': 'school', 'mogadore_8': 'mogadore', 'tenure_9': 'tenure', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'school_11': 'school', 'field_12': 'field', 'tenure_13': 'tenure'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'school_7': [0], 'mogadore_8': [0], 'tenure_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'school_11': [1], 'field_12': [1], 'tenure_13': [3]} | ['school', 'nickname', 'location', 'colors', 'tenure'] | [['barberton', 'magics', 'barberton , summit county', 'purple , white', '2005 - 2011'], ['coventry', 'comets', 'coventry twp , summit county', 'blue , gold', '1969 - 1983'], ['field', 'falcons', 'brimfield , portage county', 'red , white , black', '1978 - 1990'], ['hudson', 'explorers', 'hudson , summit county', 'navy blue , white', '1949 - 1997'], ['manchester', 'panthers', 'new franklin , summit county', 'red , black', '1949 - 1976'], ['mogadore', 'wildcats', 'mogadore , portage county', 'green , white', '1958 - 1968'], ['norton', 'panthers', 'norton , summit county', 'red , black , white', '1972 - 2005'], ['twinsburg', 'tigers', 'twinsburg , summit county', 'blue , white', '1958 - 1964']] |
netflow | https://en.wikipedia.org/wiki/NetFlow | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1206114-2.html.csv | majority | the majority of netflow implementations are software implementations . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'software', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'implementation', 'software'], 'result': True, 'ind': 0, 'tointer': 'for the implementation records of all rows , most of them fuzzily match to software .', 'tostr': 'most_eq { all_rows ; implementation ; software } = true'} | most_eq { all_rows ; implementation ; software } = true | for the implementation records of all rows , most of them fuzzily match to software . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'implementation_3': 3, 'software_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'implementation_3': 'implementation', 'software_4': 'software'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'implementation_3': [0], 'software_4': [0]} | ['vendor and type', 'models', 'netflow version', 'implementation', 'comments'] | [['cisco ios - xr routers', 'crs , asr9000 old 12000', 'v5 , v8 , v9', 'software running on line card cpu', 'comprehensive support for ipv6 and mpls'], ['alcatel - lucent routers', '7750sr', 'v5 , v8 , v9 , ipfix', 'software running on central processor module', 'ipv6 or mpls using iom3 line cards or better'], ['huawei routers', 'ne5000e ne40e / x ne80e', 'v5 , v9', 'software running on service cards', 'support for ipv6 or mpls is unknown'], ['enterasys switches', 's - serie and n - serie', 'v5 , v9', 'dedicated hardware', 'ipv6 support is unknown'], ['pc and servers', 'linux freebsd netbsd openbsd', 'v5 , v9 , ipfix', 'software like fprobe , ipt - netflow or pflow', 'ipv6 support depend on the software used'], ['vmware servers', 'vsphere 5 . x', 'v5', 'software', 'ipv6 support is unknown']] |
list of csi : ny characters | https://en.wikipedia.org/wiki/List_of_CSI%3A_NY_characters | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11240028-5.html.csv | ordinal | out of the list of csi : ny characters who were criminals , character shane casey had the second-highest murder count . | {'row': '5', 'col': '3', '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', 'crime', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; crime ; 2 }'}, 'character'], 'result': 'shane casey', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; crime ; 2 } ; character }'}, 'shane casey'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; crime ; 2 } ; character } ; shane casey } = true', 'tointer': 'select the row whose crime record of all rows is 2nd maximum . the character record of this row is shane casey .'} | eq { hop { nth_argmax { all_rows ; crime ; 2 } ; character } ; shane casey } = true | select the row whose crime record of all rows is 2nd maximum . the character record of this row is shane casey . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'crime_5': 5, '2_6': 6, 'character_7': 7, 'shane casey_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', 'crime_5': 'crime', '2_6': '2', 'character_7': 'character', 'shane casey_8': 'shane casey'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'crime_5': [0], '2_6': [0], 'character_7': [1], 'shane casey_8': [2]} | ['character', 'portrayed by', 'crime', 'first appearance', 'last appearance'] | [['sonny sassone', 'michael deluise', 'murder ( 2 counts )', 'tanglewood', 'run silent , run deep'], ['frankie mala', 'ed quinn', 'attempted murder ( attacked stella )', 'grand murder at central station', 'all access'], ['henry darius', 'james badge dale', 'murder ( 15 counts )', 'felony flight ( csi : miami crossover )', 'manhattan manhunt'], ['dj pratt', 'chad williams', 'murder / rape ( 1 / 2 counts ) ( killed aiden )', 'summer in the city', 'heroes'], ['shane casey', 'edward furlong', 'murder ( 8 counts )', 'hung out to dry', 'the 34th floor'], ['clay dobson', 'joey lawrence', 'murder ( 3 counts )', 'past imperfect', 'comes around'], ['andrew drew bedford ( aka 333 stalker )', 'kerr smith', 'attempted murder ( 6 counts )', 'the deep', 'the thing about heroes'], ['suspect x', 'kam heskin', 'murder ( 6 counts )', 'down the rabbit hole', 'doa for a day'], ['cabbie killer', 'ryan locke', 'murder ( 6 counts )', 'like water for murder', 'taxi'], ['ethan scott ( aka joe )', 'elias koteas', 'murder ( 2 counts )', 'hostage', 'veritas'], ['sebastian diakos', 'adoni maropis', 'murder ( 2 counts )', 'the cost of living', 'point of no return'], ['george kolovos', 'paul papadakis', 'murder ( 1 count )', 'the cost of living', 'grounds for deception'], ['hollis eckhart ( aka the compass killer )', 'skeet ulrich', 'murder ( 3 counts )', "lat 40 degree 47 ' n / long 73 degree 58 ' w", 'manhattanhenge'], ['raymond harris', 'clifton collins , jr', 'murder ( 2 counts )', 'nothing for something', 'life sentence'], ['john curtis', 'jason wiles', 'rape ( 5 counts )', 'crushed', 'means to an end']] |
haarlem baseball week | https://en.wikipedia.org/wiki/Haarlem_Baseball_Week | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18532667-2.html.csv | count | 13 nations were represented in the haarlem baseball week tournament . | {'scope': 'all', 'criterion': 'all', 'value': 'n/a', 'result': '13', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_all', 'args': ['all_rows', 'nation'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nation record is arbitrary .', 'tostr': 'filter_all { all_rows ; nation }'}], 'result': '13', 'ind': 1, 'tostr': 'count { filter_all { all_rows ; nation } }', 'tointer': 'select the rows whose nation record is arbitrary . the number of such rows is 13 .'}, '13'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_all { all_rows ; nation } } ; 13 } = true', 'tointer': 'select the rows whose nation record is arbitrary . the number of such rows is 13 .'} | eq { count { filter_all { all_rows ; nation } } ; 13 } = true | select the rows whose nation record is arbitrary . the number of such rows is 13 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_all_0': 0, 'all_rows_4': 4, 'nation_5': 5, '13_6': 6} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_all_0': 'filter_all', 'all_rows_4': 'all_rows', 'nation_5': 'nation', '13_6': '13'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_all_0': [1], 'all_rows_4': [0], 'nation_5': [0], '13_6': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'united states', '13', '7', '10', '30'], ['2', 'cuba', '5', '6', '2', '13'], ['3', 'netherlands', '3', '7', '7', '17'], ['4', 'japan', '3', '1', '2', '6'], ['5', 'canada', '1', '1', '0', '2'], ['6', 'netherlands antilles', '1', '0', '1', '2'], ['7', 'south korea', '0', '2', '0', '2'], ['8', 'germany', '0', '1', '1', '2'], ['9', 'australia', '0', '1', '0', '1'], ['9', 'puerto rico', '0', '1', '0', '1'], ['10', 'chinese taipei', '0', '0', '1', '1'], ['10', 'france', '0', '0', '1', '1'], ['10', 'italy', '0', '0', '1', '1']] |
rowing at the 2008 summer olympics - men 's single sculls | https://en.wikipedia.org/wiki/Rowing_at_the_2008_Summer_Olympics_%E2%80%93_Men%27s_single_sculls | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18662643-6.html.csv | ordinal | peter hardcastle recorded the 2nd fasted time of the 2008 olympics men 's single sculls rowing competition . | {'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', 'time', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; time ; 2 }'}, 'athlete'], 'result': 'peter hardcastle', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; time ; 2 } ; athlete }'}, 'peter hardcastle'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; time ; 2 } ; athlete } ; peter hardcastle } = true', 'tointer': 'select the row whose time record of all rows is 2nd minimum . the athlete record of this row is peter hardcastle .'} | eq { hop { nth_argmin { all_rows ; time ; 2 } ; athlete } ; peter hardcastle } = true | select the row whose time record of all rows is 2nd minimum . the athlete record of this row is peter hardcastle . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'time_5': 5, '2_6': 6, 'athlete_7': 7, 'peter hardcastle_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', 'time_5': 'time', '2_6': '2', 'athlete_7': 'athlete', 'peter hardcastle_8': 'peter hardcastle'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'time_5': [0], '2_6': [0], 'athlete_7': [1], 'peter hardcastle_8': [2]} | ['rank', 'athlete', 'country', 'time', 'notes'] | [['1', 'alan campbell', 'great britain', '7:14.98', 'q'], ['2', 'peter hardcastle', 'australia', '7:17.74', 'q'], ['3', 'patrick loliger', 'mexico', '7:22.55', 'q'], ['4', 'ken jurkowski', 'united states', '7:25.13', 'q'], ['5', 'ruslan naurzaliev', 'uzbekistan', '7:58.43', 'se / f']] |
2006 - 07 macedonian cup | https://en.wikipedia.org/wiki/2006%E2%80%9307_Macedonian_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17065288-2.html.csv | unique | the only game of the macedonian cup for 2006 - 07 having a team score a 2nd . leg score of 4 is rabotnički vs. ilinden . | {'scope': 'all', 'row': '6', 'col': '5', 'col_other': '1,3', 'criterion': 'fuzzily_match', 'value': '4', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '2nd leg', '4'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 2nd leg record fuzzily matches to 4 .', 'tostr': 'filter_eq { all_rows ; 2nd leg ; 4 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; 2nd leg ; 4 } }', 'tointer': 'select the rows whose 2nd leg record fuzzily matches to 4 . 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', '2nd leg', '4'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 2nd leg record fuzzily matches to 4 .', 'tostr': 'filter_eq { all_rows ; 2nd leg ; 4 }'}, 'team 1'], 'result': 'rabotnički', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; 2nd leg ; 4 } ; team 1 }'}, 'rabotnički'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; 2nd leg ; 4 } ; team 1 } ; rabotnički }', 'tointer': 'the team 1 record of this unqiue row is rabotnički .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '2nd leg', '4'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 2nd leg record fuzzily matches to 4 .', 'tostr': 'filter_eq { all_rows ; 2nd leg ; 4 }'}, 'team 2'], 'result': 'ilinden', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; 2nd leg ; 4 } ; team 2 }'}, 'ilinden'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; 2nd leg ; 4 } ; team 2 } ; ilinden }', 'tointer': 'the team 2 record of this unqiue row is ilinden .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; 2nd leg ; 4 } ; team 1 } ; rabotnički } ; eq { hop { filter_eq { all_rows ; 2nd leg ; 4 } ; team 2 } ; ilinden } }', 'tointer': 'the team 1 record of this unqiue row is rabotnički . the team 2 record of this unqiue row is ilinden .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; 2nd leg ; 4 } } ; and { eq { hop { filter_eq { all_rows ; 2nd leg ; 4 } ; team 1 } ; rabotnički } ; eq { hop { filter_eq { all_rows ; 2nd leg ; 4 } ; team 2 } ; ilinden } } } = true', 'tointer': 'select the rows whose 2nd leg record fuzzily matches to 4 . there is only one such row in the table . the team 1 record of this unqiue row is rabotnički . the team 2 record of this unqiue row is ilinden .'} | and { only { filter_eq { all_rows ; 2nd leg ; 4 } } ; and { eq { hop { filter_eq { all_rows ; 2nd leg ; 4 } ; team 1 } ; rabotnički } ; eq { hop { filter_eq { all_rows ; 2nd leg ; 4 } ; team 2 } ; ilinden } } } = true | select the rows whose 2nd leg record fuzzily matches to 4 . there is only one such row in the table . the team 1 record of this unqiue row is rabotnički . the team 2 record of this unqiue row is ilinden . | 10 | 8 | {'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, '2nd leg_10': 10, '4_11': 11, 'and_6': 6, 'str_eq_3': 3, 'str_hop_2': 2, 'team 1_12': 12, 'rabotnički_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'team 2_14': 14, 'ilinden_15': 15} | {'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_9': 'all_rows', '2nd leg_10': '2nd leg', '4_11': '4', 'and_6': 'and', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'team 1_12': 'team 1', 'rabotnički_13': 'rabotnički', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'team 2_14': 'team 2', 'ilinden_15': 'ilinden'} | {'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], '2nd leg_10': [0], '4_11': [0], 'and_6': [7], 'str_eq_3': [6], 'str_hop_2': [3], 'team 1_12': [2], 'rabotnički_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'team 2_14': [4], 'ilinden_15': [5]} | ['team 1', 'agg', 'team 2', '1st leg', '2nd leg'] | [['pobeda', '3 - 0', 'shkëndija 79', '2 - 0', '1 - 0'], ['vardar', '5 - 1', 'metalurg', '4 - 1', '1 - 0'], ['drita', '4 - 2', 'bregalnica kraun', '3 - 0', '1 - 2'], ['gostivar', '3 - 6', 'renova', '1 - 4', '2 - 2'], ['milano', '4 - 3', 'baškimi', '2 - 1', '2 - 2'], ['rabotnički', '5 - 4', 'ilinden', '4 - 0', '1 - 4'], ['makedonija', '0 - 2', 'meridian fcu', '0 - 2', '0 - 0'], ['madžari solidarnost', '2 - 4', 'pelister', '2 - 2', '0 - 2']] |
1976 los angeles rams season | https://en.wikipedia.org/wiki/1976_Los_Angeles_Rams_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11159520-2.html.csv | unique | the only los angeles rams game in november 1976 with an attendance of over 60000 was on november 14th . | {'scope': 'subset', 'row': '10', 'col': '5', 'col_other': '2', 'criterion': 'greater_than', 'value': '60000', 'subset': {'col': '2', 'criterion': 'fuzzily_match', 'value': 'november'}} | {'func': 'only', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'november'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; date ; november }', 'tointer': 'select the rows whose date record fuzzily matches to november .'}, 'attendance', '60000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to november . among these rows , select the rows whose attendance record is greater than 60000 .', 'tostr': 'filter_greater { filter_eq { all_rows ; date ; november } ; attendance ; 60000 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_greater { filter_eq { all_rows ; date ; november } ; attendance ; 60000 } } = true', 'tointer': 'select the rows whose date record fuzzily matches to november . among these rows , select the rows whose attendance record is greater than 60000 . there is only one such row in the table .'} | only { filter_greater { filter_eq { all_rows ; date ; november } ; attendance ; 60000 } } = true | select the rows whose date record fuzzily matches to november . among these rows , select the rows whose attendance record is greater than 60000 . 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, 'date_5': 5, 'november_6': 6, 'attendance_7': 7, '60000_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', 'date_5': 'date', 'november_6': 'november', 'attendance_7': 'attendance', '60000_8': '60000'} | {'only_2': [3], 'result_3': [], 'filter_greater_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'date_5': [0], 'november_6': [0], 'attendance_7': [1], '60000_8': [1]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 12 , 1976', 'atlanta falcons', 'w 30 - 14', '53607'], ['2', 'september 19 , 1976', 'minnesota vikings', 't 10 - 10', '47310'], ['3', 'september 26 , 1976', 'new york giants', 'w 24 - 10', '60698'], ['4', 'october 3 , 1976', 'miami dolphins', 'w 31 - 28', '60753'], ['5', 'october 11 , 1976', 'san francisco 49ers', 'l 16 - 0', '80532'], ['6', 'october 17 , 1976', 'chicago bears', 'w 20 - 12', '71751'], ['7', 'october 24 , 1976', 'new orleans saints', 'w 16 - 10', '51984'], ['8', 'october 31 , 1976', 'seattle seahawks', 'w 45 - 6', '52035'], ['9', 'november 7 , 1976', 'cincinnati bengals', 'l 20 - 12', '52480'], ['10', 'november 14 , 1976', 'st louis cardinals', 'l 30 - 28', '64698'], ['11', 'november 21 , 1976', 'san francisco 49ers', 'w 23 - 3', '58573'], ['12', 'november 28 , 1976', 'new orleans saints', 'w 33 - 14', '54906'], ['13', 'december 4 , 1976', 'atlanta falcons', 'w 59 - 0', '57366'], ['14', 'december 11 , 1976', 'detroit lions', 'w 20 - 17', '73470']] |
2003 - 04 european challenge cup | https://en.wikipedia.org/wiki/2003%E2%80%9304_European_Challenge_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27987767-3.html.csv | aggregation | in the 2003-04 european challenge cup the players had a points margin average of 20 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '20', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'points margin'], 'result': '20', 'ind': 0, 'tostr': 'avg { all_rows ; points margin }'}, '20'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; points margin } ; 20 } = true', 'tointer': 'the average of the points margin record of all rows is 20 .'} | round_eq { avg { all_rows ; points margin } ; 20 } = true | the average of the points margin record of all rows is 20 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'points margin_4': 4, '20_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'points margin_4': 'points margin', '20_5': '20'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'points margin_4': [0], '20_5': [1]} | ['proceed to quarter - final', 'match points', 'aggregate score', 'points margin', 'eliminated from competition'] | [['nec harlequins', '4 - 0', '89 - 25', '64', 'montauban'], ['béziers', '4 - 0', '43 - 23', '20', 'grenoble'], ['bath', '4 - 0', '58 - 42', '16', 'colomiers'], ['connacht', '2 - 2', '35 - 17', '18', 'pau'], ['narbonne', '2 - 2', '42 - 30', '12', 'london irish'], ['brive', '2 - 2', '58 - 48', '10', 'castres olympique'], ['montferrand', '2 - 2', '28 - 23', '5', 'newcastle falcons']] |
1967 - 68 new york rangers season | https://en.wikipedia.org/wiki/1967%E2%80%9368_New_York_Rangers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17311408-4.html.csv | unique | the only game against the detroit red wings in the 1967 - 68 new york rangers season to finish in a draw was on 6 december . | {'scope': 'subset', 'row': '3', 'col': '4', 'col_other': '2', 'criterion': 'equal', 'value': '3-3', 'subset': {'col': '3', 'criterion': 'equal', 'value': 'detroit red wings'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'detroit red wings'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; opponent ; detroit red wings }', 'tointer': 'select the rows whose opponent record fuzzily matches to detroit red wings .'}, 'score', '3-3'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to detroit red wings . among these rows , select the rows whose score record fuzzily matches to 3-3 .', 'tostr': 'filter_eq { filter_eq { all_rows ; opponent ; detroit red wings } ; score ; 3-3 }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; opponent ; detroit red wings } ; score ; 3-3 } }', 'tointer': 'select the rows whose opponent record fuzzily matches to detroit red wings . among these rows , select the rows whose score record fuzzily matches to 3-3 . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'opponent', 'detroit red wings'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; opponent ; detroit red wings }', 'tointer': 'select the rows whose opponent record fuzzily matches to detroit red wings .'}, 'score', '3-3'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose opponent record fuzzily matches to detroit red wings . among these rows , select the rows whose score record fuzzily matches to 3-3 .', 'tostr': 'filter_eq { filter_eq { all_rows ; opponent ; detroit red wings } ; score ; 3-3 }'}, 'december'], 'result': '6', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; opponent ; detroit red wings } ; score ; 3-3 } ; december }'}, '6'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; opponent ; detroit red wings } ; score ; 3-3 } ; december } ; 6 }', 'tointer': 'the december record of this unqiue row is 6 .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; opponent ; detroit red wings } ; score ; 3-3 } } ; eq { hop { filter_eq { filter_eq { all_rows ; opponent ; detroit red wings } ; score ; 3-3 } ; december } ; 6 } } = true', 'tointer': 'select the rows whose opponent record fuzzily matches to detroit red wings . among these rows , select the rows whose score record fuzzily matches to 3-3 . there is only one such row in the table . the december record of this unqiue row is 6 .'} | and { only { filter_eq { filter_eq { all_rows ; opponent ; detroit red wings } ; score ; 3-3 } } ; eq { hop { filter_eq { filter_eq { all_rows ; opponent ; detroit red wings } ; score ; 3-3 } ; december } ; 6 } } = true | select the rows whose opponent record fuzzily matches to detroit red wings . among these rows , select the rows whose score record fuzzily matches to 3-3 . there is only one such row in the table . the december record of this unqiue row is 6 . | 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, 'opponent_8': 8, 'detroit red wings_9': 9, 'score_10': 10, '3-3_11': 11, 'eq_4': 4, 'num_hop_3': 3, 'december_12': 12, '6_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', 'opponent_8': 'opponent', 'detroit red wings_9': 'detroit red wings', 'score_10': 'score', '3-3_11': '3-3', 'eq_4': 'eq', 'num_hop_3': 'num_hop', 'december_12': 'december', '6_13': '6'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_str_eq_0': [1], 'all_rows_7': [0], 'opponent_8': [0], 'detroit red wings_9': [0], 'score_10': [1], '3-3_11': [1], 'eq_4': [5], 'num_hop_3': [4], 'december_12': [3], '6_13': [4]} | ['game', 'december', 'opponent', 'score', 'record'] | [['21', '2', 'pittsburgh penguins', '4 - 1', '10 - 8 - 3'], ['22', '3', 'los angeles kings', '4 - 2', '11 - 8 - 3'], ['23', '6', 'detroit red wings', '3 - 3', '11 - 8 - 4'], ['24', '7', 'boston bruins', '3 - 1', '11 - 9 - 4'], ['25', '9', 'detroit red wings', '3 - 2', '11 - 10 - 4'], ['26', '10', 'montreal canadiens', '3 - 2', '12 - 10 - 4'], ['27', '13', 'chicago black hawks', '5 - 2', '12 - 11 - 4'], ['28', '16', 'toronto maple leafs', '4 - 2', '12 - 12 - 4'], ['29', '17', 'st louis blues', '5 - 3', '13 - 12 - 4'], ['30', '20', 'detroit red wings', '2 - 0', '14 - 12 - 4'], ['31', '23', 'boston bruins', '4 - 0', '14 - 13 - 4'], ['32', '25', 'philadelphia flyers', '3 - 1', '15 - 13 - 4'], ['33', '27', 'minnesota north stars', '3 - 3', '15 - 13 - 5'], ['34', '30', 'chicago black hawks', '3 - 3', '15 - 13 - 6'], ['35', '31', 'toronto maple leafs', '4 - 0', '16 - 13 - 6']] |
2008 - 09 süper lig | https://en.wikipedia.org/wiki/2008%E2%80%9309_S%C3%BCper_Lig | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17356873-2.html.csv | majority | in the süper lig , the manner of departure for most of the managers , was that they resigned . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'resigned', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'manner of departure', 'resigned'], 'result': True, 'ind': 0, 'tointer': 'for the manner of departure records of all rows , most of them fuzzily match to resigned .', 'tostr': 'most_eq { all_rows ; manner of departure ; resigned } = true'} | most_eq { all_rows ; manner of departure ; resigned } = true | for the manner of departure records of all rows , most of them fuzzily match to resigned . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'manner of departure_3': 3, 'resigned_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'manner of departure_3': 'manner of departure', 'resigned_4': 'resigned'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'manner of departure_3': [0], 'resigned_4': [0]} | ['team', 'outgoing manager', 'manner of departure', 'date of vacancy', 'replaced by', 'date of appointment'] | [['konyaspor', 'raşit çetiner', 'sacked', '17 september 2008', 'giray bulak', '24 september 2008'], ['kocaelispor', 'engin ipekoğlu', 'sacked', '25 september 2008', 'yılmaz vural', '28 september 2008'], ['beşiktaş', 'ertuğrul sağlam', 'resigned', '7 october 2008', 'mustafa denizli', '9 october 2008'], ['ankaragücü', 'hakan kutlu', 'sacked', '20 october 2008', 'ünal karaman', '24 october 2008'], ['antalyaspor', 'jozef jarabinský', 'sacked', '28 october 2008', 'mehmet özdilek', '28 october 2008'], ['hacettepe', 'osman özdemir', 'resigned', '2 november 2008', 'erdoğan arıca', '3 november 2008'], ['denizlispor', 'ali yalçın', 'resigned', '2 november 2008', 'ümit kayıhan', '10 november 2008'], ['gençlerbirliği', 'mesut bakkal', 'resigned', '3 november 2008', 'samet aybaba', '5 november 2008'], ['bursaspor', 'samet aybaba', 'resigned', '4 november 2008', 'güvenç kurtar', '4 november 2008'], ['ankaragücü', 'ünal karaman', 'resigned', '8 december 2008', 'hakan kutlu', '2 january 2009'], ['bursaspor', 'güvenç kurtar', 'resigned', '23 december 2008', 'ertuğrul sağlam', '2 january 2009'], ['kocaelispor', 'yılmaz vural', 'resigned', '29 december 2008', 'erhan altın', '17 january 2009'], ['denizlispor', 'ümit kayıhan', 'sacked', '5 february 2009', 'mesut bakkal', '6 february 2009'], ['galatasaray', 'michael skibbe', 'sacked', '23 february 2009', 'bülent korkmaz', '23 february 2009'], ['hacettepe', 'erdoğan arıca', 'resigned', '2 march 2009', 'ergün penbe', '2 march 2009'], ['gaziantepspor', 'nurullah sağlam', 'resigned', '9 march 2009', 'josé couceiro', '6 april 2009'], ['konyaspor', 'giray bulak', 'sacked', '19 may 2009', 'ünal karaman', '20 may 2009']] |
1964 oakland raiders season | https://en.wikipedia.org/wiki/1964_Oakland_Raiders_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12828281-1.html.csv | count | the oakland raiders won just 5 of their games in 1964 . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'w', 'result': '5', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'w'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to w .', 'tostr': 'filter_eq { all_rows ; result ; w }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; result ; w } }', 'tointer': 'select the rows whose result record fuzzily matches to w . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; result ; w } } ; 5 } = true', 'tointer': 'select the rows whose result record fuzzily matches to w . the number of such rows is 5 .'} | eq { count { filter_eq { all_rows ; result ; w } } ; 5 } = true | select the rows whose result record fuzzily matches to w . the number of such rows is 5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'result_5': 5, 'w_6': 6, '5_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'result_5': 'result', 'w_6': 'w', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'result_5': [0], 'w_6': [0], '5_7': [2]} | ['week', 'date', 'opponent', 'result', 'attendance'] | [['1', 'september 13 , 1964', 'boston patriots', 'l 17 - 14', '21126'], ['2', 'september 19 , 1964', 'houston oilers', 'l 42 - 28', '26482'], ['3', 'september 27 , 1964', 'kansas city chiefs', 'l 21 - 9', '18163'], ['4', 'october 3 , 1964', 'buffalo bills', 'l 23 - 20', '36451'], ['5', 'october 10 , 1964', 'new york jets', 'l 35 - 13', '36499'], ['6', 'october 16 , 1964', 'boston patriots', 't 43 - 43', '23279'], ['7', 'october 25 , 1964', 'denver broncos', 'w 40 - 7', '17858'], ['8', 'november 1 , 1964', 'san diego chargers', 'l 31 - 17', '25557'], ['9', 'november 8 , 1964', 'kansas city chiefs', 'l 42 - 7', '21023'], ['10', 'november 15 , 1964', 'houston oilers', 'w 20 - 10', '16375'], ['11', 'november 22 , 1964', 'new york jets', 'w 35 - 26', '15589'], ['12', 'november 29 , 1964', 'denver broncos', 't 20 - 20', '15958'], ['13', 'december 6 , 1964', 'buffalo bills', 'w 16 - 13', '18134'], ['14', 'december 20 , 1964', 'san diego chargers', 'w 21 - 20', '20124']] |
khym | https://en.wikipedia.org/wiki/KHYM | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14993391-1.html.csv | comparative | the khym radio channel with the call sign k297al operates on a higher frequency than the call sign k239ax . | {'row_1': '1', 'row_2': '4', 'col': '2', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'call sign', 'k297al'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose call sign record fuzzily matches to k297al .', 'tostr': 'filter_eq { all_rows ; call sign ; k297al }'}, 'frequency mhz'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; call sign ; k297al } ; frequency mhz }', 'tointer': 'select the rows whose call sign record fuzzily matches to k297al . take the frequency mhz record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'call sign', 'k239ax'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose call sign record fuzzily matches to k239ax .', 'tostr': 'filter_eq { all_rows ; call sign ; k239ax }'}, 'frequency mhz'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; call sign ; k239ax } ; frequency mhz }', 'tointer': 'select the rows whose call sign record fuzzily matches to k239ax . take the frequency mhz record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; call sign ; k297al } ; frequency mhz } ; hop { filter_eq { all_rows ; call sign ; k239ax } ; frequency mhz } } = true', 'tointer': 'select the rows whose call sign record fuzzily matches to k297al . take the frequency mhz record of this row . select the rows whose call sign record fuzzily matches to k239ax . take the frequency mhz record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; call sign ; k297al } ; frequency mhz } ; hop { filter_eq { all_rows ; call sign ; k239ax } ; frequency mhz } } = true | select the rows whose call sign record fuzzily matches to k297al . take the frequency mhz record of this row . select the rows whose call sign record fuzzily matches to k239ax . take the frequency mhz 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, 'call sign_7': 7, 'k297al_8': 8, 'frequency mhz_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'call sign_11': 11, 'k239ax_12': 12, 'frequency mhz_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', 'call sign_7': 'call sign', 'k297al_8': 'k297al', 'frequency mhz_9': 'frequency mhz', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'call sign_11': 'call sign', 'k239ax_12': 'k239ax', 'frequency mhz_13': 'frequency mhz'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'call sign_7': [0], 'k297al_8': [0], 'frequency mhz_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'call sign_11': [1], 'k239ax_12': [1], 'frequency mhz_13': [3]} | ['call sign', 'frequency mhz', 'city of license', 'erp w', 'class', 'fcc info'] | [['k297al', '107.3', 'dighton , kansas', '170', 'd', 'fcc'], ['k236 am', '95.1', 'elkhart , kansas', '170', 'd', 'fcc'], ['k207et', '89.3', 'healy , kansas', '75', 'd', 'fcc'], ['k239ax', '95.7', 'larned , kansas', '170', 'd', 'fcc'], ['k211ch', '90.5', 'leoti , kansas', '250', 'd', 'fcc'], ['k232dh', '94.3', 'ulysses , kansas', '170', 'd', 'fcc']] |
julian bailey | https://en.wikipedia.org/wiki/Julian_Bailey | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1235920-4.html.csv | count | julian bailey drove with the team mg sport & racing ltd for a total of two years . | {'scope': 'all', 'criterion': 'equal', 'value': 'mg sport & racing ltd', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'mg sport & racing ltd'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team record fuzzily matches to mg sport & racing ltd .', 'tostr': 'filter_eq { all_rows ; team ; mg sport & racing ltd }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; team ; mg sport & racing ltd } }', 'tointer': 'select the rows whose team record fuzzily matches to mg sport & racing ltd . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; team ; mg sport & racing ltd } } ; 2 } = true', 'tointer': 'select the rows whose team record fuzzily matches to mg sport & racing ltd . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; team ; mg sport & racing ltd } } ; 2 } = true | select the rows whose team record fuzzily matches to mg sport & racing ltd . 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, 'team_5': 5, 'mg sport & racing ltd_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', 'team_5': 'team', 'mg sport & racing ltd_6': 'mg sport & racing ltd', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'team_5': [0], 'mg sport & racing ltd_6': [0], '2_7': [2]} | ['year', 'class', 'tyres', 'team', 'co - drivers', 'laps', 'pos'] | [['1989', 'c1', 'd', 'nissan motorsports', 'mark blundell martin donnelly', '5', 'dnf'], ['1990', 'c1', 'd', 'nissan motorsports international', 'mark blundell gianfranco brancatelli', '142', 'dnf'], ['1997', 'gt1', 'd', 'newcastle united lister', 'thomas erdos mark skaife', '77', 'dnf'], ['2001', 'lmp675', 'm', 'mg sport & racing ltd', 'mark blundell kevin mcgarrity', '92', 'dnf'], ['2002', 'lmp675', 'm', 'mg sport & racing ltd', 'mark blundell kevin mcgarrity', '219', 'dnf']] |
2008 - 09 in scottish football | https://en.wikipedia.org/wiki/2008%E2%80%9309_in_Scottish_football | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17327458-19.html.csv | majority | the majority of scottish football games are recorded by the bbc . | {'scope': 'all', 'col': '5', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'bbc', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'report', 'bbc'], 'result': True, 'ind': 0, 'tointer': 'for the report records of all rows , all of them fuzzily match to bbc .', 'tostr': 'all_eq { all_rows ; report ; bbc } = true'} | all_eq { all_rows ; report ; bbc } = true | for the report records of all rows , all of them fuzzily match to bbc . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'report_3': 3, 'bbc_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'report_3': 'report', 'bbc_4': 'bbc'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'report_3': [0], 'bbc_4': [0]} | ['date', 'venue', 'score', 'competition', 'report'] | [['20 august', 'hampden park , glasgow ( h )', '0 - 0', 'friendly', 'bbc sport'], ['6 september', 'skopje city stadium , skopje ( a )', '0 - 1', 'wcq ( 9 )', 'bbc sport'], ['10 september', 'laugardalsvöllur , reykjavík ( a )', '2 - 1', 'wcq ( 9 )', 'bbc sport'], ['11 october', 'hampden park , glasgow ( h )', '0 - 0', 'wcq ( 9 )', 'bbc sport'], ['20 november', 'hampden park , glasgow ( h )', '0 - 1', 'friendly', 'bbc sport'], ['28 march', 'amsterdam arena , amsterdam ( a )', '0 - 3', 'wcq ( 9 )', 'bbc sport'], ['1 april', 'hampden park , glasgow ( h )', '2 - 1', 'wcq ( 9 )', 'bbc sport']] |
list of olympic medalists in athletics ( men ) | https://en.wikipedia.org/wiki/List_of_Olympic_medalists_in_athletics_%28men%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-22355-26.html.csv | majority | most of the men 's olympic medalists did not win any bronze medals . | {'scope': 'all', 'col': '7', 'most_or_all': 'most', 'criterion': 'equal', 'value': '0', 'subset': None} | {'func': 'most_eq', 'args': ['all_rows', 'bronze', '0'], 'result': True, 'ind': 0, 'tointer': 'for the bronze records of all rows , most of them are equal to 0 .', 'tostr': 'most_eq { all_rows ; bronze ; 0 } = true'} | most_eq { all_rows ; bronze ; 0 } = true | for the bronze records of all rows , most of them are equal to 0 . | 1 | 1 | {'most_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'bronze_3': 3, '0_4': 4} | {'most_eq_0': 'most_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'bronze_3': 'bronze', '0_4': '0'} | {'most_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'bronze_3': [0], '0_4': [0]} | ['rank', 'athlete', 'nation', 'olympics', 'gold', 'silver', 'bronze', 'total ( min 2 medals )'] | [['1', 'lee calhoun', 'united states ( usa )', '1952 - 1956', '2', '0', '0', '2'], ['1', 'roger kingdom', 'united states ( usa )', '1984 - 1988', '2', '0', '0', '2'], ['3', 'sydney atkinson', 'south africa ( rsa )', '1924 - 1928', '1', '1', '0', '2'], ['3', 'guy drut', 'france ( fra )', '1972 - 1976', '1', '1', '0', '2'], ['5', 'hayes jones', 'united states ( usa )', '1960 - 1964', '1', '0', '1', '2'], ['5', 'willie davenport', 'united states ( usa )', '1968 - 1976', '1', '0', '1', '2'], ['5', 'anier garcia', 'cuba ( cub )', '2000 - 2004', '1', '0', '1', '2'], ['8', 'jack davis', 'united states ( usa )', '1952 - 1956', '0', '2', '0', '2'], ['8', 'alejandro casanas', 'cuba ( cub )', '1976 - 1980', '0', '2', '0', '2'], ['8', 'terrence trammell', 'united states ( usa )', '2000 - 2004', '0', '2', '0', '2'], ['11', 'don finlay', 'great britain ( gbr )', '1932 - 1936', '0', '1', '1', '2']] |
list of whose line is it anyway ? uk episodes | https://en.wikipedia.org/wiki/List_of_Whose_Line_Is_It_Anyway%3F_UK_episodes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14934885-7.html.csv | count | tony slattery was performer 4 on whose line is it anyway ? uk a total of seven times . | {'scope': 'all', 'criterion': 'equal', 'value': 'tony slattery', 'result': '7', 'col': '6', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'performer 4', 'tony slattery'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose performer 4 record fuzzily matches to tony slattery .', 'tostr': 'filter_eq { all_rows ; performer 4 ; tony slattery }'}], 'result': '7', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; performer 4 ; tony slattery } }', 'tointer': 'select the rows whose performer 4 record fuzzily matches to tony slattery . the number of such rows is 7 .'}, '7'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; performer 4 ; tony slattery } } ; 7 } = true', 'tointer': 'select the rows whose performer 4 record fuzzily matches to tony slattery . the number of such rows is 7 .'} | eq { count { filter_eq { all_rows ; performer 4 ; tony slattery } } ; 7 } = true | select the rows whose performer 4 record fuzzily matches to tony slattery . the number of such rows is 7 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'performer 4_5': 5, 'tony slattery_6': 6, '7_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'performer 4_5': 'performer 4', 'tony slattery_6': 'tony slattery', '7_7': '7'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'performer 4_5': [0], 'tony slattery_6': [0], '7_7': [2]} | ['date', 'episode', 'performer 1', 'performer 2', 'performer 3', 'performer 4'] | [['1 july 1994', '1', 'stephen frost', 'colin mochrie', 'ryan stiles', 'tony slattery'], ['8 july 1994', '2', 'josie lawrence', 'ryan stiles', 'greg proops', 'mike mcshane'], ['15 july 1994', '3', 'stephen frost', 'colin mochrie', 'ryan stiles', 'tony slattery'], ['22 july 1994', '4', 'mike mcshane', 'greg proops', 'ryan stiles', 'tony slattery'], ['29 july 1994', '5', 'josie lawrence', 'stephen frost', 'ryan stiles', 'tony slattery'], ['5 august 1994', '6', 'stephen frost', 'colin mochrie', 'ryan stiles', 'tony slattery'], ['12 august 1994', '7', 'josie lawrence', 'rory bremner', 'tony slattery', 'mike mcshane'], ['19 august 1994', '8', 'greg proops', 'chip esten', 'ryan stiles', 'tony slattery'], ['26 august 1994', '9', 'greg proops', 'colin mochrie', 'ryan stiles', 'tony slattery'], ['2 september 1994', '10', 'compilation 1', 'compilation 1', 'compilation 1', 'compilation 1'], ['9 september 1994', '11', 'compilation 2', 'compilation 2', 'compilation 2', 'compilation 2']] |
2009 nrl season | https://en.wikipedia.org/wiki/2009_NRL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17678435-10.html.csv | majority | the majority of the games featured a losing team that scored more than 10 points . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '10', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'score', '10'], 'result': True, 'ind': 0, 'tointer': 'for the score records of all rows , most of them are greater than 10 .', 'tostr': 'most_greater { all_rows ; score ; 10 } = true'} | most_greater { all_rows ; score ; 10 } = true | for the score records of all rows , most of them are greater than 10 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'score_3': 3, '10_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'score_3': 'score', '10_4': '10'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'score_3': [0], '10_4': [0]} | ['team', 'opponent', 'score', 'venue', 'round'] | [['brisbane broncos', 'penrith panthers', '58 - 24', 'suncorp stadium', 'round 23'], ['wests tigers', 'cronulla sharks', '56 - 10', 'toyota stadium', 'round 23'], ['canberra raiders', 'brisbane broncos', '56 - 0', 'canberra stadium', 'round 21'], ['wests tigers', 'south sydney rabbitohs', '54 - 20', 'anz stadium', 'round 17'], ['south sydney rabbitohs', 'sydney roosters', '52 - 12', 'sydney football stadium', 'round 1']] |
united states house of representatives elections , 1972 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1972 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1341707-15.html.csv | unique | roman c pucinski was the only incumbent who decided to retire their house seat to run for the us senate . | {'scope': 'all', 'row': '5', 'col': '5', 'col_other': '2', 'criterion': 'fuzzily_match', 'value': 'retired to run for us senate', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'retired to run for us senate'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to retired to run for us senate .', 'tostr': 'filter_eq { all_rows ; result ; retired to run for us senate }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; result ; retired to run for us senate } }', 'tointer': 'select the rows whose result record fuzzily matches to retired to run for us senate . 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 to run for us senate'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to retired to run for us senate .', 'tostr': 'filter_eq { all_rows ; result ; retired to run for us senate }'}, 'incumbent'], 'result': 'roman c pucinski', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; result ; retired to run for us senate } ; incumbent }'}, 'roman c pucinski'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; result ; retired to run for us senate } ; incumbent } ; roman c pucinski }', 'tointer': 'the incumbent record of this unqiue row is roman c pucinski .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; result ; retired to run for us senate } } ; eq { hop { filter_eq { all_rows ; result ; retired to run for us senate } ; incumbent } ; roman c pucinski } } = true', 'tointer': 'select the rows whose result record fuzzily matches to retired to run for us senate . there is only one such row in the table . the incumbent record of this unqiue row is roman c pucinski .'} | and { only { filter_eq { all_rows ; result ; retired to run for us senate } } ; eq { hop { filter_eq { all_rows ; result ; retired to run for us senate } ; incumbent } ; roman c pucinski } } = true | select the rows whose result record fuzzily matches to retired to run for us senate . there is only one such row in the table . the incumbent record of this unqiue row is roman c pucinski . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'result_7': 7, 'retired to run for us senate_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'incumbent_9': 9, 'roman c pucinski_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 to run for us senate_8': 'retired to run for us senate', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'incumbent_9': 'incumbent', 'roman c pucinski_10': 'roman c pucinski'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'result_7': [0], 'retired to run for us senate_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'incumbent_9': [2], 'roman c pucinski_10': [3]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['illinois 1', 'ralph h metcalfe', 'democratic', '1970', 're - elected', 'ralph h metcalfe ( d ) 91.4 % louis coggs ( r ) 8.6 %'], ['illinois 4', 'ed derwinski', 'republican', '1958', 're - elected', "ed derwinski ( r ) 70.5 % c f ' bob ' dore ( d ) 29.5 %"], ['illinois 10', 'abner j mikva redistricted from the 2nd district', 'democratic', '1968', 'lost re - election republican gain', 'samuel h young ( r ) 51.6 % abner j mikva ( d ) 48.4 %'], ['illinois 11', 'frank annunzio redistricted from the 7th district', 'democratic', '1964', 're - elected', 'frank annunzio ( d ) 53.3 % john j hoellen ( r ) 46.7 %'], ['illinois 11', 'roman c pucinski', 'democratic', '1958', 'retired to run for us senate democratic loss', 'frank annunzio ( d ) 53.3 % john j hoellen ( r ) 46.7 %'], ['illinois 12', 'phil crane redistricted from the 13th district', 'republican', '1969', 're - elected', 'phil crane ( r ) 74.2 % edwin l frank ( d ) 25.8 %'], ['illinois 15', 'cliffard d carlson', 'republican', 'april 4 , 1972 ( special )', 'retired republican loss', 'leslie c arends ( r ) 57.2 % tim l hall ( d ) 42.8 %'], ['illinois 19', 'tom railsback', 'republican', '1966', 're - elected', 'tom railsback ( r ) unopposed'], ['illinois 20', 'paul findley', 'republican', '1960', 're - elected', "paul findley ( r ) 68.8 % robert s o ' shea ( d ) 31.2 %"]] |
1995 pga tour | https://en.wikipedia.org/wiki/1995_PGA_Tour | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14611590-4.html.csv | comparative | of the players listed as winners on the 1995 pga tour nick price had more wins than fred couples . | {'row_1': '4', 'row_2': '5', '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', 'player', 'nick price'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to nick price .', 'tostr': 'filter_eq { all_rows ; player ; nick price }'}, 'wins'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; nick price } ; wins }', 'tointer': 'select the rows whose player record fuzzily matches to nick price . take the wins record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'fred couples'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to fred couples .', 'tostr': 'filter_eq { all_rows ; player ; fred couples }'}, 'wins'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; fred couples } ; wins }', 'tointer': 'select the rows whose player record fuzzily matches to fred couples . take the wins record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; player ; nick price } ; wins } ; hop { filter_eq { all_rows ; player ; fred couples } ; wins } } = true', 'tointer': 'select the rows whose player record fuzzily matches to nick price . take the wins record of this row . select the rows whose player record fuzzily matches to fred couples . take the wins record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; player ; nick price } ; wins } ; hop { filter_eq { all_rows ; player ; fred couples } ; wins } } = true | select the rows whose player record fuzzily matches to nick price . take the wins record of this row . select the rows whose player record fuzzily matches to fred couples . take the wins 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, 'nick price_8': 8, 'wins_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'fred couples_12': 12, 'wins_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', 'nick price_8': 'nick price', 'wins_9': 'wins', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'fred couples_12': 'fred couples', 'wins_13': 'wins'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'nick price_8': [0], 'wins_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'fred couples_12': [1], 'wins_13': [3]} | ['rank', 'player', 'country', 'earnings', 'wins'] | [['1', 'greg norman', 'australia', '9592829', '17'], ['2', 'tom kite', 'united states', '9337998', '19'], ['3', 'payne stewart', 'united states', '7389479', '9'], ['4', 'nick price', 'zimbabwe', '7338119', '15'], ['5', 'fred couples', 'united states', '7188408', '11']] |
germany | https://en.wikipedia.org/wiki/Germany | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11867-3.html.csv | comparative | the revenue of metro ag is lower than the revenue of daimler ag . | {'row_1': '7', 'row_2': '3', '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', 'name', 'metro ag'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to metro ag .', 'tostr': 'filter_eq { all_rows ; name ; metro ag }'}, 'revenue ( mil )'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; metro ag } ; revenue ( mil ) }', 'tointer': 'select the rows whose name record fuzzily matches to metro ag . take the revenue ( mil ) record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'daimler ag'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to daimler ag .', 'tostr': 'filter_eq { all_rows ; name ; daimler ag }'}, 'revenue ( mil )'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; daimler ag } ; revenue ( mil ) }', 'tointer': 'select the rows whose name record fuzzily matches to daimler ag . take the revenue ( mil ) record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; name ; metro ag } ; revenue ( mil ) } ; hop { filter_eq { all_rows ; name ; daimler ag } ; revenue ( mil ) } } = true', 'tointer': 'select the rows whose name record fuzzily matches to metro ag . take the revenue ( mil ) record of this row . select the rows whose name record fuzzily matches to daimler ag . take the revenue ( mil ) record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; name ; metro ag } ; revenue ( mil ) } ; hop { filter_eq { all_rows ; name ; daimler ag } ; revenue ( mil ) } } = true | select the rows whose name record fuzzily matches to metro ag . take the revenue ( mil ) record of this row . select the rows whose name record fuzzily matches to daimler ag . take the revenue ( mil ) 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, 'name_7': 7, 'metro ag_8': 8, 'revenue (mil)_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'name_11': 11, 'daimler ag_12': 12, 'revenue (mil)_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', 'name_7': 'name', 'metro ag_8': 'metro ag', 'revenue (mil)_9': 'revenue ( mil )', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'name_11': 'name', 'daimler ag_12': 'daimler ag', 'revenue (mil)_13': 'revenue ( mil )'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'name_7': [0], 'metro ag_8': [0], 'revenue (mil)_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'name_11': [1], 'daimler ag_12': [1], 'revenue (mil)_13': [3]} | ['rank', 'name', 'headquarters', 'revenue ( mil )', 'profit ( mil )', 'employees ( world )'] | [['0 1', 'volkswagen ag', 'wolfsburg', '159.000', '15.800', '502 ,000'], ['0 2', 'eon se', 'düsseldorf', '113.000', '1.900', '79 ,000'], ['0 3', 'daimler ag', 'stuttgart', '107.000', '6.000', '271 ,000'], ['0 4', 'siemens ag', 'berlin , münchen', '74.000', '6.300', '360 ,000'], ['0 5', 'basf se', 'ludwigshafen am rhein', '73.000', '6.600', '111 ,000'], ['0 6', 'bmw ag', 'münchen', '69.000', '4.900', '100 ,000'], ['0 7', 'metro ag', 'düsseldorf', '67.000', '740', '288 ,000'], ['0 8', 'schwarz - gruppe ( lidl / kaufland )', 'neckarsulm', '63.000', 'n / a', '315 ,000'], ['0 9', 'deutsche telekom ag', 'bonn', '59.000', '670', '235 ,000'], ['0 10', 'deutsche post ag', 'bonn', '53.000', '1.300', '471 ,000'], ['-', 'allianz se', 'münchen', '104.000', '2.800', '141 ,000'], ['-', 'deutsche bank ag', 'frankfurt am main', '2.160.000', '4.300', '101 ,000']] |
woden valley | https://en.wikipedia.org/wiki/Woden_Valley | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1174162-1.html.csv | unique | the only place in woden valley that had less than one thousand inhabitants was o ' malley . | {'scope': 'all', 'row': '9', 'col': '2', 'col_other': '1', 'criterion': 'less_than', 'value': '1000', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'population ( in 2008 )', '1000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose population ( in 2008 ) record is less than 1000 .', 'tostr': 'filter_less { all_rows ; population ( in 2008 ) ; 1000 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_less { all_rows ; population ( in 2008 ) ; 1000 } }', 'tointer': 'select the rows whose population ( in 2008 ) record is less than 1000 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_less', 'args': ['all_rows', 'population ( in 2008 )', '1000'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose population ( in 2008 ) record is less than 1000 .', 'tostr': 'filter_less { all_rows ; population ( in 2008 ) ; 1000 }'}, 'suburb'], 'result': "o'malley", 'ind': 2, 'tostr': 'hop { filter_less { all_rows ; population ( in 2008 ) ; 1000 } ; suburb }'}, "o'malley"], 'result': True, 'ind': 3, 'tostr': "eq { hop { filter_less { all_rows ; population ( in 2008 ) ; 1000 } ; suburb } ; o'malley }", 'tointer': "the suburb record of this unqiue row is o'malley ."}], 'result': True, 'ind': 4, 'tostr': "and { only { filter_less { all_rows ; population ( in 2008 ) ; 1000 } } ; eq { hop { filter_less { all_rows ; population ( in 2008 ) ; 1000 } ; suburb } ; o'malley } } = true", 'tointer': "select the rows whose population ( in 2008 ) record is less than 1000 . there is only one such row in the table . the suburb record of this unqiue row is o'malley ."} | and { only { filter_less { all_rows ; population ( in 2008 ) ; 1000 } } ; eq { hop { filter_less { all_rows ; population ( in 2008 ) ; 1000 } ; suburb } ; o'malley } } = true | select the rows whose population ( in 2008 ) record is less than 1000 . there is only one such row in the table . the suburb record of this unqiue row is o'malley . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_less_0': 0, 'all_rows_6': 6, 'population (in 2008)_7': 7, '1000_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'suburb_9': 9, "o'malley_10": 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_less_0': 'filter_less', 'all_rows_6': 'all_rows', 'population (in 2008)_7': 'population ( in 2008 )', '1000_8': '1000', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'suburb_9': 'suburb', "o'malley_10": "o'malley"} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_less_0': [1, 2], 'all_rows_6': [0], 'population (in 2008)_7': [0], '1000_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'suburb_9': [2], "o'malley_10": [3]} | ['suburb', 'population ( in 2008 )', 'median age ( in 2006 )', 'mean household size ( in 2006 )', 'area ( km square )', 'density ( / km square )', 'date first settled as a suburb', 'gazetted as a division name'] | [['chifley', '2325', '36 years', '2.3 persons', '1.6', '1453', '1966', '12 may 1966'], ['curtin', '5133', '41 years', '2.5 persons', '4.8', '1069', '1962', '20 september 1962'], ['farrer', '3360', '41 years', '2.7 persons', '2.1', '1600', '1967', '12 may 1966'], ['garran', '3175', '39 years', '2.5 persons', '2.7', '1175', '1966', '12 may 1966'], ['hughes', '2898', '41 years', '2.5 persons', '1.8', '1610', '1963', '20 september 1962'], ['isaacs', '2424', '45 years', '2.6 persons', '3.1', '781', '1986', '12 may 1966'], ['lyons', '2444', '38 years', '2.1 persons', '2.3', '1062', '1965', '20 september 1962'], ['mawson', '2861', '40 years', '2.2 persons', '2.1', '1362', '1967', '12 may 1966'], ["o'malley", '684', '47 years', '3.1 persons', '2.6', '263', '1973', '12 may 1966'], ['pearce', '2509', '41 years', '2.3 persons', '1.7', '1475', '1967', '12 may 1966'], ['phillip', '1910', '32 years', '1.7 persons', '2.6', '734', '1966', '12 may 1966']] |
1976 - 77 segunda división | https://en.wikipedia.org/wiki/1976%E2%80%9377_Segunda_Divisi%C3%B3n | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12239755-2.html.csv | comparative | sporting de gijon got more points than cadiz cf in the 1976 - 77 segunda división . | {'row_1': '1', 'row_2': '2', 'col': '4', 'col_other': '2', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'club', 'sporting de gijón'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose club record fuzzily matches to sporting de gijón .', 'tostr': 'filter_eq { all_rows ; club ; sporting de gijón }'}, 'points'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; club ; sporting de gijón } ; points }', 'tointer': 'select the rows whose club record fuzzily matches to sporting de gijón . take the points record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'club', 'cádiz cf'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose club record fuzzily matches to cádiz cf .', 'tostr': 'filter_eq { all_rows ; club ; cádiz cf }'}, 'points'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; club ; cádiz cf } ; points }', 'tointer': 'select the rows whose club record fuzzily matches to cádiz cf . take the points record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; club ; sporting de gijón } ; points } ; hop { filter_eq { all_rows ; club ; cádiz cf } ; points } } = true', 'tointer': 'select the rows whose club record fuzzily matches to sporting de gijón . take the points record of this row . select the rows whose club record fuzzily matches to cádiz cf . take the points record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; club ; sporting de gijón } ; points } ; hop { filter_eq { all_rows ; club ; cádiz cf } ; points } } = true | select the rows whose club record fuzzily matches to sporting de gijón . take the points record of this row . select the rows whose club record fuzzily matches to cádiz cf . take the points 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, 'club_7': 7, 'sporting de gijón_8': 8, 'points_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'club_11': 11, 'cádiz cf_12': 12, 'points_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', 'club_7': 'club', 'sporting de gijón_8': 'sporting de gijón', 'points_9': 'points', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'club_11': 'club', 'cádiz cf_12': 'cádiz cf', 'points_13': 'points'} | {'greater_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'club_7': [0], 'sporting de gijón_8': [0], 'points_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'club_11': [1], 'cádiz cf_12': [1], 'points_13': [3]} | ['position', 'club', 'played', 'points', 'wins', 'draws', 'losses', 'goals for', 'goals against', 'goal difference'] | [['1', 'sporting de gijón', '38', '47 + 9', '18', '11', '9', '62', '35', '+ 27'], ['2', 'cádiz cf', '38', '46 + 8', '17', '12', '9', '60', '42', '+ 18'], ['3', 'rayo vallecano', '38', '45 + 7', '17', '11', '10', '46', '34', '+ 12'], ['4', 'real jaén', '38', '43 + 5', '15', '13', '10', '42', '32', '+ 10'], ['5', 'real oviedo', '38', '43 + 5', '18', '7', '13', '48', '43', '+ 5'], ['6', 'cd tenerife', '38', '40 + 2', '15', '10', '13', '48', '48', '0'], ['7', 'terrassa fc', '38', '40 + 2', '13', '14', '11', '44', '34', '+ 10'], ['8', 'deportivo alavés', '38', '40 + 2', '14', '12', '12', '57', '42', '+ 15'], ['9', 'recreativo de huelva', '38', '38', '14', '10', '14', '42', '50', '- 8'], ['10', 'granada cf', '38', '36 - 2', '14', '8', '16', '42', '39', '+ 3'], ['11', 'deportivo de la coruña', '38', '36 - 2', '11', '14', '13', '40', '50', '- 10'], ['12', 'real valladolid', '38', '36 - 2', '14', '8', '16', '57', '56', '+ 1'], ['13', 'getafe deportivo', '38', '35 - 3', '12', '11', '15', '37', '48', '- 11'], ['14', 'cd castellón', '38', '35 - 3', '14', '7', '17', '46', '45', '+ 1'], ['15', 'córdoba cf', '38', '35 - 3', '10', '15', '13', '39', '45', '- 6'], ['16', 'cf calvo sotelo', '38', '34 - 4', '14', '6', '18', '39', '56', '- 17'], ['17', 'pontevedra cf', '38', '34 - 4', '10', '14', '14', '34', '44', '- 10'], ['18', 'levante ud', '38', '34 - 4', '12', '10', '16', '47', '61', '- 14'], ['19', 'ue sant andreu', '38', '33 - 5', '10', '13', '15', '39', '52', '- 13'], ['20', 'barcelona atlètic', '38', '30 - 8', '10', '10', '18', '39', '52', '- 13']] |
2009 - 10 fis ski jumping world cup | https://en.wikipedia.org/wiki/2009%E2%80%9310_FIS_Ski_Jumping_World_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-24489942-10.html.csv | count | there are three jumpers that are from austria . | {'scope': 'all', 'criterion': 'equal', 'value': 'austria', 'result': '3', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'austria'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to austria .', 'tostr': 'filter_eq { all_rows ; nationality ; austria }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; nationality ; austria } }', 'tointer': 'select the rows whose nationality record fuzzily matches to austria . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; nationality ; austria } } ; 3 } = true', 'tointer': 'select the rows whose nationality record fuzzily matches to austria . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; nationality ; austria } } ; 3 } = true | select the rows whose nationality record fuzzily matches to austria . 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, 'nationality_5': 5, 'austria_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', 'nationality_5': 'nationality', 'austria_6': 'austria', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'nationality_5': [0], 'austria_6': [0], '3_7': [2]} | ['rank', 'name', 'nationality', '1st ( m )', '2nd ( m )', 'points', 'overall fht points', 'overall wc points ( rank )'] | [['1', 'thomas morgenstern', 'austria', '133.0', '136.0', '264.7', '987.1 ( 6 )', '440 ( 4 )'], ['2', 'janne ahonen', 'finland', '134.0', '133.5', '264.0', '1013.9 ( 2 )', '350 ( 7 )'], ['3', 'simon ammann', 'switzerland', '136.0', '131.5', '261.5', '1008.3 ( 5 )', '669 ( 1 )'], ['4', 'wolfgang loitzl', 'austria', '130.5', '135.0', '260.9', '1011.6 ( 3 )', '411 ( 5 )'], ['5', 'andreas kofler', 'austria', '129.0', '133.5', '255.0', '1027.2 ( 1 )', '521 ( 3 )']] |
1997 - 98 toronto raptors season | https://en.wikipedia.org/wiki/1997%E2%80%9398_Toronto_Raptors_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-13619053-9.html.csv | aggregation | during april of the 1997 - 98 toronto raptors season , toronto scored an average of almost 100 points per game . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '100', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'score'], 'result': '100', 'ind': 0, 'tostr': 'avg { all_rows ; score }'}, '100'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; score } ; 100 } = true', 'tointer': 'the average of the score record of all rows is 100 .'} | round_eq { avg { all_rows ; score } ; 100 } = true | the average of the score record of all rows is 100 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'score_4': 4, '100_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'score_4': 'score', '100_5': '100'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'score_4': [0], '100_5': [1]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['72', 'april 1', 'atlanta', 'l 91 - 105 ( ot )', 'doug christie , gary trent ( 14 )', 'marcus camby , tracy mcgrady ( 9 )', 'doug christie ( 3 )', 'georgia dome 10441', '15 - 57'], ['73', 'april 3', 'washington', 'l 112 - 120 ( ot )', 'dee brown ( 30 )', 'gary trent ( 10 )', 'dee brown ( 6 )', 'mci center 18324', '15 - 58'], ['74', 'april 5', 'philadelphia', 'l 104 - 116 ( ot )', 'gary trent ( 25 )', 'tracy mcgrady ( 13 )', 'dee brown ( 6 )', 'corestates center 15808', '15 - 59'], ['75', 'april 7', 'milwaukee', 'l 105 - 114 ( ot )', 'doug christie ( 20 )', 'reggie slater ( 8 )', 'doug christie ( 5 )', 'bradley center 13288', '15 - 60'], ['76', 'april 8', 'milwaukee', 'l 100 - 107 ( ot )', 'gary trent ( 24 )', 'chauncey billups , tracy mcgrady ( 9 )', 'doug christie ( 8 )', 'skydome 14168', '15 - 61'], ['77', 'april 10', 'miami', 'l 105 - 111 ( ot )', 'doug christie ( 26 )', 'tracy mcgrady ( 15 )', 'doug christie ( 7 )', 'skydome 16111', '15 - 62'], ['78', 'april 12', 'new jersey', 'l 109 - 116 ( ot )', 'dee brown ( 30 )', 'marcus camby ( 11 )', 'tracy mcgrady ( 6 )', 'skydome 14088', '15 - 63'], ['79', 'april 14', 'new jersey', 'w 96 - 92 ( ot )', 'doug christie ( 23 )', 'marcus camby ( 12 )', 'chauncey billups , doug christie ( 4 )', 'continental airlines arena 17521', '16 - 63'], ['80', 'april 16', 'new york', 'l 79 - 108 ( ot )', 'doug christie ( 14 )', 'doug christie , tracy mcgrady ( 7 )', 'dee brown ( 4 )', 'madison square garden 19763', '16 - 64'], ['81', 'april 17', 'indiana', 'l 98 - 107 ( ot )', 'doug christie ( 24 )', 'gary trent ( 12 )', 'chauncey billups ( 5 )', 'market square arena 16059', '16 - 65']] |
television in thailand | https://en.wikipedia.org/wiki/Television_in_Thailand | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18987481-3.html.csv | comparative | modernine tv had a higher market share of television in thailand than nbt in 2005 . | {'row_1': '4', 'row_2': '5', 'col': '2', 'col_other': '1', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tv station ( operator )', 'modernine tv'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose tv station ( operator ) record fuzzily matches to modernine tv .', 'tostr': 'filter_eq { all_rows ; tv station ( operator ) ; modernine tv }'}, '2005'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; tv station ( operator ) ; modernine tv } ; 2005 }', 'tointer': 'select the rows whose tv station ( operator ) record fuzzily matches to modernine tv . take the 2005 record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'tv station ( operator )', 'nbt'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose tv station ( operator ) record fuzzily matches to nbt .', 'tostr': 'filter_eq { all_rows ; tv station ( operator ) ; nbt }'}, '2005'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; tv station ( operator ) ; nbt } ; 2005 }', 'tointer': 'select the rows whose tv station ( operator ) record fuzzily matches to nbt . take the 2005 record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; tv station ( operator ) ; modernine tv } ; 2005 } ; hop { filter_eq { all_rows ; tv station ( operator ) ; nbt } ; 2005 } } = true', 'tointer': 'select the rows whose tv station ( operator ) record fuzzily matches to modernine tv . take the 2005 record of this row . select the rows whose tv station ( operator ) record fuzzily matches to nbt . take the 2005 record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; tv station ( operator ) ; modernine tv } ; 2005 } ; hop { filter_eq { all_rows ; tv station ( operator ) ; nbt } ; 2005 } } = true | select the rows whose tv station ( operator ) record fuzzily matches to modernine tv . take the 2005 record of this row . select the rows whose tv station ( operator ) record fuzzily matches to nbt . take the 2005 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, 'tv station (operator)_7': 7, 'modernine tv_8': 8, '2005_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'tv station (operator)_11': 11, 'nbt_12': 12, '2005_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', 'tv station (operator)_7': 'tv station ( operator )', 'modernine tv_8': 'modernine tv', '2005_9': '2005', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'tv station (operator)_11': 'tv station ( operator )', 'nbt_12': 'nbt', '2005_13': '2005'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'tv station (operator)_7': [0], 'modernine tv_8': [0], '2005_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'tv station (operator)_11': [1], 'nbt_12': [1], '2005_13': [3]} | ['tv station ( operator )', '2005', '2006', '2007', '2008', '2009', '2010', '2011 1h'] | [['bbtv ch7', '42.4', '41.3', '42.0', '44.7', '45.4', '43.8', '47.5'], ['tv3', '24.5', '25.6', '29.5', '26.8', '27.7', '29.5', '29.0'], ['tv5', '8.1', '7.3', '6.7', '7.6', '8.6', '8.0', '6.9'], ['modernine tv', '10.3', '10.2', '9.2', '9.6', '9.9', '9.7', '9.2'], ['nbt', '2.9', '3.0', '2.4', '4.9', '3.4', '3.4', '2.4'], ['thai pbs', '11.8', '12.6', '10.2', '6.1', '4.9', '5.6', '5.0']] |
list of intel atom microprocessors | https://en.wikipedia.org/wiki/List_of_Intel_Atom_microprocessors | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16729930-17.html.csv | majority | all of the atom microprocessors in this list were released on september 14 , 2010 . | {'scope': 'all', 'col': '10', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'september 14 , 2010', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'release date', 'september 14 , 2010'], 'result': True, 'ind': 0, 'tointer': 'for the release date records of all rows , all of them fuzzily match to september 14 , 2010 .', 'tostr': 'all_eq { all_rows ; release date ; september 14 , 2010 } = true'} | all_eq { all_rows ; release date ; september 14 , 2010 } = true | for the release date records of all rows , all of them fuzzily match to september 14 , 2010 . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'release date_3': 3, 'september 14, 2010_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'release date_3': 'release date', 'september 14, 2010_4': 'september 14 , 2010'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'release date_3': [0], 'september 14, 2010_4': [0]} | ['model number', 'sspec number', 'frequency', 'gpu frequency', 'l2 cache', 'i / o bus', 'memory', 'voltage', 'socket', 'release date', 'part number ( s )', 'release price ( usd )'] | [['atom e620', 'slh56 ( b0 ) slj32 ( b1 )', '600 mhz', '320 mhz', '512 kb', 'pcie', '1 ddr2 - 800', '0.8 - 1.175 v', 'fc - bga 676', 'september 14 , 2010', 'ct80618005844aa', '19'], ['atom e620t', 'slh5n ( b0 ) slj36 ( b1 )', '600 mhz', '320 mhz', '512 kb', 'pcie', '1 ddr2 - 800', '0.8 - 1.175 v', 'fc - bga 676', 'september 14 , 2010', 'ct80618005844ab', '22'], ['atom e640', 'slh55 ( b0 ) slj33 ( b1 )', '1 ghz', '320 mhz', '512 kb', 'pcie', '1 ddr2 - 800', '0.8 - 1.175 v', 'fc - bga 676', 'september 14 , 2010', 'ct80618005841aa', '29'], ['atom e640t', 'slh5 m ( b0 ) slj37 ( b1 )', '1 ghz', '320 mhz', '512 kb', 'pcie', '1 ddr2 - 800', '0.8 - 1.175 v', 'fc - bga 676', 'september 14 , 2010', 'ct80618005841ab', '37'], ['atom e660', 'slh54 ( b0 ) slj34 ( b1 )', '1.3 ghz', '400 mhz', '512 kb', 'pcie', '1 ddr2 - 800', '0.8 - 1.175 v', 'fc - bga 676', 'september 14 , 2010', 'ct80618003201aa', '54'], ['atom e660t', 'slh5l ( b0 ) slj38 ( b1 )', '1.3 ghz', '400 mhz', '512 kb', 'pcie', '1 ddr2 - 800', '0.8 - 1.175 v', 'fc - bga 676', 'september 14 , 2010', 'ct80618003201ab', '64'], ['atom e680', 'slh94 ( b0 ) slj35 ( b1 )', '1.6 ghz', '400 mhz', '512 kb', 'pcie', '1 ddr2 - 800', '0.8 - 1.175 v', 'fc - bga 676', 'september 14 , 2010', 'ct80618007035aa', '74'], ['atom e680t', 'slh95 ( b0 ) slj39 ( b1 )', '1.6 ghz', '400 mhz', '512 kb', 'pcie', '1 ddr2 - 800', '0.8 - 1.175 v', 'fc - bga 676', 'september 14 , 2010', 'ct80618007035ab', '85']] |
1965 vfl season | https://en.wikipedia.org/wiki/1965_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10788451-13.html.csv | aggregation | the average crowd attendance of games in the 1965 vfl season was 20301 . | {'scope': 'all', 'col': '6', 'type': 'average', 'result': '20301', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'crowd'], 'result': '20301', 'ind': 0, 'tostr': 'avg { all_rows ; crowd }'}, '20301'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; crowd } ; 20301 } = true', 'tointer': 'the average of the crowd record of all rows is 20301 .'} | round_eq { avg { all_rows ; crowd } ; 20301 } = true | the average of the crowd record of all rows is 20301 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'crowd_4': 4, '20301_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'crowd_4': 'crowd', '20301_5': '20301'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'crowd_4': [0], '20301_5': [1]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['st kilda', '18.9 ( 117 )', 'south melbourne', '6.12 ( 48 )', 'moorabbin oval', '18709', '24 july 1965'], ['fitzroy', '7.13 ( 55 )', 'footscray', '6.6 ( 42 )', 'brunswick street oval', '7456', '24 july 1965'], ['north melbourne', '11.15 ( 81 )', 'melbourne', '9.6 ( 60 )', 'city of coburg oval', '8312', '24 july 1965'], ['hawthorn', '7.5 ( 47 )', 'essendon', '10.11 ( 71 )', 'glenferrie oval', '11400', '24 july 1965'], ['richmond', '8.8 ( 56 )', 'collingwood', '12.7 ( 79 )', 'mcg', '56360', '24 july 1965'], ['geelong', '5.9 ( 39 )', 'carlton', '9.12 ( 66 )', 'kardinia park', '19568', '24 july 1965']] |
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/1-1342013-37.html.csv | majority | in the us house of representatives elections of 1954 , most of the pennsylvania incumbents were reelected . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 're - elected', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'result', 're - elected'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , most of them fuzzily match to re - elected .', 'tostr': 'most_eq { all_rows ; result ; re - elected } = true'} | most_eq { all_rows ; result ; re - elected } = true | for the result records of all rows , most of them fuzzily match to re - elected . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 're - elected_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 're - elected_4': 're - elected'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 're - elected_4': [0]} | ['district', 'incumbent', 'party', 'first elected', 'result', 'candidates'] | [['pennsylvania 6', 'hugh scott', 'republican', '1946', 're - elected', 'hugh scott ( r ) 50.6 % alexander hemphill ( d ) 49.4 %'], ['pennsylvania 8', 'karl c king', 'republican', '1951', 're - elected', 'karl c king ( r ) 51.2 % john p fullam ( d ) 48.8 %'], ['pennsylvania 9', 'paul b dague', 'republican', '1946', 're - elected', 'paul b dague ( r ) 62.7 % edward g wilson ( d ) 37.3 %'], ['pennsylvania 12', 'ivor d fenton', 'republican', '1938', 're - elected', 'ivor d fenton ( r ) 55.5 % charles e lotz ( d ) 44.5 %'], ['pennsylvania 15', 'francis e walter', 'democratic', '1932', 're - elected', 'francis e walter ( d ) 61.6 % leroy mikels ( r ) 38.4 %'], ['pennsylvania 17', 'alvin bush', 'republican', '1950', 're - elected', 'alvin bush ( r ) 56.5 % william t longe ( d ) 43.5 %'], ['pennsylvania 22', 'john p saylor', 'republican', '1949', 're - elected', 'john p saylor ( r ) 51.9 % robert s glass ( d ) 48.1 %'], ['pennsylvania 23', 'leon h gavin', 'republican', '1942', 're - elected', 'leon h gavin ( r ) 61.9 % fred c barr ( d ) 38.1 %'], ['pennsylvania 25', 'louis e graham', 'republican', '1938', 'lost re - election democratic gain', 'frank m clark ( d ) 53.5 % louis e graham ( r ) 46.5 %'], ['pennsylvania 26', 'thomas e morgan', 'democratic', '1944', 're - elected', 'thomas e morgan ( d ) 65.3 % branko stupar ( r ) 34.7 %']] |
casualty ( series 5 ) | https://en.wikipedia.org/wiki/Casualty_%28series_5%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-27208817-1.html.csv | count | 3 episodes in series 5 of casualty were directed by alan wareing . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'alan wareing', 'result': '3', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'director', 'alan wareing'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose director record fuzzily matches to alan wareing .', 'tostr': 'filter_eq { all_rows ; director ; alan wareing }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; director ; alan wareing } }', 'tointer': 'select the rows whose director record fuzzily matches to alan wareing . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; director ; alan wareing } } ; 3 } = true', 'tointer': 'select the rows whose director record fuzzily matches to alan wareing . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; director ; alan wareing } } ; 3 } = true | select the rows whose director record fuzzily matches to alan wareing . 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, 'director_5': 5, 'alan wareing_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', 'director_5': 'director', 'alan wareing_6': 'alan wareing', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'director_5': [0], 'alan wareing_6': [0], '3_7': [2]} | ['episode no episode no refers to the episodes number in the overall series , whereas series no refers to the episodes number in this particular series', 'series no', 'episode', 'director', 'writer', 'original airdate'] | [['53', '1', 'penalty', 'michael owen morris', 'ginnie hole', '7 september 1990'], ['54', '2', 'results', 'andrew morgan', 'ben aaronovitch', '14 september 1990'], ['55', '3', 'close to home', 'alan wareing', 'jim hill', '21 september 1990'], ['56', '4', 'street life', 'jim hill', 'ian briggs', '28 september 1990'], ['57', '5', 'hiding place', 'jim hill', 'tony etchells', '5 october 1990'], ['58', '6', 'salvation', 'michael owen morris', 'robin mukherjee', '12 october 1990'], ['59', '7', 'say it with flowers', 'alan wareing', 'rona munro', '19 october 1990'], ['60', '8', "love 's a pain", 'andrew morgan', 'sam snape', '26 october 1990'], ['61', '9', 'a will to die', 'michael brayshaw', 'christopher penfold', '2 november 1990'], ['62', '10', "big boys do n't cry", 'jenny killick', 'ginnie hole', '9 november 1990'], ['63', '11', 'remembrance', 'michael owen morris', 'robin mukherjee', '16 november 1990'], ['64', '12', "all 's fair", 'alan wareing', 'stephen wyatt', '30 november 1990']] |
soo line locomotives | https://en.wikipedia.org/wiki/Soo_Line_locomotives | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17248696-6.html.csv | comparative | a higher number of f-20 soo line locomotives were made than f-22 soo line locomotives . | {'row_1': '13', 'row_2': '15', 'col': '6', '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', 'class', 'f - 20'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose class record fuzzily matches to f - 20 .', 'tostr': 'filter_eq { all_rows ; class ; f - 20 }'}, 'quantity made'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; class ; f - 20 } ; quantity made }', 'tointer': 'select the rows whose class record fuzzily matches to f - 20 . take the quantity made record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'class', 'f - 22'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose class record fuzzily matches to f - 22 .', 'tostr': 'filter_eq { all_rows ; class ; f - 22 }'}, 'quantity made'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; class ; f - 22 } ; quantity made }', 'tointer': 'select the rows whose class record fuzzily matches to f - 22 . take the quantity made record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; class ; f - 20 } ; quantity made } ; hop { filter_eq { all_rows ; class ; f - 22 } ; quantity made } } = true', 'tointer': 'select the rows whose class record fuzzily matches to f - 20 . take the quantity made record of this row . select the rows whose class record fuzzily matches to f - 22 . take the quantity made record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; class ; f - 20 } ; quantity made } ; hop { filter_eq { all_rows ; class ; f - 22 } ; quantity made } } = true | select the rows whose class record fuzzily matches to f - 20 . take the quantity made record of this row . select the rows whose class record fuzzily matches to f - 22 . take the quantity made 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, 'class_7': 7, 'f - 20_8': 8, 'quantity made_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'class_11': 11, 'f - 22_12': 12, 'quantity made_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', 'class_7': 'class', 'f - 20_8': 'f - 20', 'quantity made_9': 'quantity made', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'class_11': 'class', 'f - 22_12': 'f - 22', 'quantity made_13': 'quantity made'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'class_7': [0], 'f - 20_8': [0], 'quantity made_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'class_11': [1], 'f - 22_12': [1], 'quantity made_13': [3]} | ['class', 'wheel arrangement', 'fleet number ( s )', 'manufacturer', 'year made', 'quantity made', 'quantity preserved'] | [['2 - 8 - 0 - ooooo - consolidation', '2 - 8 - 0 - ooooo - consolidation', '2 - 8 - 0 - ooooo - consolidation', '2 - 8 - 0 - ooooo - consolidation', '2 - 8 - 0 - ooooo - consolidation', '2 - 8 - 0 - ooooo - consolidation', '2 - 8 - 0 - ooooo - consolidation'], ['f - 1', '2 - 8 - 0', '403 - 405 , 407 - 412', 'schenectady', '1893', '9', '0'], ['f - 2', '2 - 8 - 0', '406', 'schenectady', '1893', '1', '0'], ['f - 3', '2 - 8 - 0', '413 - 416', 'schenectady', '1893', '4', '0'], ['f - 4', '2 - 8 - 0', '417', 'schenectady', '1893', '1', '0'], ['f - 6', '2 - 8 - 0', '400 - 402 , 418 - 427', 'rhode island', '1893', '13', '0'], ['f - 7', '2 - 8 - 0', '428 - 430', 'schenectady', '1900', '3', '0'], ['f - 8', '2 - 8 - 0', '431 - 444', 'alco - schenectady', '1902 - 1903', '14', '1'], ['f - 9', '2 - 8 - 0', '445 - 472', 'alco - schenectady', '1905 - 1906', '28', '1'], ['f - 10', '2 - 8 - 0', '473 - 474', 'alco - schenectady', '1909', '2', '0'], ['f - 11', '2 - 8 - 0', '475 - 484', 'alco - schenectady', '1910', '10', '0'], ['f - 12', '2 - 8 - 0', '485 - 499', 'alco - schenectady', '1912 - 1913', '15', '0'], ['f - 20', '2 - 8 - 0', '2400 - 2424', 'alco - schenectady', '1903 - 1907', '25', '1'], ['f - 21', '2 - 8 - 0', '2425 - 2428', 'alco - schenectady', '1909', '4', '1'], ['f - 22', '2 - 8 - 0', '2429 - 2443', 'alco - schenectady', '1911', '15', '1'], ['f - 23', '2 - 8 - 0', '2444 - 2450', 'alco - schenectady', '1914', '7', '0']] |
1984 - 85 philadelphia flyers season | https://en.wikipedia.org/wiki/1984%E2%80%9385_Philadelphia_Flyers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14208855-10.html.csv | comparative | in the game of the 18 th of april against the new york islanders the philadelphia flyers defense was more successful than on the 21st of april . | {'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', 'date', 'april 18'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose date record fuzzily matches to april 18 .', 'tostr': 'filter_eq { all_rows ; date ; april 18 }'}, 'score'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; date ; april 18 } ; score }', 'tointer': 'select the rows whose date record fuzzily matches to april 18 . take the score record of this row .'}, {'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'date', 'april 21'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose date record fuzzily matches to april 21 .', 'tostr': 'filter_eq { all_rows ; date ; april 21 }'}, 'score'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; date ; april 21 } ; score }', 'tointer': 'select the rows whose date record fuzzily matches to april 21 . take the score record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; date ; april 18 } ; score } ; hop { filter_eq { all_rows ; date ; april 21 } ; score } } = true', 'tointer': 'select the rows whose date record fuzzily matches to april 18 . take the score record of this row . select the rows whose date record fuzzily matches to april 21 . take the score record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; date ; april 18 } ; score } ; hop { filter_eq { all_rows ; date ; april 21 } ; score } } = true | select the rows whose date record fuzzily matches to april 18 . take the score record of this row . select the rows whose date record fuzzily matches to april 21 . take the score 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, 'date_7': 7, 'april 18_8': 8, 'score_9': 9, 'str_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'date_11': 11, 'april 21_12': 12, 'score_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', 'date_7': 'date', 'april 18_8': 'april 18', 'score_9': 'score', 'str_hop_3': 'str_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'date_11': 'date', 'april 21_12': 'april 21', 'score_13': 'score'} | {'less_4': [5], 'result_5': [], 'str_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'date_7': [0], 'april 18_8': [0], 'score_9': [2], 'str_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'date_11': [1], 'april 21_12': [1], 'score_13': [3]} | ['game', 'date', 'opponent', 'score', 'series'] | [['1', 'april 18', 'new york islanders', '3 - 0', 'flyers lead 1 - 0'], ['2', 'april 21', 'new york islanders', '5 - 2', 'flyers lead 2 - 0'], ['3', 'april 23', 'new york islanders', '5 - 3', 'flyers lead 3 - 0'], ['4', 'april 25', 'new york islanders', '2 - 6', 'flyers lead 3 - 1'], ['5', 'april 28', 'new york islanders', '1 - 0', 'flyers win 3 - 0']] |
stefan johansson | https://en.wikipedia.org/wiki/Stefan_Johansson | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1226329-2.html.csv | unique | 1983 was the only year that stefan johansson drove as an entrant with the spirit racing team . | {'scope': 'all', 'row': '2', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'spirit racing', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'entrant', 'spirit racing'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose entrant record fuzzily matches to spirit racing .', 'tostr': 'filter_eq { all_rows ; entrant ; spirit racing }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; entrant ; spirit racing } }', 'tointer': 'select the rows whose entrant record fuzzily matches to spirit racing . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'entrant', 'spirit racing'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose entrant record fuzzily matches to spirit racing .', 'tostr': 'filter_eq { all_rows ; entrant ; spirit racing }'}, 'year'], 'result': '1983', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; entrant ; spirit racing } ; year }'}, '1983'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; entrant ; spirit racing } ; year } ; 1983 }', 'tointer': 'the year record of this unqiue row is 1983 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; entrant ; spirit racing } } ; eq { hop { filter_eq { all_rows ; entrant ; spirit racing } ; year } ; 1983 } } = true', 'tointer': 'select the rows whose entrant record fuzzily matches to spirit racing . there is only one such row in the table . the year record of this unqiue row is 1983 .'} | and { only { filter_eq { all_rows ; entrant ; spirit racing } } ; eq { hop { filter_eq { all_rows ; entrant ; spirit racing } ; year } ; 1983 } } = true | select the rows whose entrant record fuzzily matches to spirit racing . there is only one such row in the table . the year record of this unqiue row is 1983 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'entrant_7': 7, 'spirit racing_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'year_9': 9, '1983_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'entrant_7': 'entrant', 'spirit racing_8': 'spirit racing', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'year_9': 'year', '1983_10': '1983'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'entrant_7': [0], 'spirit racing_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'year_9': [2], '1983_10': [3]} | ['year', 'entrant', 'chassis', 'engine', 'pts'] | [['1980', 'shadow cars', 'shadow dn11', 'ford cosworth dfv v8', '0'], ['1983', 'spirit racing', 'spirit 201c', 'honda v6 ( t / c )', '0'], ['1984', 'tyrrell racing organisation', 'tyrrell 012', 'ford cosworth dfy v8', '3'], ['1984', 'toleman group motorsport', 'toleman tg184', 'hart straight - 4 ( t / c )', '3'], ['1985', 'tyrrell racing organisation', 'tyrrell 012', 'ford cosworth dfy v8', '26'], ['1985', 'scuderia ferrari', 'ferrari 156 / 85', 'ferrari v6 ( t / c )', '26'], ['1986', 'scuderia ferrari', 'ferrari f1 / 86', 'ferrari v6 ( t / c )', '23'], ['1987', 'marlboro mclaren international', 'mclaren mp4 / 3', 'tag v6 ( t / c )', '30'], ['1988', 'ligier loto', 'ligier js31', 'judd v8', '0'], ['1989', 'moneytron onyx', 'onyx ore - 1', 'ford cosworth dfr v8', '6'], ['1990', 'moneytron onyx', 'onyx ore - 1', 'ford cosworth dfr v8', '0'], ['1991', 'automobiles gonfaronnaises sportives', 'ags jh25b', 'ford cosworth dfr v8', '0'], ['1991', 'footwork grand prix international', 'footwork fa12', 'porsche v12', '0'], ['1991', 'footwork grand prix international', 'footwork fa12c', 'ford cosworth dfr v8', '0']] |
2011 icf canoe sprint world championships | https://en.wikipedia.org/wiki/2011_ICF_Canoe_Sprint_World_Championships | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18771517-7.html.csv | aggregation | at the 2011 icf canoe sprint world championships there were a total of 8 golds awarded . | {'scope': 'all', 'col': '3', 'type': 'sum', 'result': '8', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'gold'], 'result': '8', 'ind': 0, 'tostr': 'sum { all_rows ; gold }'}, '8'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; gold } ; 8 } = true', 'tointer': 'the sum of the gold record of all rows is 8 .'} | round_eq { sum { all_rows ; gold } ; 8 } = true | the sum of the gold record of all rows is 8 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'gold_4': 4, '8_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'gold_4': 'gold', '8_5': '8'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'gold_4': [0], '8_5': [1]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'canada', '2', '1', '0', '3'], ['2', 'brazil', '2', '0', '1', '3'], ['3', 'great britain', '1', '1', '1', '3'], ['4', 'hungary', '1', '0', '1', '2'], ['5', 'austria', '1', '0', '0', '1'], ['5', 'romania', '1', '0', '0', '1'], ['7', 'germany', '0', '1', '1', '2'], ['7', 'italy', '0', '1', '1', '2'], ['7', 'poland', '0', '1', '1', '2'], ['7', 'spain', '0', '1', '1', '2'], ['7', 'united states', '0', '1', '1', '2'], ['12', 'russia', '0', '1', '0', '1'], ['total', 'total', '8', '8', '8', '24']] |
2008 thailand national games | https://en.wikipedia.org/wiki/2008_Thailand_National_Games | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14892957-1.html.csv | unique | bangkok was the only province to receive more than 100 gold medals . | {'scope': 'all', 'row': '1', 'col': '3', 'col_other': '2', 'criterion': 'greater_than', 'value': '100', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'gold', '100'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose gold record is greater than 100 .', 'tostr': 'filter_greater { all_rows ; gold ; 100 }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_greater { all_rows ; gold ; 100 } }', 'tointer': 'select the rows whose gold record is greater than 100 . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_greater', 'args': ['all_rows', 'gold', '100'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose gold record is greater than 100 .', 'tostr': 'filter_greater { all_rows ; gold ; 100 }'}, 'province'], 'result': 'bangkok', 'ind': 2, 'tostr': 'hop { filter_greater { all_rows ; gold ; 100 } ; province }'}, 'bangkok'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_greater { all_rows ; gold ; 100 } ; province } ; bangkok }', 'tointer': 'the province record of this unqiue row is bangkok .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_greater { all_rows ; gold ; 100 } } ; eq { hop { filter_greater { all_rows ; gold ; 100 } ; province } ; bangkok } } = true', 'tointer': 'select the rows whose gold record is greater than 100 . there is only one such row in the table . the province record of this unqiue row is bangkok .'} | and { only { filter_greater { all_rows ; gold ; 100 } } ; eq { hop { filter_greater { all_rows ; gold ; 100 } ; province } ; bangkok } } = true | select the rows whose gold record is greater than 100 . there is only one such row in the table . the province record of this unqiue row is bangkok . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_greater_0': 0, 'all_rows_6': 6, 'gold_7': 7, '100_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'province_9': 9, 'bangkok_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_greater_0': 'filter_greater', 'all_rows_6': 'all_rows', 'gold_7': 'gold', '100_8': '100', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'province_9': 'province', 'bangkok_10': 'bangkok'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_greater_0': [1, 2], 'all_rows_6': [0], 'gold_7': [0], '100_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'province_9': [2], 'bangkok_10': [3]} | ['rank', 'province', 'gold', 'silver', 'bronze', 'total'] | [['1', 'bangkok', '125', '90', '76', '291'], ['2', 'chonburi', '44', '34', '48', '126'], ['3', 'chiang mai', '37', '34', '41', '112'], ['4', 'phitsanulok', '24', '15', '31', '70'], ['5', 'suphan buri', '21', '27', '24', '72'], ['6', 'nakhon ratchasima', '21', '21', '31', '73'], ['7', 'nakhon si thammarat', '11', '10', '15', '36'], ['8', 'khon kaen', '9', '15', '8', '32'], ['9', 'pathum thani', '9', '11', '9', '29'], ['10', 'si sa ket', '9', '6', '21', '36']] |
delaware valley collegiate hockey conference | https://en.wikipedia.org/wiki/Delaware_Valley_Collegiate_Hockey_Conference | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16432543-1.html.csv | comparative | the shippensburg university was established earlier than the penn state harrisburg . | {'row_1': '7', 'row_2': '4', 'col': '5', 'col_other': '1', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'institution', 'shippensburg university'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose institution record fuzzily matches to shippensburg university .', 'tostr': 'filter_eq { all_rows ; institution ; shippensburg university }'}, 'established'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; institution ; shippensburg university } ; established }', 'tointer': 'select the rows whose institution record fuzzily matches to shippensburg university . take the established record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'institution', 'penn state harrisburg'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose institution record fuzzily matches to penn state harrisburg .', 'tostr': 'filter_eq { all_rows ; institution ; penn state harrisburg }'}, 'established'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; institution ; penn state harrisburg } ; established }', 'tointer': 'select the rows whose institution record fuzzily matches to penn state harrisburg . take the established record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; institution ; shippensburg university } ; established } ; hop { filter_eq { all_rows ; institution ; penn state harrisburg } ; established } } = true', 'tointer': 'select the rows whose institution record fuzzily matches to shippensburg university . take the established record of this row . select the rows whose institution record fuzzily matches to penn state harrisburg . take the established record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; institution ; shippensburg university } ; established } ; hop { filter_eq { all_rows ; institution ; penn state harrisburg } ; established } } = true | select the rows whose institution record fuzzily matches to shippensburg university . take the established record of this row . select the rows whose institution record fuzzily matches to penn state harrisburg . take the established 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, 'institution_7': 7, 'shippensburg university_8': 8, 'established_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'institution_11': 11, 'penn state harrisburg_12': 12, 'established_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', 'institution_7': 'institution', 'shippensburg university_8': 'shippensburg university', 'established_9': 'established', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'institution_11': 'institution', 'penn state harrisburg_12': 'penn state harrisburg', 'established_13': 'established'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'institution_7': [0], 'shippensburg university_8': [0], 'established_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'institution_11': [1], 'penn state harrisburg_12': [1], 'established_13': [3]} | ['institution', 'location', 'nickname', 'enrollment', 'established'] | [['university of delaware', 'newark , de', 'blue hens', '19391', '1743'], ['dickinson college', 'carlisle , pa', 'red devils', '2300', '1773'], ["mount saint mary 's university", 'emmitsburg , md', 'mountaineers', '2100', '1808'], ['penn state harrisburg', 'lower swatara township , pa', 'nittany lions', '4700', '1966'], ['rowan university', 'glassboro , nj', 'profs', '10483', '1923'], ['rutgers university - camden', 'camden , nj', 'raptors', '4497', '1766'], ['shippensburg university', 'shippensburg , pa', 'raiders', '6579', '1871'], ['widener university', 'philadelphia , pa', 'pride', '3204', '1862']] |
1971 icf canoe sprint world championships | https://en.wikipedia.org/wiki/1971_ICF_Canoe_Sprint_World_Championships | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18567469-4.html.csv | superlative | the soviet union won the most medals overall in the 1971 icf canoe sprint world championships . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '1', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '2', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'total'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; total }'}, 'nation'], 'result': 'soviet union', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; total } ; nation }'}, 'soviet union'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; total } ; nation } ; soviet union } = true', 'tointer': 'select the row whose total record of all rows is maximum . the nation record of this row is soviet union .'} | eq { hop { argmax { all_rows ; total } ; nation } ; soviet union } = true | select the row whose total record of all rows is maximum . the nation record of this row is soviet union . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'total_5': 5, 'nation_6': 6, 'soviet union_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'total_5': 'total', 'nation_6': 'nation', 'soviet union_7': 'soviet union'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'total_5': [0], 'nation_6': [1], 'soviet union_7': [2]} | ['rank', 'nation', 'gold', 'silver', 'bronze', 'total'] | [['1', 'soviet union', '7', '2', '6', '15'], ['2', 'hungary', '4', '5', '2', '11'], ['3', 'romania', '2', '2', '5', '9'], ['4', 'west germany', '2', '2', '1', '5'], ['5', 'east germany', '1', '1', '2', '4'], ['6', 'sweden', '1', '1', '0', '2'], ['7', 'bulgaria', '0', '0', '2', '2'], ['8', 'poland', '1', '0', '0', '1'], ['9', 'austria', '0', '1', '0', '1'], ['10', 'belgium', '0', '1', '0', '1'], ['11', 'czechoslovakia', '0', '1', '0', '1'], ['12', 'netherlands', '0', '1', '0', '1'], ['13', 'norway', '0', '1', '0', '1'], ['total', 'total', '18', '18', '18', '54']] |
1959 vfl season | https://en.wikipedia.org/wiki/1959_VFL_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10775038-8.html.csv | unique | only the game between fitzroy and collingwood was played at the brunswick street oval . | {'scope': 'all', 'row': '3', 'col': '5', 'col_other': '1,3', 'criterion': 'equal', 'value': 'brunswick street oval', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'brunswick street oval'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to brunswick street oval .', 'tostr': 'filter_eq { all_rows ; venue ; brunswick street oval }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; venue ; brunswick street oval } }', 'tointer': 'select the rows whose venue record fuzzily matches to brunswick street oval . 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', 'venue', 'brunswick street oval'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to brunswick street oval .', 'tostr': 'filter_eq { all_rows ; venue ; brunswick street oval }'}, 'home team'], 'result': 'fitzroy', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; venue ; brunswick street oval } ; home team }'}, 'fitzroy'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; venue ; brunswick street oval } ; home team } ; fitzroy }', 'tointer': 'the home team record of this unqiue row is fitzroy .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'brunswick street oval'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to brunswick street oval .', 'tostr': 'filter_eq { all_rows ; venue ; brunswick street oval }'}, 'away team'], 'result': 'collingwood', 'ind': 4, 'tostr': 'hop { filter_eq { all_rows ; venue ; brunswick street oval } ; away team }'}, 'collingwood'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; venue ; brunswick street oval } ; away team } ; collingwood }', 'tointer': 'the away team record of this unqiue row is collingwood .'}], 'result': True, 'ind': 6, 'tostr': 'and { eq { hop { filter_eq { all_rows ; venue ; brunswick street oval } ; home team } ; fitzroy } ; eq { hop { filter_eq { all_rows ; venue ; brunswick street oval } ; away team } ; collingwood } }', 'tointer': 'the home team record of this unqiue row is fitzroy . the away team record of this unqiue row is collingwood .'}], 'result': True, 'ind': 7, 'tostr': 'and { only { filter_eq { all_rows ; venue ; brunswick street oval } } ; and { eq { hop { filter_eq { all_rows ; venue ; brunswick street oval } ; home team } ; fitzroy } ; eq { hop { filter_eq { all_rows ; venue ; brunswick street oval } ; away team } ; collingwood } } } = true', 'tointer': 'select the rows whose venue record fuzzily matches to brunswick street oval . there is only one such row in the table . the home team record of this unqiue row is fitzroy . the away team record of this unqiue row is collingwood .'} | and { only { filter_eq { all_rows ; venue ; brunswick street oval } } ; and { eq { hop { filter_eq { all_rows ; venue ; brunswick street oval } ; home team } ; fitzroy } ; eq { hop { filter_eq { all_rows ; venue ; brunswick street oval } ; away team } ; collingwood } } } = true | select the rows whose venue record fuzzily matches to brunswick street oval . there is only one such row in the table . the home team record of this unqiue row is fitzroy . the away team record of this unqiue row is collingwood . | 10 | 8 | {'and_7': 7, 'result_8': 8, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_9': 9, 'venue_10': 10, 'brunswick street oval_11': 11, 'and_6': 6, 'str_eq_3': 3, 'str_hop_2': 2, 'home team_12': 12, 'fitzroy_13': 13, 'str_eq_5': 5, 'str_hop_4': 4, 'away team_14': 14, 'collingwood_15': 15} | {'and_7': 'and', 'result_8': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_9': 'all_rows', 'venue_10': 'venue', 'brunswick street oval_11': 'brunswick street oval', 'and_6': 'and', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'home team_12': 'home team', 'fitzroy_13': 'fitzroy', 'str_eq_5': 'str_eq', 'str_hop_4': 'str_hop', 'away team_14': 'away team', 'collingwood_15': 'collingwood'} | {'and_7': [8], 'result_8': [], 'only_1': [7], 'filter_str_eq_0': [1, 2, 4], 'all_rows_9': [0], 'venue_10': [0], 'brunswick street oval_11': [0], 'and_6': [7], 'str_eq_3': [6], 'str_hop_2': [3], 'home team_12': [2], 'fitzroy_13': [3], 'str_eq_5': [6], 'str_hop_4': [5], 'away team_14': [4], 'collingwood_15': [5]} | ['home team', 'home team score', 'away team', 'away team score', 'venue', 'crowd', 'date'] | [['footscray', '4.13 ( 37 )', 'richmond', '9.9 ( 63 )', 'western oval', '11533', '13 june 1959'], ['north melbourne', '12.12 ( 84 )', 'hawthorn', '8.6 ( 54 )', 'arden street oval', '12500', '13 june 1959'], ['fitzroy', '5.10 ( 40 )', 'collingwood', '3.12 ( 30 )', 'brunswick street oval', '17632', '13 june 1959'], ['south melbourne', '16.13 ( 109 )', 'st kilda', '7.11 ( 53 )', 'lake oval', '29500', '15 june 1959'], ['melbourne', '19.15 ( 129 )', 'essendon', '8.8 ( 56 )', 'mcg', '52880', '15 june 1959'], ['geelong', '11.13 ( 79 )', 'carlton', '12.16 ( 88 )', 'kardinia park', '11533', '15 june 1959']] |
b " the women 's ashes " | https://en.wikipedia.org/wiki/The_Women%27s_Ashes | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2554479-2.html.csv | count | 7 series of the the women 's ashes competition resulted in a draw . | {'scope': 'all', 'criterion': 'equal', 'value': 'drawn', 'result': '7', 'col': '9', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'series result', 'drawn'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose series result record fuzzily matches to drawn .', 'tostr': 'filter_eq { all_rows ; series result ; drawn }'}], 'result': '7', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; series result ; drawn } }', 'tointer': 'select the rows whose series result record fuzzily matches to drawn . the number of such rows is 7 .'}, '7'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; series result ; drawn } } ; 7 } = true', 'tointer': 'select the rows whose series result record fuzzily matches to drawn . the number of such rows is 7 .'} | eq { count { filter_eq { all_rows ; series result ; drawn } } ; 7 } = true | select the rows whose series result record fuzzily matches to drawn . the number of such rows is 7 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'series result_5': 5, 'drawn_6': 6, '7_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'series result_5': 'series result', 'drawn_6': 'drawn', '7_7': '7'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'series result_5': [0], 'drawn_6': [0], '7_7': [2]} | ['series', 'season', 'played in', 'first match', 'tests played ( sched )', 'tests won by australia', 'tests won by england', 'tests drawn', 'series result', 'holder at series end'] | [['1', '1934 - 35', 'australia', '28 december 1934', '3', '0', '2', '1', 'england', 'england'], ['2', '1937', 'england', '12 june 1937', '3', '1', '1', '1', 'drawn', 'england'], ['3', '1949 - 50', 'australia', '15 january 1949', '3', '1', '0', '2', 'australia', 'australia'], ['4', '1951', 'england', '16 june 1951', '3', '1', '1', '1', 'drawn', 'australia'], ['5', '1957 - 58', 'australia', '7 february 1958', '3 ( 4 )', '0', '0', '3', 'drawn', 'australia'], ['6', '1963', 'england', '15 june 1961', '3', '0', '1', '2', 'england', 'england'], ['7', '1968 - 69', 'australia', '27 december 1968', '3', '0', '0', '3', 'drawn', 'england'], ['8', '1976', 'england', '19 june 1976', '3', '0', '0', '3', 'drawn', 'england'], ['9', '1984 - 85', 'australia', '13 december 1984', '5', '2', '1', '2', 'australia', 'australia'], ['10', '1987', 'england', '1 august 1987', '3', '1', '0', '2', 'australia', 'australia'], ['11', '1991 - 92', 'australia', '19 february 1992', '1', '1', '0', '0', 'australia', 'australia'], ['12', '1998', 'england', '6 august 1998', '3', '0', '0', '3', 'drawn', 'australia'], ['13', '2001', 'england', '24 june 2001', '2', '2', '0', '0', 'australia', 'australia'], ['14', '2002 - 2003', 'australia', '15 february 2003', '2', '1', '0', '1', 'australia', 'australia'], ['15', '2005', 'england', '9 august 2005', '2', '0', '1', '1', 'england', 'england'], ['16', '2007 - 2008', 'australia', '15 february 2008', '1', '0', '1', '0', 'england', 'england'], ['17', '2009', 'england', '10 july 2009', '1', '0', '0', '1', 'drawn', 'england']] |
1989 pga championship | https://en.wikipedia.org/wiki/1989_PGA_Championship | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18135029-1.html.csv | aggregation | the avergae total for all players in the 1989 pga championship was 289.5 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '289.5', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'total'], 'result': '289.5', 'ind': 0, 'tostr': 'avg { all_rows ; total }'}, '289.5'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; total } ; 289.5 } = true', 'tointer': 'the average of the total record of all rows is 289.5 .'} | round_eq { avg { all_rows ; total } ; 289.5 } = true | the average of the total record of all rows is 289.5 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'total_4': 4, '289.5_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'total_4': 'total', '289.5_5': '289.5'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'total_4': [0], '289.5_5': [1]} | ['player', 'country', 'year ( s ) won', 'total', 'to par', 'finish'] | [['jeff sluman', 'united states', '1988', '284', '- 4', 't24'], ['jack nicklaus', 'united states', '1963 , 1971 , 1973 1975 , 1980', '285', '- 3', 't27'], ['larry nelson', 'united states', '1981 , 1987', '288', 'e', 't46'], ['raymond floyd', 'united states', '1969 , 1982', '288', 'e', 't46'], ['hubert green', 'united states', '1985', '295', '+ 7', '66'], ['dave stockton', 'united states', '1970 , 1976', '297', '+ 9', '68']] |
nikon coolpix series | https://en.wikipedia.org/wiki/Nikon_Coolpix_series | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1017391-7.html.csv | aggregation | from nikon coolpix series models p1 to p7100 , released from september 1 , 2005 to august 16 , 2011 , the sensor resolution average is 9.88125 megapixels . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '9.88125', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'sensor res , size'], 'result': '9.88125', 'ind': 0, 'tostr': 'avg { all_rows ; sensor res , size }'}, '9.88125'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; sensor res , size } ; 9.88125 } = true', 'tointer': 'the average of the sensor res , size record of all rows is 9.88125 .'} | round_eq { avg { all_rows ; sensor res , size } ; 9.88125 } = true | the average of the sensor res , size record of all rows is 9.88125 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'sensor res , size_4': 4, '9.88125_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'sensor res , size_4': 'sensor res , size', '9.88125_5': '9.88125'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'sensor res , size_4': [0], '9.88125_5': [1]} | ['model', 'release date', 'sensor res , size', 'lens ( 35 mmequiv ) zoom , aperture', 'screen size , pixels', 'dimensions whd ( mm )', 'weight'] | [['p1', 'sep 1 , 2005', '8.0 mp 32642448 1 / 1.8', '36 - 126 mm ( 3.5 ) f / 2.7 - 5.2', '2.5 110000', '916039', '170 g ( w / out batt )'], ['p2', 'sep 1 , 2005', '5.0 mp 25921944 1 / 1.8', '36 - 126 mm ( 3.5 ) f / 2.7 - 5.2', '2.5 110000', '916039', '170 g ( w / out batt )'], ['p3', 'feb 21 , 2006', '8.1 mp 32642448 1 / 1.8', '36 - 126 mm ( 3.5 ) f / 2.7 - 5.3', '2.5 110000', '926131', '170 g ( w / out batt )'], ['p4', 'feb 21 , 2006', '8.1 mp 32642448 1 / 1.8', '36 - 126 mm ( 3.5 ) f / 2.7 - 5.3', '2.5 110000', '926131', '170 g ( w / out batt )'], ['p50', 'aug 30 , 2007', '8.1 mp 32642448 1 / 2.5', '28 - 102 mm ( 3.6 ) f / 2.8 - 5.6', '2.4 115000', '94.56644', '160 g ( w / out batt )'], ['p60', 'jan 29 , 2008', '8.1 mp 32642448 1 / 2.5', '36 - 180 mm ( 5 ) f / 3.6 - 4.5', '2.5 153000', '95.563.536', '160 g ( w / out batt )'], ['p80', 'apr 10 , 2008', '10.1 mp 36482736 1 / 2.33', '27 - 486 mm ( 18 ) f / 2.8 - 4.5', '2.7 230000', '1107978', '365 g ( w / out batt )'], ['p90', 'feb 3 , 2009', '12.1 mp 40003000 1 / 2.33', '26 - 624 mm ( 24 ) f / 2.8 - 5', '3 230000', '1148399', '460 g ( w / out batt )'], ['p100', 'feb 3 , 2010', '10.3 mp 36482736 1 / 2.3', '26 - 678 mm ( 26 ) f / 2.8 - 5', '3 460000', '114.482.798.6', '481 g ( w / batt )'], ['p300', 'feb 9 , 2011', '12.2 mp 40003000 1 / 2.3', '24 - 100 mm ( 4.2 ) f / 1.8 - 4.9', '3 921000', '10358.332', '189 g ( w / batt )'], ['p500', 'feb 9 , 2011', '12.1 mp 40003000 1 / 2.3', '22.5 - 810 mm ( 36 ) f / 3.4 - 5.7', '3 921000', '115.583.7102.5', '494 g ( w / batt )'], ['p5000', 'feb 20 , 2007', '10.0 mp 36482736 1 / 1.8', '36 - 126 mm ( 3.5 ) f / 2.7 - 5.3', '2.5 230000', '9864.541', '200 g ( w / out batt )'], ['p5100', 'aug 30 , 2007', '12.1 mp 40003000 1 / 1.72', '35 - 123 mm ( 3.5 ) f / 2.7 - 5.3', '2.5 230000', '9864.541', '200 g ( w / out batt )'], ['p6000', 'aug 7 , 2008', '13.5 mp 42243168 1 / 1.7', '28 - 112 mm ( 4 ) f / 2.7 - 5.9', '2.7 230000', '10765.542', '240 g ( w / out batt )'], ['p7000', 'sep 8 , 2010', '10.2 mp 32642448 1 / 1.7', '28 - 200 mm ( 7.1 ) f / 2.8 - 5.6', '3 921000', '114.27744.8', '360 g ( w / batt )'], ['p7100', 'aug 26 , 2011', '10.1 mp 36482736 1 / 1.7', '28 - 200 mm ( 7.1 ) f / 2.8 - 5.6', '3 921000', '1167748', '395 g ( w / batt )']] |
2008 - 09 temple owls men 's basketball team | https://en.wikipedia.org/wiki/2008%E2%80%9309_Temple_Owls_men%27s_basketball_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-30054758-3.html.csv | ordinal | the december 20 game against kansas was the 4th earliest game played by the 2008-09 temple owls . | {'row': '4', 'col': '2', 'order': '4', 'col_other': '3', 'max_or_min': 'min_to_max', 'value_mentioned': 'yes', 'scope': 'all', 'subset': None} | {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'nth_min', 'args': ['all_rows', 'date', '4'], 'result': 'december 20', 'ind': 0, 'tostr': 'nth_min { all_rows ; date ; 4 }', 'tointer': 'the 4th minimum date record of all rows is december 20 .'}, 'december 20'], 'result': True, 'ind': 1, 'tostr': 'eq { nth_min { all_rows ; date ; 4 } ; december 20 }', 'tointer': 'the 4th minimum date record of all rows is december 20 .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': ['all_rows', 'date', '4'], 'result': None, 'ind': 2, 'tostr': 'nth_argmin { all_rows ; date ; 4 }'}, 'team'], 'result': 'kansas', 'ind': 3, 'tostr': 'hop { nth_argmin { all_rows ; date ; 4 } ; team }'}, 'kansas'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { nth_argmin { all_rows ; date ; 4 } ; team } ; kansas }', 'tointer': 'the team record of the row with 4th minimum date record is kansas .'}], 'result': True, 'ind': 5, 'tostr': 'and { eq { nth_min { all_rows ; date ; 4 } ; december 20 } ; eq { hop { nth_argmin { all_rows ; date ; 4 } ; team } ; kansas } } = true', 'tointer': 'the 4th minimum date record of all rows is december 20 . the team record of the row with 4th minimum date record is kansas .'} | and { eq { nth_min { all_rows ; date ; 4 } ; december 20 } ; eq { hop { nth_argmin { all_rows ; date ; 4 } ; team } ; kansas } } = true | the 4th minimum date record of all rows is december 20 . the team record of the row with 4th minimum date record is kansas . | 6 | 6 | {'and_5': 5, 'result_6': 6, 'eq_1': 1, 'nth_min_0': 0, 'all_rows_7': 7, 'date_8': 8, '4_9': 9, 'december 20_10': 10, 'str_eq_4': 4, 'str_hop_3': 3, 'nth_argmin_2': 2, 'all_rows_11': 11, 'date_12': 12, '4_13': 13, 'team_14': 14, 'kansas_15': 15} | {'and_5': 'and', 'result_6': 'true', 'eq_1': 'eq', 'nth_min_0': 'nth_min', 'all_rows_7': 'all_rows', 'date_8': 'date', '4_9': '4', 'december 20_10': 'december 20', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'nth_argmin_2': 'nth_argmin', 'all_rows_11': 'all_rows', 'date_12': 'date', '4_13': '4', 'team_14': 'team', 'kansas_15': 'kansas'} | {'and_5': [6], 'result_6': [], 'eq_1': [5], 'nth_min_0': [1], 'all_rows_7': [0], 'date_8': [0], '4_9': [0], 'december 20_10': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'nth_argmin_2': [3], 'all_rows_11': [2], 'date_12': [2], '4_13': [2], 'team_14': [3], 'kansas_15': [4]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'record'] | [['6', 'december 3', 'miami ( oh )', 'l 68 - 52', 'sergio olmos - 12', 'brooks - 6', 'inge - 5', 'liacouras center , philadelphia , pa ( 5029 )', '3 - 3'], ['7', 'december 6', 'penn state', 'w 65 - 59', 'inge - 19', 'allen - 10', 'inge - 6', 'bryce jordan center , state college , pa ( 9833 )', '4 - 3'], ['8', 'december 13', '8 tennessee', 'w 88 - 72', 'christmas - 35', 'brooks - 10', 'inge - 4', 'liacouras center , philadelphia , pa ( 8068 )', '5 - 3'], ['9', 'december 20', 'kansas', 'l 71 - 59', 'christmas - 21', 'allen - 7', 'allen - 5', 'phog allen fieldhouse , lawrence , ks ( 16300 )', '5 - 4'], ['10', 'december 22', 'long beach state', 'l 76 - 71', 'christmas - 19', 'allen - 11', 'allen - 5', 'walter pyramid , long beach , ca ( 2042 )', '5 - 5']] |
1980 buffalo bills season | https://en.wikipedia.org/wiki/1980_Buffalo_Bills_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16677887-2.html.csv | ordinal | in the 1980 buffalo bills season , the 2nd highest attendance was at the game on september 7th . | {'row': '1', 'col': '9', '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', 'attendance', '2'], 'result': None, 'ind': 0, 'tostr': 'nth_argmax { all_rows ; attendance ; 2 }'}, 'date'], 'result': 'sept 7', 'ind': 1, 'tostr': 'hop { nth_argmax { all_rows ; attendance ; 2 } ; date }'}, 'sept 7'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; sept 7 } = true', 'tointer': 'select the row whose attendance record of all rows is 2nd maximum . the date record of this row is sept 7 .'} | eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; sept 7 } = true | select the row whose attendance record of all rows is 2nd maximum . the date record of this row is sept 7 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmax_0': 0, 'all_rows_4': 4, 'attendance_5': 5, '2_6': 6, 'date_7': 7, 'sept 7_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', 'attendance_5': 'attendance', '2_6': '2', 'date_7': 'date', 'sept 7_8': 'sept 7'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmax_0': [1], 'all_rows_4': [0], 'attendance_5': [0], '2_6': [0], 'date_7': [1], 'sept 7_8': [2]} | ['game', 'date', 'opponent', 'result', 'bills points', 'opponents', 'bills first downs', 'record', 'attendance'] | [['1', 'sept 7', 'miami dolphins', 'win', '17', '7', '22', '1 - 0', '79598'], ['2', 'sept 14', 'new york jets', 'win', '20', '10', '22', '2 - 0', '65315'], ['3', 'sept 21', 'new orleans saints', 'win', '35', '26', '26', '3 - 0', '51154'], ['4', 'sept 28', 'oakland raiders', 'win', '24', '7', '25', '4 - 0', '77259'], ['5', 'oct 5', 'san diego chargers', 'win', '26', '24', '14', '5 - 0', '51982'], ['6', 'oct 12', 'baltimore colts', 'loss', '12', '17', '24', '5 - 1', '73634'], ['7', 'oct 19', 'miami dolphins', 'loss', '14', '17', '18', '5 - 2', '41636'], ['8', 'oct 26', 'new england patriots', 'win', '31', '13', '21', '6 - 2', '75092'], ['9', 'nov 2', 'atlanta falcons', 'loss', '14', '30', '20', '6 - 3', '57959'], ['10', 'nov 9', 'new york jets', 'win', '31', '24', '17', '7 - 3', '45677'], ['11', 'nov 16', 'cincinnati bengals', 'win', '14', '0', '22', '8 - 3', '40836'], ['12', 'nov 23', 'pittsburgh steelers', 'win', '28', '13', '23', '9 - 3', '79659'], ['13', 'nov 30', 'baltimore colts', 'loss', '24', '28', '24', '9 - 4', '36184'], ['14', 'dec 7', 'los angeles rams', 'win', '10', '7', '15', '10 - 4', '77133'], ['15', 'dec 14', 'new england patriots', 'loss', '2', '24', '28', '10 - 5', '58324']] |
2007 - 08 tampa bay lightning season | https://en.wikipedia.org/wiki/2007%E2%80%9308_Tampa_Bay_Lightning_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11766617-3.html.csv | count | in the 2007 - 08 tampa bay lightning season , when tampa bay was the home team , there were 4 games where attendance was over 19000 . | {'scope': 'subset', 'criterion': 'greater_than', 'value': '19,000', 'result': '4', 'col': '6', 'subset': {'col': '4', 'criterion': 'equal', 'value': 'tampa bay'}} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_greater', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'home', 'tampa bay'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; home ; tampa bay }', 'tointer': 'select the rows whose home record fuzzily matches to tampa bay .'}, 'attendance', '19,000'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose home record fuzzily matches to tampa bay . among these rows , select the rows whose attendance record is greater than 19,000 .', 'tostr': 'filter_greater { filter_eq { all_rows ; home ; tampa bay } ; attendance ; 19,000 }'}], 'result': '4', 'ind': 2, 'tostr': 'count { filter_greater { filter_eq { all_rows ; home ; tampa bay } ; attendance ; 19,000 } }', 'tointer': 'select the rows whose home record fuzzily matches to tampa bay . among these rows , select the rows whose attendance record is greater than 19,000 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 3, 'tostr': 'eq { count { filter_greater { filter_eq { all_rows ; home ; tampa bay } ; attendance ; 19,000 } } ; 4 } = true', 'tointer': 'select the rows whose home record fuzzily matches to tampa bay . among these rows , select the rows whose attendance record is greater than 19,000 . the number of such rows is 4 .'} | eq { count { filter_greater { filter_eq { all_rows ; home ; tampa bay } ; attendance ; 19,000 } } ; 4 } = true | select the rows whose home record fuzzily matches to tampa bay . among these rows , select the rows whose attendance record is greater than 19,000 . 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, 'home_6': 6, 'tampa bay_7': 7, 'attendance_8': 8, '19,000_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', 'home_6': 'home', 'tampa bay_7': 'tampa bay', 'attendance_8': 'attendance', '19,000_9': '19,000', '4_10': '4'} | {'eq_3': [4], 'result_4': [], 'count_2': [3], 'filter_greater_1': [2], 'filter_str_eq_0': [1], 'all_rows_5': [0], 'home_6': [0], 'tampa bay_7': [0], 'attendance_8': [1], '19,000_9': [1], '4_10': [3]} | ['date', 'visitor', 'score', 'home', 'decision', 'attendance', 'record'] | [['october 4', 'new jersey', '1 - 3', 'tampa bay', 'holmqvist', '19454', '1 - 0 - 0'], ['october 6', 'atlanta', '2 - 5', 'tampa bay', 'holmqvist', '19220', '2 - 0 - 0'], ['october 10', 'florida', '1 - 2', 'tampa bay', 'holmqvist', '18540', '3 - 0 - 0'], ['october 13', 'tampa bay', '4 - 6', 'florida', 'denis', '15801', '3 - 1 - 0'], ['october 18', 'tampa bay', '1 - 4', 'boston', 'holmqvist', '16363', '3 - 2 - 0'], ['october 20', 'atlanta', '2 - 6', 'tampa bay', 'holmqvist', '19420', '4 - 2 - 0'], ['october 24', 'tampa bay', '3 - 5', 'washington', 'denis', '10226', '4 - 3 - 0'], ['october 25', 'philadelphia', '2 - 5', 'tampa bay', 'holmqvist', '18616', '5 - 3 - 0'], ['october 27', 'buffalo', '4 - 3', 'tampa bay', 'holmqvist', '19804', '5 - 3 - 1'], ['october 29', 'tampa bay', '1 - 3', 'ny rangers', 'holmqvist', '18200', '5 - 4 - 1'], ['october 31', 'tampa bay', '1 - 6', 'new jersey', 'holmqvist', '13218', '5 - 5 - 1']] |
rotores de portugal | https://en.wikipedia.org/wiki/Rotores_de_Portugal | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16965464-1.html.csv | ordinal | in rotores de portugal , squadron 33 is the earliest between the year 1976 and 2005 . | {'scope': 'subset', 'row': '1', 'col': '5', 'order': '1', 'col_other': '3', 'max_or_min': 'min_to_max', 'value_mentioned': 'no', 'subset': {'col': '5', 'criterion': 'less_than_eq', 'value': '2005'}} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'nth_argmin', 'args': [{'func': 'filter_less_eq', 'args': ['all_rows', 'dates', '2005'], 'result': None, 'ind': 0, 'tostr': 'filter_less_eq { all_rows ; dates ; 2005 }', 'tointer': 'select the rows whose dates record is less than or equal to 2005 .'}, 'dates', '1'], 'result': None, 'ind': 1, 'tostr': 'nth_argmin { filter_less_eq { all_rows ; dates ; 2005 } ; dates ; 1 }'}, 'squadron'], 'result': 'squadron 33', 'ind': 2, 'tostr': 'hop { nth_argmin { filter_less_eq { all_rows ; dates ; 2005 } ; dates ; 1 } ; squadron }'}, 'squadron 33'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { nth_argmin { filter_less_eq { all_rows ; dates ; 2005 } ; dates ; 1 } ; squadron } ; squadron 33 } = true', 'tointer': 'select the rows whose dates record is less than or equal to 2005 . select the row whose dates record of these rows is 1st minimum . the squadron record of this row is squadron 33 .'} | eq { hop { nth_argmin { filter_less_eq { all_rows ; dates ; 2005 } ; dates ; 1 } ; squadron } ; squadron 33 } = true | select the rows whose dates record is less than or equal to 2005 . select the row whose dates record of these rows is 1st minimum . the squadron record of this row is squadron 33 . | 4 | 4 | {'str_eq_3': 3, 'result_4': 4, 'str_hop_2': 2, 'nth_argmin_1': 1, 'filter_less_eq_0': 0, 'all_rows_5': 5, 'dates_6': 6, '2005_7': 7, 'dates_8': 8, '1_9': 9, 'squadron_10': 10, 'squadron 33_11': 11} | {'str_eq_3': 'str_eq', 'result_4': 'true', 'str_hop_2': 'str_hop', 'nth_argmin_1': 'nth_argmin', 'filter_less_eq_0': 'filter_less_eq', 'all_rows_5': 'all_rows', 'dates_6': 'dates', '2005_7': '2005', 'dates_8': 'dates', '1_9': '1', 'squadron_10': 'squadron', 'squadron 33_11': 'squadron 33'} | {'str_eq_3': [4], 'result_4': [], 'str_hop_2': [3], 'nth_argmin_1': [2], 'filter_less_eq_0': [1], 'all_rows_5': [0], 'dates_6': [0], '2005_7': [0], 'dates_8': [1], '1_9': [1], 'squadron_10': [2], 'squadron 33_11': [3]} | ['aircraft', 'origin', 'squadron', 'display aircraft', 'dates'] | [['sud aviation alouette iii', 'france', 'squadron 33', '4', '1976-1980'], ['sud aviation alouette iii', 'france', 'squadron 102', '2', '1982-1992'], ['sud aviation alouette iii', 'france', 'squadron 111', '4', '1993-1994'], ['sud aviation alouette iii', 'france', 'squadron 552', '2', '2004-2005'], ['sud aviation alouette iii', 'france', 'squadron 552', '3', '2006-present']] |
2008 - 09 orlando magic season | https://en.wikipedia.org/wiki/2008%E2%80%9309_Orlando_Magic_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-17311797-11.html.csv | count | rafer alston had the most assists on three occasions . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'rafer alston', 'result': '3', 'col': '7', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'high assists', 'rafer alston'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose high assists record fuzzily matches to rafer alston .', 'tostr': 'filter_eq { all_rows ; high assists ; rafer alston }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; high assists ; rafer alston } }', 'tointer': 'select the rows whose high assists record fuzzily matches to rafer alston . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; high assists ; rafer alston } } ; 3 } = true', 'tointer': 'select the rows whose high assists record fuzzily matches to rafer alston . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; high assists ; rafer alston } } ; 3 } = true | select the rows whose high assists record fuzzily matches to rafer alston . 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 assists_5': 5, 'rafer alston_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 assists_5': 'high assists', 'rafer alston_6': 'rafer alston', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'high assists_5': [0], 'rafer alston_6': [0], '3_7': [2]} | ['game', 'date', 'team', 'score', 'high points', 'high rebounds', 'high assists', 'location attendance', 'series'] | [['1', 'april 19', '76ers', 'l 98 - 100 ( ot )', 'dwight howard ( 31 )', 'dwight howard ( 16 )', 'rafer alston ( 5 )', 'amway arena 17461', '0 - 1'], ['2', 'april 22', '76ers', 'w 96 - 87 ( ot )', 'courtney lee ( 24 )', 'dwight howard ( 10 )', 'rashard lewis ( 6 )', 'amway arena 17461', '1 - 1'], ['3', 'april 24', '76ers', 'l 94 - 96 ( ot )', 'dwight howard ( 36 )', 'dwight howard ( 11 )', 'courtney lee ( 5 )', 'wachovia center 16492', '1 - 2'], ['4', 'april 26', '76ers', 'w 84 - 81 ( ot )', 'dwight howard ( 18 )', 'dwight howard ( 18 )', 'rafer alston ( 5 )', 'wachovia center 16464', '2 - 2'], ['5', 'april 28', '76ers', 'w 91 - 78 ( ot )', 'dwight howard , rashard lewis ( 24 )', 'dwight howard ( 24 )', 'rafer alston , hedo türkoğlu ( 4 )', 'amway arena 17461', '3 - 2']] |
new england women 's and men 's athletic conference | https://en.wikipedia.org/wiki/New_England_Women%27s_and_Men%27s_Athletic_Conference | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1974782-1.html.csv | superlative | the school in the new england athletic conference with the highest enrollment is massachusetts institute of technology . | {'scope': 'all', 'col_superlative': '6', 'row_superlative': '4', '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', 'enrollment'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; enrollment }'}, 'institution'], 'result': 'massachusetts institute of technology', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; enrollment } ; institution }'}, 'massachusetts institute of technology'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; enrollment } ; institution } ; massachusetts institute of technology } = true', 'tointer': 'select the row whose enrollment record of all rows is maximum . the institution record of this row is massachusetts institute of technology .'} | eq { hop { argmax { all_rows ; enrollment } ; institution } ; massachusetts institute of technology } = true | select the row whose enrollment record of all rows is maximum . the institution record of this row is massachusetts institute of technology . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'enrollment_5': 5, 'institution_6': 6, 'massachusetts institute of technology_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'enrollment_5': 'enrollment', 'institution_6': 'institution', 'massachusetts institute of technology_7': 'massachusetts institute of technology'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'enrollment_5': [0], 'institution_6': [1], 'massachusetts institute of technology_7': [2]} | ['institution', 'location', 'nickname', 'founded', 'type', 'enrollment', 'joined'] | [['babson college', 'wellesley , massachusetts', 'beavers', '1919', 'private / non - sectarian', '3200', '1985'], ['clark university', 'worcester , massachusetts', 'cougars', '1887', 'private / non - sectarian', '2780', '1995'], ['emerson college', 'boston , massachusetts', 'lions', '1880', 'private / non - sectarian', '4290', '2013'], ['massachusetts institute of technology', 'cambridge , massachusetts', 'engineers', '1861', 'private / non - sectarian', '10253', '1985'], ['mount holyoke college', 'south hadley , massachusetts', 'lyons', '1837', 'private / non - sectarian', '2100', '1987'], ['smith college', 'northampton , massachusetts', 'pioneers', '1871', 'private / non - sectarian', '2600', '1985'], ['springfield college', 'springfield , massachusetts', 'pride', '1885', 'private / non - sectarian', '5062', '1998'], ['united states coast guard academy', 'new london , connecticut', 'bears', '1876', 'federal / military', '990', '1998'], ['wellesley college', 'wellesley , massachusetts', 'blue', '1870', 'private / non - sectarian', '2300', '1985'], ['wheaton college', 'norton , massachusetts', 'lyons', '1834', 'private / non - sectarian', '1550', '1985']] |
1970 isle of man tt | https://en.wikipedia.org/wiki/1970_Isle_of_Man_TT | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-10301911-2.html.csv | unique | brian finch was the only driver to not ride either a suzuki or triumph motorcycle in the top 7 . | {'scope': 'all', 'row': '7', 'col': '3', 'col_other': '2', 'criterion': 'fuzzily_match', 'value': 'velocette', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'velocette'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team record fuzzily matches to velocette .', 'tostr': 'filter_eq { all_rows ; team ; velocette }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; team ; velocette } }', 'tointer': 'select the rows whose team record fuzzily matches to velocette . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'team', 'velocette'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose team record fuzzily matches to velocette .', 'tostr': 'filter_eq { all_rows ; team ; velocette }'}, 'rider'], 'result': 'brian finch', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; team ; velocette } ; rider }'}, 'brian finch'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; team ; velocette } ; rider } ; brian finch }', 'tointer': 'the rider record of this unqiue row is brian finch .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; team ; velocette } } ; eq { hop { filter_eq { all_rows ; team ; velocette } ; rider } ; brian finch } } = true', 'tointer': 'select the rows whose team record fuzzily matches to velocette . there is only one such row in the table . the rider record of this unqiue row is brian finch .'} | and { only { filter_eq { all_rows ; team ; velocette } } ; eq { hop { filter_eq { all_rows ; team ; velocette } ; rider } ; brian finch } } = true | select the rows whose team record fuzzily matches to velocette . there is only one such row in the table . the rider record of this unqiue row is brian finch . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'team_7': 7, 'velocette_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'rider_9': 9, 'brian finch_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'team_7': 'team', 'velocette_8': 'velocette', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'rider_9': 'rider', 'brian finch_10': 'brian finch'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'team_7': [0], 'velocette_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'rider_9': [2], 'brian finch_10': [3]} | ['rank', 'rider', 'team', 'speed', 'time'] | [['1', 'frank whiteway', 'suzuki', '89.94 mph', '2:05.52.0'], ['2', 'gordon pantall', 'triumph', '88.90 mph', '2:07.20.0'], ['3', 'ray knight', 'triumph', '88.89 mph', '2:07.20.4'], ['4', 'rbaylie', 'triumph', '87.58 mph', '2:09.15.0'], ['5', 'graham penny', 'triumph', '86.70 mph', '2:10.34.4'], ['6', 'jwade', 'suzuki', '85.31 mph', '2:12.42.0'], ['7', 'brian finch', 'velocette', '83.86 mph', '2:14.59.0']] |
swiss locomotive and machine works | https://en.wikipedia.org/wiki/Swiss_Locomotive_and_Machine_Works | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1562368-2.html.csv | comparative | the locomotive nicknamed enid was built before the locamotive nicknamed snowden . | {'row_1': '2', 'row_2': '4', 'col': '1', 'col_other': '7', 'relation': 'less', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'less', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'notes', 'enid'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose notes record fuzzily matches to enid .', 'tostr': 'filter_eq { all_rows ; notes ; enid }'}, 'built'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; notes ; enid } ; built }', 'tointer': 'select the rows whose notes record fuzzily matches to enid . take the built record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'notes', 'snowdon'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose notes record fuzzily matches to snowdon .', 'tostr': 'filter_eq { all_rows ; notes ; snowdon }'}, 'built'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; notes ; snowdon } ; built }', 'tointer': 'select the rows whose notes record fuzzily matches to snowdon . take the built record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; notes ; enid } ; built } ; hop { filter_eq { all_rows ; notes ; snowdon } ; built } } = true', 'tointer': 'select the rows whose notes record fuzzily matches to enid . take the built record of this row . select the rows whose notes record fuzzily matches to snowdon . take the built record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; notes ; enid } ; built } ; hop { filter_eq { all_rows ; notes ; snowdon } ; built } } = true | select the rows whose notes record fuzzily matches to enid . take the built record of this row . select the rows whose notes record fuzzily matches to snowdon . take the built 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, 'notes_7': 7, 'enid_8': 8, 'built_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'notes_11': 11, 'snowdon_12': 12, 'built_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', 'notes_7': 'notes', 'enid_8': 'enid', 'built_9': 'built', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'notes_11': 'notes', 'snowdon_12': 'snowdon', 'built_13': 'built'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'notes_7': [0], 'enid_8': [0], 'built_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'notes_11': [1], 'snowdon_12': [1], 'built_13': [3]} | ['built', 'number', 'type', 'slm number', 'wheel arrangement', 'location', 'notes'] | [['1895', '1', 'mountain railway rack steam locomotive', '923', '0 - 4 - 2 t', 'snowdon mountain railway', 'ladas'], ['1895', '2', 'mountain railway rack steam locomotive', '924', '0 - 4 - 2 t', 'snowdon mountain railway', 'enid'], ['1895', '3', 'mountain railway rack steam locomotive', '925', '0 - 4 - 2 t', 'snowdon mountain railway', 'wyddfa'], ['1896', '4', 'mountain railway rack steam locomotive', '988', '0 - 4 - 2 t', 'snowdon mountain railway', 'snowdon'], ['1896', '5', 'mountain railway rack steam locomotive', '989', '0 - 4 - 2 t', 'snowdon mountain railway', 'moel siabod'], ['1922', '6', 'mountain railway rack steam locomotive', '2838', '0 - 4 - 2 t', 'snowdon mountain railway', 'padarn'], ['1923', '7', 'mountain railway rack steam locomotive', '2869', '0 - 4 - 2 t', 'snowdon mountain railway', 'ralph'], ['1923', '8', 'mountain railway rack steam locomotive', '2870', '0 - 4 - 2 t', 'snowdon mountain railway', 'eryri']] |
2009 - 10 louisville cardinals men 's basketball team | https://en.wikipedia.org/wiki/2009%E2%80%9310_Louisville_Cardinals_men%27s_basketball_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25118909-3.html.csv | count | the height of 4 players on the 2009 - 10 louisville cardinals men 's basketball team is 6 - 4 . | {'scope': 'all', 'criterion': 'equal', 'value': '6-4', 'result': '4', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'height', '6-4'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose height record fuzzily matches to 6-4 .', 'tostr': 'filter_eq { all_rows ; height ; 6-4 }'}], 'result': '4', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; height ; 6-4 } }', 'tointer': 'select the rows whose height record fuzzily matches to 6-4 . the number of such rows is 4 .'}, '4'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; height ; 6-4 } } ; 4 } = true', 'tointer': 'select the rows whose height record fuzzily matches to 6-4 . the number of such rows is 4 .'} | eq { count { filter_eq { all_rows ; height ; 6-4 } } ; 4 } = true | select the rows whose height record fuzzily matches to 6-4 . the number of such rows is 4 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'height_5': 5, '6-4_6': 6, '4_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'height_5': 'height', '6-4_6': '6-4', '4_7': '4'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'height_5': [0], '6-4_6': [0], '4_7': [2]} | ['name', '-', 'position', 'height', 'weight', 'year', 'former school', 'hometown'] | [['chris brickley', '11', 'guard', '6 - 4', '175', 'senior', 'northeastern university', 'manchester , nh'], ['rakeem buckles', '4', 'forward', '6 - 8', '200', 'freshman', 'pace', 'miami , fl'], ['reginald delk', '12', 'guard', '6 - 4', '175', 'senior', 'mississippi state university', 'jackson , tn'], ['george goode', '22', 'guard', '6 - 8', '205', 'sophomore', 'raytown south', 'raytown , mo'], ['terrence jennings', '23', 'forward', '6 - 10', '225', 'sophomore', 'notre dame prep', 'sacramento , ca'], ['preston knowles', '2', 'guard', '6 - 1', '170', 'junior', 'george rogers clark', 'winchester , ky'], ['kyle kuric', '14', 'guard', '6 - 4', '175', 'sophomore', 'reitz memorial', 'evansville , in'], ['mike marra', '33', 'guard', '6 - 4', '190', 'freshman', 'northfield mt hermon', 'esmond , ri'], ['samardo samuels', '15', 'forward', '6 - 8', '240', 'sophomore', 'st benedict', 'trelawny , jamaica'], ['peyton siva', '3', 'guard', '5 - 10', '165', 'freshman', 'franklin', 'seattle , wa'], ['jerry smith', '34', 'guard', '6 - 1', '200', 'senior', 'east', 'wauwatosa , wi'], ['edgar sosa', '10', 'guard', '6 - 1', '200', 'senior', 'rice', 'bronx , ny'], ['jared swopshire', '21', 'forward', '6 - 7', '215', 'sophomore', 'img academy', 'st louis , mo'], ['stephan van treese', '44', 'forward', '6 - 8', '220', 'freshman', 'lawrence north', 'indianapolis , in']] |
tamil nadu legislative assembly | https://en.wikipedia.org/wiki/Tamil_Nadu_Legislative_Assembly | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-23512864-4.html.csv | count | for the tamil nadu legislative assembly , there were three times when the indian national congress was the winning party . | {'scope': 'all', 'criterion': 'equal', 'value': 'indian national congress', 'result': '3', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'winning party / coalition', 'indian national congress'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose winning party / coalition record fuzzily matches to indian national congress .', 'tostr': 'filter_eq { all_rows ; winning party / coalition ; indian national congress }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; winning party / coalition ; indian national congress } }', 'tointer': 'select the rows whose winning party / coalition record fuzzily matches to indian national congress . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; winning party / coalition ; indian national congress } } ; 3 } = true', 'tointer': 'select the rows whose winning party / coalition record fuzzily matches to indian national congress . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; winning party / coalition ; indian national congress } } ; 3 } = true | select the rows whose winning party / coalition record fuzzily matches to indian national congress . 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, 'winning party / coalition_5': 5, 'indian national congress_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', 'winning party / coalition_5': 'winning party / coalition', 'indian national congress_6': 'indian national congress', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'winning party / coalition_5': [0], 'indian national congress_6': [0], '3_7': [2]} | ['election year', 'assembly', 'winning party / coalition', 'chief minister', 'speaker'] | [['1952', 'first assembly', 'indian national congress', 'c rajagopalachari k kamaraj', 'j shivashanmugam pillai ( 2 )'], ['1957', 'second assembly', 'indian national congress', 'k kamaraj ( 2 )', 'n gopala menon u krishna rao'], ['1962', 'third assembly', 'indian national congress', 'k kamaraj ( 3 ) m bakthavatsalam', 's chellapandian'], ['1967', 'fourth assembly', 'dravida munnetra kazhagam', 'cn annadurai m karunanidhi', 's p adithanar pulavar k govindan'], ['1977', 'sixth assembly', 'anna dravida munnetra kazhagam', 'mg ramachandran', 'munu adhi'], ['1980', 'seventh assembly', 'anna dravida munnetra kazhagam', 'mg ramachandran ( 2 )', 'munu adhi ( 2 ) k rajaram'], ['1984', 'eighth assembly', 'anna dravida munnetra kazhagam', 'mg ramachandran ( 3 ) janaki ramachandran', 'k rajaram ( 2 ) p h pandian'], ['1989', 'ninth assembly', 'dravida munnetra kazhagam', 'm karunanidhi ( 3 )', 'm tamilkudimagan'], ['1991', 'tenth assembly', 'all india anna dravida munnetra kazhagam', 'j jayalalithaa', 'r muthiah'], ['1996', 'eleventh assembly', 'dravida munnetra kazhagam', 'm karunanidhi ( 4 )', 'p t r palanivel rajan'], ['2001', 'twelfth assembly', 'all india anna dravida munnetra kazhagam', 'o panneerselvam j jayalalithaa ( 2 )', 'k kalimuthu a arunachalam'], ['2006', 'thirteenth assembly', 'dravida munnetra kazhagam ( dpa )', 'm karunanidhi ( 5 )', 'r avudaiappan']] |
gambrinus liga | https://en.wikipedia.org/wiki/Gambrinus_Liga | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-2429942-2.html.csv | majority | sparta prague was the champion team in the majority of gambrinus liga seasons . | {'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'sparta prague', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'champions', 'sparta prague'], 'result': True, 'ind': 0, 'tointer': 'for the champions records of all rows , most of them fuzzily match to sparta prague .', 'tostr': 'most_eq { all_rows ; champions ; sparta prague } = true'} | most_eq { all_rows ; champions ; sparta prague } = true | for the champions records of all rows , most of them fuzzily match to sparta prague . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'champions_3': 3, 'sparta prague_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'champions_3': 'champions', 'sparta prague_4': 'sparta prague'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'champions_3': [0], 'sparta prague_4': [0]} | ['season', 'champions', 'runner - up', 'third place', 'top goalscorer', 'club'] | [['1993 - 94', 'sparta prague ( 1 )', 'slavia prague', 'baník ostrava', 'horst siegl ( 20 )', 'sparta prague'], ['1994 - 95', 'sparta prague ( 2 )', 'slavia prague', 'fc brno', 'radek drulák ( 15 )', 'drnovice'], ['1995 - 96', 'slavia prague ( 1 )', 'sigma olomouc', 'baumit jablonec', 'radek drulák ( 22 )', 'drnovice'], ['1996 - 97', 'sparta prague ( 3 )', 'slavia prague', 'baumit jablonec', 'horst siegl ( 19 )', 'sparta prague'], ['1997 - 98', 'sparta prague ( 4 )', 'slavia prague', 'sigma olomouc', 'horst siegl ( 13 )', 'sparta prague'], ['1998 - 99', 'sparta prague ( 5 )', 'teplice', 'slavia prague', 'horst siegl ( 18 )', 'sparta prague'], ['1999 - 00', 'sparta prague ( 6 )', 'slavia prague', 'drnovice', 'vratislav lokvenc ( 21 )', 'sparta prague'], ['2000 - 01', 'sparta prague ( 7 )', 'slavia prague', 'sigma olomouc', 'vítězslav tuma ( 15 )', 'drnovice'], ['2001 - 02', 'slovan liberec ( 1 )', 'sparta prague', 'viktoria žižkov', 'jiří štajner ( 15 )', 'slovan liberec'], ['2002 - 03', 'sparta prague ( 8 )', 'slavia prague', 'viktoria žižkov', 'jiří kowalík ( 16 )', '1 . fc synot'], ['2003 - 04', 'baník ostrava ( 1 )', 'sparta prague', 'sigma olomouc', 'marek heinz ( 19 )', 'baník ostrava'], ['2004 - 05', 'sparta prague ( 9 )', 'slavia prague', 'teplice', 'tomáš jun ( 14 )', 'sparta prague'], ['2005 - 06', 'slovan liberec ( 2 )', 'mladá boleslav', 'slavia prague', 'milan ivana ( 11 )', 'fc slovácko'], ['2006 - 07', 'sparta prague ( 10 )', 'slavia prague', 'mladá boleslav', 'luboš pecka ( 16 )', 'mladá boleslav'], ['2007 - 08', 'slavia prague ( 2 )', 'sparta prague', 'baník ostrava', 'václav svěrkoš ( 15 )', 'baník ostrava'], ['2008 - 09', 'slavia prague ( 3 )', 'sparta prague', 'slovan liberec', 'andrej kerić ( 15 )', 'slovan liberec'], ['2009 - 10', 'sparta prague ( 11 )', 'jablonec', 'baník ostrava', 'michal ordoš ( 12 )', 'sigma olomouc'], ['2010 - 11', 'viktoria plzeň ( 1 )', 'sparta prague', 'jablonec', 'david lafata ( 19 )', 'jablonec'], ['2011 - 12', 'slovan liberec ( 3 )', 'sparta prague', 'viktoria plzeň', 'david lafata ( 25 )', 'jablonec']] |
1982 denver broncos season | https://en.wikipedia.org/wiki/1982_Denver_Broncos_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17928444-1.html.csv | majority | the majority of games in the 1982 denver broncos season ended in losses for the broncos . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'fuzzily_match', 'value': 'l', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'result', 'l'], 'result': True, 'ind': 0, 'tointer': 'for the result records of all rows , most of them fuzzily match to l .', 'tostr': 'most_eq { all_rows ; result ; l } = true'} | most_eq { all_rows ; result ; l } = true | for the result records of all rows , most of them fuzzily match to l . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'result_3': 3, 'l_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'result_3': 'result', 'l_4': 'l'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'result_3': [0], 'l_4': [0]} | ['week', 'date', 'opponent', 'result', 'game site', 'record', 'attendance'] | [['1', 'september 12', 'san diego chargers', 'l 3 - 23', 'mile high stadium', '0 - 1', '73564'], ['2', 'september 19', 'san francisco 49ers', 'w 24 - 21', 'mile high stadium', '1 - 1', '73899'], ['10', 'november 21', 'seattle seahawks', 'l 10 - 17', 'mile high stadium', '1 - 2', '73996'], ['11', 'november 28', 'san diego chargers', 'l 20 - 30', 'jack murphy stadium', '1 - 3', '47629'], ['12', 'december 5', 'atlanta falcons', 'l 27 - 34', 'mile high stadium', '1 - 4', '73984'], ['13', 'december 12', 'los angeles rams', 'w 27 - 24', 'anaheim stadium', '2 - 4', '48112'], ['14', 'december 19', 'kansas city chiefs', 'l 16 - 37', 'mile high stadium', '2 - 5', '74192'], ['15', 'december 26', 'los angeles raiders', 'l 10 - 27', 'los angeles memorial coliseum', '2 - 6', '44160'], ['16', 'january 2', 'seattle seahawks', 'l 11 - 13', 'kingdome', '2 - 7', '43145']] |
list of montreal canadiens draft picks | https://en.wikipedia.org/wiki/List_of_Montreal_Canadiens_draft_picks | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18259953-8.html.csv | unique | the player selected in the 6th round is the only one whose name was omitted . | {'scope': 'all', 'row': '6', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': '-', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'player', '-'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record is equal to - .', 'tostr': 'filter_eq { all_rows ; player ; - }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; player ; - } }', 'tointer': 'select the rows whose player record is equal to - . there is only one such row in the table .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'player', '-'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record is equal to - .', 'tostr': 'filter_eq { all_rows ; player ; - }'}, 'round'], 'result': '6', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; - } ; round }'}, '6'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; player ; - } ; round } ; 6 }', 'tointer': 'the round record of this unqiue row is 6 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; player ; - } } ; eq { hop { filter_eq { all_rows ; player ; - } ; round } ; 6 } } = true', 'tointer': 'select the rows whose player record is equal to - . there is only one such row in the table . the round record of this unqiue row is 6 .'} | and { only { filter_eq { all_rows ; player ; - } } ; eq { hop { filter_eq { all_rows ; player ; - } ; round } ; 6 } } = true | select the rows whose player record is equal to - . there is only one such row in the table . the round record of this unqiue row is 6 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_eq_0': 0, 'all_rows_6': 6, 'player_7': 7, '-_8': 8, 'eq_3': 3, 'num_hop_2': 2, 'round_9': 9, '6_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_eq_0': 'filter_eq', 'all_rows_6': 'all_rows', 'player_7': 'player', '-_8': '-', 'eq_3': 'eq', 'num_hop_2': 'num_hop', 'round_9': 'round', '6_10': '6'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_eq_0': [1, 2], 'all_rows_6': [0], 'player_7': [0], '-_8': [0], 'eq_3': [4], 'num_hop_2': [3], 'round_9': [2], '6_10': [3]} | ['round', 'player', 'position', 'nationality', 'college / junior / club team ( league )'] | [['1', 'nathan beaulieu', 'defence', 'canada', 'saint john sea dogs ( qmjhl )'], ['4', 'josiah didier', 'defence', 'canada', 'cedar rapids roughriders ( ushl )'], ['4', 'olivier archambault', 'left wing', 'canada', "val d'or foreurs ( qmjhl )"], ['4', 'magnus nygren', 'defence', 'sweden', 'fã ¤ rjestads bk ( elitserien )'], ['5', 'darren dietz', 'defence', 'canada', 'saskatoon blades ( whl )'], ['6', '-', 'forward', 'czech republic', 'hc sparta praha ( czech extraliga )'], ['7', 'colin sullivan', 'defence', 'united states', 'avon old farms hs ( ushs )']] |
newington college | https://en.wikipedia.org/wiki/Newington_College | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1839872-3.html.csv | majority | all of the newington college employees have won the medal of the order of australia honour . | {'scope': 'all', 'col': '4', 'most_or_all': 'all', 'criterion': 'equal', 'value': 'medal of the order of australia', 'subset': None} | {'func': 'all_str_eq', 'args': ['all_rows', 'honour', 'medal of the order of australia'], 'result': True, 'ind': 0, 'tointer': 'for the honour records of all rows , all of them fuzzily match to medal of the order of australia .', 'tostr': 'all_eq { all_rows ; honour ; medal of the order of australia } = true'} | all_eq { all_rows ; honour ; medal of the order of australia } = true | for the honour records of all rows , all of them fuzzily match to medal of the order of australia . | 1 | 1 | {'all_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'honour_3': 3, 'medal of the order of australia_4': 4} | {'all_str_eq_0': 'all_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'honour_3': 'honour', 'medal of the order of australia_4': 'medal of the order of australia'} | {'all_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'honour_3': [0], 'medal of the order of australia_4': [0]} | ['name', 'employed', 'position held', 'honour', 'citation'] | [['davis , phillip harris ( phil )', '1951 - 2000', 'mathematics & prefect master', 'medal of the order of australia', "it 's an honour"], ['morgan , michael dennis', '1981 - 2001', 'physical education ist viii coach', 'medal of the order of australia', "it 's an honour"], ['swain , elizabeth anne ( liz )', '1973 - 1995', 'director of music & chapel choir', 'medal of the order of australia', "it 's an honour"], ['swain , peter leonard', '1970 - 1996', 'chaplain & archivist', 'medal of the order of australia', "it 's an honour"], ['woosnam , clive thomas', '1970 - 2005', 'senior boarding master & registrar', 'medal of the order of australia', "it 's an honour"], ['zimmerman , roy alfred', '1966 - 1996', 'master - in - charge wyvern house', 'medal of the order of australia', "it 's an honour"]] |
primera división de fútbol profesional apertura 2008 | https://en.wikipedia.org/wiki/Primera_Divisi%C3%B3n_de_F%C3%BAtbol_Profesional_Apertura_2008 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18522916-5.html.csv | superlative | mauricio cienfuegos had the earliest date of vacancy in the primera division de futbol . | {'scope': 'all', 'col_superlative': '4', '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', 'date of vacancy'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; date of vacancy }'}, 'outgoing manager'], 'result': 'mauricio cienfuegos', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; date of vacancy } ; outgoing manager }'}, 'mauricio cienfuegos'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; date of vacancy } ; outgoing manager } ; mauricio cienfuegos } = true', 'tointer': 'select the row whose date of vacancy record of all rows is minimum . the outgoing manager record of this row is mauricio cienfuegos .'} | eq { hop { argmin { all_rows ; date of vacancy } ; outgoing manager } ; mauricio cienfuegos } = true | select the row whose date of vacancy record of all rows is minimum . the outgoing manager record of this row is mauricio cienfuegos . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'date of vacancy_5': 5, 'outgoing manager_6': 6, 'mauricio cienfuegos_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'date of vacancy_5': 'date of vacancy', 'outgoing manager_6': 'outgoing manager', 'mauricio cienfuegos_7': 'mauricio cienfuegos'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'date of vacancy_5': [0], 'outgoing manager_6': [1], 'mauricio cienfuegos_7': [2]} | ['team', 'outgoing manager', 'manner of departure', 'date of vacancy', 'replaced by', 'date of appointment', 'position in table'] | [['nejapa', 'mauricio cienfuegos', 'mutual consent', '14 august 2008', 'daniel uberti', '5 september 2008', '10th'], ['firpo', 'gerardo reinoso', 'sacked', '25 august 2008', 'oscar benitez', '2 september 2008', '7th'], ['balboa', 'gustavo de simone', 'sacked', '30 august 2008', 'roberto gamarra', '5 september 2008', '10th'], ['alianza', 'pablo centrone', 'sacked', '14 september 2008', 'carlos jurado', '16 september 2008', '5th'], ['firpo', 'oscar benítez', 'sacked', '9 december 2008', 'agustín castillo', '23 december 2008', 'post - season ( 6th )'], ['águila', 'agustín castillo', 'sacked', '15 december 2008', 'pablo centrone', '24 december 2008', 'post - season ( semifinals )'], ['fas', 'nelson ancheta', 'sacked', '27 december 2008', 'roberto gamarra', '1 january 2009', 'post - season ( semifinals )'], ['nejapa', 'daniel uberti', 'sacked', '29 december 2008', 'nelson ancheta', '29 december 2008', 'post - season ( 10th )'], ['balboa', 'roberto gamarra', 'mutual consent', '1 january 2009', 'carlos de toro', '16 january 2009', 'post - season ( 7th )'], ['independiente', 'jorge abrego', 'sacked', 'december 2008', 'ramón sánchez', 'december 2009', 'post - season ( 8th )']] |
australian national bl class | https://en.wikipedia.org/wiki/Australian_National_BL_class | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11373937-1.html.csv | majority | the majority of australian national bl class locomotives have a pacific national blue & yellow livery . | {'scope': 'all', 'col': '5', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'pacific national blue & yellow', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'livery', 'pacific national blue & yellow'], 'result': True, 'ind': 0, 'tointer': 'for the livery records of all rows , most of them fuzzily match to pacific national blue & yellow .', 'tostr': 'most_eq { all_rows ; livery ; pacific national blue & yellow } = true'} | most_eq { all_rows ; livery ; pacific national blue & yellow } = true | for the livery records of all rows , most of them fuzzily match to pacific national blue & yellow . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'livery_3': 3, 'pacific national blue & yellow_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'livery_3': 'livery', 'pacific national blue & yellow_4': 'pacific national blue & yellow'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'livery_3': [0], 'pacific national blue & yellow_4': [0]} | ['locomotive', 'serial no', 'entered service', 'gauge', 'livery'] | [['bl26', '83 - 1010', 'march 1983', 'standard', 'pacific national blue & yellow'], ['bl27', '83 - 1011', 'august 1983', 'standard', 'pacific national blue & yellow'], ['bl28', '83 - 1012', 'september 1983', 'standard', 'pacific national blue & yellow'], ['bl29', '83 - 1013', 'october 1983', 'broad', 'pacific national blue & yellow'], ['bl30', '83 - 1014', 'december 1983', 'standard', 'pacific national blue & yellow'], ['bl31', '83 - 1015', 'november 1983', 'standard', 'pacific national blue & yellow'], ['bl32', '83 - 1016', 'february 1984', 'broad', 'national rail orange & grey'], ['bl33', '83 - 1017', 'april 1984', 'standard', 'pacific national blue & yellow'], ['bl34', '83 - 1018', 'june 1984', 'broad', 'pacific national blue & yellow'], ['bl35', '83 - 1019', 'july 1984', 'standard', 'pacific national blue & yellow']] |
history of test cricket from 1901 to 1914 | https://en.wikipedia.org/wiki/History_of_Test_cricket_from_1901_to_1914 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1598207-2.html.csv | count | on two different sets of dates , the result was a draw . | {'scope': 'all', 'criterion': 'equal', 'value': 'draw', 'result': '2', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'result', 'draw'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose result record fuzzily matches to draw .', 'tostr': 'filter_eq { all_rows ; result ; draw }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; result ; draw } }', 'tointer': 'select the rows whose result record fuzzily matches to draw . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; result ; draw } } ; 2 } = true', 'tointer': 'select the rows whose result record fuzzily matches to draw . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; result ; draw } } ; 2 } = true | select the rows whose result record fuzzily matches to draw . 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, 'result_5': 5, 'draw_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', 'result_5': 'result', 'draw_6': 'draw', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'result_5': [0], 'draw_6': [0], '2_7': [2]} | ['date', 'home captain', 'away captain', 'venue', 'result'] | [['29 , 3031 may 1902', 'archie maclaren', 'joe darling', 'edgbaston', 'draw'], ['12 , 13 , 14 jun 1902', 'archie maclaren', 'joe darling', "lord 's", 'draw'], ['3 , 4 , 5 jul 1902', 'archie maclaren', 'joe darling', 'bramall lane', 'aus by 143 runs'], ['24 , 25 , 26 jul 1902', 'archie maclaren', 'joe darling', 'old trafford', 'aus by 3 runs'], ['11 , 12 , 13 aug 1902', 'archie maclaren', 'joe darling', 'oval', 'eng by 1 wkt']] |
1907 michigan wolverines football team | https://en.wikipedia.org/wiki/1907_Michigan_Wolverines_football_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25724294-2.html.csv | aggregation | the players on the 1907 michigan wolverines football team averaged 2.29 touchdowns each . | {'scope': 'all', 'col': '2', 'type': 'average', 'result': '2.29', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'touchdowns'], 'result': '2.29', 'ind': 0, 'tostr': 'avg { all_rows ; touchdowns }'}, '2.29'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; touchdowns } ; 2.29 } = true', 'tointer': 'the average of the touchdowns record of all rows is 2.29 .'} | round_eq { avg { all_rows ; touchdowns } ; 2.29 } = true | the average of the touchdowns record of all rows is 2.29 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'touchdowns_4': 4, '2.29_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'touchdowns_4': 'touchdowns', '2.29_5': '2.29'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'touchdowns_4': [0], '2.29_5': [1]} | ['player', 'touchdowns', 'extra points', 'field goals', 'points'] | [['paul magoffin', '7', '0', '0', '35'], ['walter rheinschild', '5', '0', '0', '25'], ['octy graham', '0', '7', '4', '24'], ['jack loell', '3', '0', '0', '15'], ['prentiss douglass', '1', '0', '0', '5'], ['dave allerdice', '0', '3', '0', '3'], ['harry s hammond', '0', '1', '0', '1']] |
ucla bruins gymnastics | https://en.wikipedia.org/wiki/UCLA_Bruins_gymnastics | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17617256-1.html.csv | unique | the only person on the ucla bruins gymnastics team from orlando metro is olivia courtney . | {'scope': 'all', 'row': '3', 'col': '5', 'col_other': '1', 'criterion': 'equal', 'value': 'orlando metro', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'club', 'orlando metro'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose club record fuzzily matches to orlando metro .', 'tostr': 'filter_eq { all_rows ; club ; orlando metro }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; club ; orlando metro } }', 'tointer': 'select the rows whose club record fuzzily matches to orlando metro . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'club', 'orlando metro'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose club record fuzzily matches to orlando metro .', 'tostr': 'filter_eq { all_rows ; club ; orlando metro }'}, 'name'], 'result': 'olivia courtney', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; club ; orlando metro } ; name }'}, 'olivia courtney'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; club ; orlando metro } ; name } ; olivia courtney }', 'tointer': 'the name record of this unqiue row is olivia courtney .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; club ; orlando metro } } ; eq { hop { filter_eq { all_rows ; club ; orlando metro } ; name } ; olivia courtney } } = true', 'tointer': 'select the rows whose club record fuzzily matches to orlando metro . there is only one such row in the table . the name record of this unqiue row is olivia courtney .'} | and { only { filter_eq { all_rows ; club ; orlando metro } } ; eq { hop { filter_eq { all_rows ; club ; orlando metro } ; name } ; olivia courtney } } = true | select the rows whose club record fuzzily matches to orlando metro . there is only one such row in the table . the name record of this unqiue row is olivia courtney . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'club_7': 7, 'orlando metro_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'olivia courtney_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'club_7': 'club', 'orlando metro_8': 'orlando metro', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'olivia courtney_10': 'olivia courtney'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'club_7': [0], 'orlando metro_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'olivia courtney_10': [3]} | ['name', 'height', 'year', 'hometown', 'club'] | [['sadiqua bynum', '5 - 4', 'jr', 'berkeley , calif', 'head over heels athletic arts'], ['angi cipra', '5 - 2', 'fr', 'mesa , ariz', 'desert devils gymnastics'], ['olivia courtney', '5 - 2', 'jr', 'fairfax , va', 'orlando metro'], ['ellette craddock', '5 - 5', 'so', 'san francisco , calif', 'san mateo gymnastics center'], ['sophina dejesus', '5 - 1', 'so', 'temecula , calif', 'scega'], ['danusia francis', '5 - 4', 'so', 'kenilworth , england', 'heathrow gymnastics club'], ['mikaela gerber', '5 - 5', 'fr', 'cambridge , ontario', 'oakville gymnastics club'], ['mattie larson', '5 - 2', 'jr', 'los angeles , calif', 'all olympia gymnastics center'], ['christine peng - peng lee', '5 - 2', 'fr', 'richmond hill , ontario', 'sport seneca / oakville gymnastics club'], ['jessy macarthur', '5 - 5', 'fr', 'newhall , calif', 'gymjam'], ['dana mcdonald', '5 - 3', 'so', 'piedmont , calif', 'darrell boykins gymnastics'], ['hallie mossett', '5 - 2', 'fr', 'los angeles calif', 'west coast elite gymnastics'], ['asi peko', '5 - 6', 'fr', 'henderson , nev', "brown 's gymnastics"], ['samantha peszek', '5 - 1', 'jr', 'indianapolis , ind', "sharp 's gymnastics / deveau 's"], ['jennifer pinches', '5 - 4', 'fr', 'manchester , england', 'city of liverpool'], ['sydney sawa', '5 - 2', 'sr', 'calgary , alberta', 'calgary gymnastics center'], ['alex waller', '5 - 3', 'fr', 'valencia , calif', 'gymjam']] |
list of intel pentium dual - core microprocessors | https://en.wikipedia.org/wiki/List_of_Intel_Pentium_Dual-Core_microprocessors | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11602313-4.html.csv | majority | the majority of the processors had a frequency greater than 1.5 ghz . | {'scope': 'all', 'col': '3', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '1.5 ghz', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'frequency', '1.5 ghz'], 'result': True, 'ind': 0, 'tointer': 'for the frequency records of all rows , most of them are greater than 1.5 ghz .', 'tostr': 'most_greater { all_rows ; frequency ; 1.5 ghz } = true'} | most_greater { all_rows ; frequency ; 1.5 ghz } = true | for the frequency records of all rows , most of them are greater than 1.5 ghz . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'frequency_3': 3, '1.5 ghz_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'frequency_3': 'frequency', '1.5 ghz_4': '1.5 ghz'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'frequency_3': [0], '1.5 ghz_4': [0]} | ['model number', 'sspec number', 'frequency', 'l2 cache', 'fsb', 'mult', 'voltage', 'tdp', 'socket', 'release date', 'part number ( s )', 'release price ( usd )'] | [['pentium dual - core t2310', 'slaec ( m0 )', '1.47 ghz', '1 mb', '533 mt / s', '11', '1.075 - 1.175 v', '35 w', 'socket p', 'q4 2007', 'lf80537 ge0201 m', '90'], ['pentium dual - core t2330', 'sla4k ( m0 )', '1.6 ghz', '1 mb', '533 mt / s', '12', '1.075 - 1.175 v', '35 w', 'socket p', 'q4 2007', 'lf80537 ge0251 mn', 'oem'], ['pentium dual - core t2370', 'sla4j ( m0 )', '1.73 ghz', '1 mb', '533 mt / s', '13', '1.075 - 1.175 v', '35 w', 'socket p', 'q4 2007', 'lf80537 ge0301 m', 'oem'], ['pentium dual - core t2390', 'sla4h ( m0 )', '1.87 ghz', '1 mb', '533 mt / s', '14', '1.075 - 1.175 v', '35 w', 'socket p', 'q2 2008', 'lf80537 ge0361 m', 'oem'], ['pentium dual - core t2410', 'sla4 g ( m0 )', '2 ghz', '1 mb', '533 mt / s', '15', '1.075 - 1.175 v', '35 w', 'socket p', 'q3 2008', 'lf80537 ge0411 m', 'oem'], ['pentium dual - core t3200', 'slavg ( m0 )', '2 ghz', '1 mb', '667 mt / s', '12', '1.075 - 1.175 v', '35 w', 'socket p', 'q4 2008', 'lf80537 gf0411 m', 'oem']] |
marc girardelli | https://en.wikipedia.org/wiki/Marc_Girardelli | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1376129-1.html.csv | count | marc giradielli finished 1st in overall season rankings 5 times . | {'scope': 'all', 'criterion': 'equal', 'value': '1', 'result': '5', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'overall', '1'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose overall record is equal to 1 .', 'tostr': 'filter_eq { all_rows ; overall ; 1 }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; overall ; 1 } }', 'tointer': 'select the rows whose overall record is equal to 1 . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; overall ; 1 } } ; 5 } = true', 'tointer': 'select the rows whose overall record is equal to 1 . the number of such rows is 5 .'} | eq { count { filter_eq { all_rows ; overall ; 1 } } ; 5 } = true | select the rows whose overall record is equal to 1 . the number of such rows is 5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'overall_5': 5, '1_6': 6, '5_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'overall_5': 'overall', '1_6': '1', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'overall_5': [0], '1_6': [0], '5_7': [2]} | ['season', 'overall', 'slalom', 'giant slalom', 'super g', 'downhill', 'combined'] | [['1980', '84', '-', '32', 'not run', '-', '-'], ['1981', '26', '15', '23', 'not run', '-', '-'], ['1982', '6', '8', '3', 'not run', '-', '-'], ['1983', '4', '7', '6', 'not awarded', '-', '3'], ['1984', '3', '1', '4', 'not awarded', '-', '34'], ['1985', '1', '1', '1', 'not awarded', '19', '-'], ['1986', '1', '11', '5', '3', '4', '2'], ['1987', '2', '28', '5', '2', '10', '-'], ['1988', '5', '23', '13', '4', '7', '-'], ['1989', '1', '3', '5', '5', '1', '1'], ['1990', '25', '15', '12', '-', '-', '-'], ['1991', '1', '1', '3', '10', '28', '1'], ['1992', '3', '12', '7', '2', '13', '11'], ['1993', '1', '13', '3', '5', '6', '1'], ['1994', '2', '29', '19', '2', '1', '-'], ['1995', '4', '9', '18', '10', '24', '1'], ['1996', '22', '20', '23', '51', '47', '2'], ['1997', '115', '58', '49', '-', '-', '-']] |
list of schools in the auckland region | https://en.wikipedia.org/wiki/List_of_schools_in_the_Auckland_Region | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-12017602-20.html.csv | majority | most of the listed schools in the auckland region are in the area of papakura . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'papakura', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'area', 'papakura'], 'result': True, 'ind': 0, 'tointer': 'for the area records of all rows , most of them fuzzily match to papakura .', 'tostr': 'most_eq { all_rows ; area ; papakura } = true'} | most_eq { all_rows ; area ; papakura } = true | for the area records of all rows , most of them fuzzily match to papakura . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'area_3': 3, 'papakura_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'area_3': 'area', 'papakura_4': 'papakura'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'area_3': [0], 'papakura_4': [0]} | ['name', 'years', 'gender', 'area', 'authority', 'decile', 'roll'] | [['conifer grove school', '1 - 8', 'coed', 'takanini', 'state', '7', '526'], ['cosgrove school', '1 - 6', 'coed', 'papakura', 'state', '2', '606'], ['drury school', '1 - 8', 'coed', 'drury', 'state', '8', '423'], ['edmund hillary school', '1 - 8', 'coed', 'papakura', 'state', '1', '146'], ['hingaia peninsula school', '1 - 8', 'coed', 'karaka', 'state', '9', '91'], ['kelvin road school', '1 - 6', 'coed', 'papakura', 'state', '1', '459'], ['kereru park campus', '1 - 8', 'coed', 'papakura', 'state', '1', '76'], ['mansell senior school', '7 - 8', 'coed', 'papakura', 'state', '1', '149'], ['opaheke school', '1 - 8', 'coed', 'papakura', 'state', '5', '605'], ['papakura central school', '1 - 6', 'coed', 'papakura', 'state', '5', '347'], ['papakura normal school', '1 - 8', 'coed', 'papakura', 'state', '3', '651'], ['park estate school', '1 - 6', 'coed', 'papakura', 'state', '1', '112'], ['redhill school', '1 - 8', 'coed', 'papakura', 'state', '1', '199'], ['rosehill intermediate', '7 - 8', 'coed', 'papakura', 'state', '4', '368'], ["st mary 's catholic school", '1 - 8', 'coed', 'papakura', 'state integrated', '4', '281'], ['takanini school', '1 - 8', 'coed', 'takanini', 'state', '1', '442']] |
henry cejudo | https://en.wikipedia.org/wiki/Henry_Cejudo | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18931507-2.html.csv | majority | in four out of five of henry cejudo 's fights , the method he used to finish the fight was punches . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'punches', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'method', 'punches'], 'result': True, 'ind': 0, 'tointer': 'for the method records of all rows , most of them fuzzily match to punches .', 'tostr': 'most_eq { all_rows ; method ; punches } = true'} | most_eq { all_rows ; method ; punches } = true | for the method records of all rows , most of them fuzzily match to punches . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'method_3': 3, 'punches_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'method_3': 'method', 'punches_4': 'punches'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'method_3': [0], 'punches_4': [0]} | ['res', 'record', 'opponent', 'method', 'event', 'round', 'time', 'location'] | [['win', '5 - 0', 'ryan hollis', 'decision ( unanimous )', 'lfc 24 - legacy fighting championship 24', '3', '5:00', 'dallas , texas , united states'], ['win', '4 - 0', 'miguelito marti', 'tko ( punches )', 'gladiator challenge : american dream', '1', '1:43', 'lincoln , california , united states'], ['win', '3 - 0', 'anthony sessions', 'tko ( punches )', 'wff 10 : cejudo v sessions', '1', '4:23', 'chandler , arizona , united states'], ['win', '2 - 0', 'sean henry barnett', 'tko ( punches )', 'gladiator challenge : battleground', '1', '4:55', 'san jacinto , california , united states'], ['win', '1 - 0', 'michael poe', 'submission ( punches )', 'wff mma : pascua yaqui fights 4', '1', '1:25', 'tucson , arizona , united states']] |
1996 ansett australia cup | https://en.wikipedia.org/wiki/1996_Ansett_Australia_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16388091-1.html.csv | majority | most games of the 1996 ansett australia cup competition were played in the month of february . | {'scope': 'all', 'col': '7', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'february', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'date', 'february'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , most of them fuzzily match to february .', 'tostr': 'most_eq { all_rows ; date ; february } = true'} | most_eq { all_rows ; date ; february } = true | for the date records of all rows , most of them fuzzily match to february . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, 'february_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', 'february_4': 'february'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], 'february_4': [0]} | ['home team', 'home team score', 'away team', 'away team score', 'ground', 'crowd', 'date', 'time'] | [['adelaide', '18.16 ( 124 )', 'melbourne', '10.5 ( 65 )', 'football park', '24143', 'friday 23 february 1996', '8:00 pm'], ['hawthorn', '9.19 ( 73 )', 'st kilda', '19.13 ( 127 )', 'waverley park', '16061', 'saturday , 23 february 1996', '8:00 pm'], ['fremantle', '7.15 ( 57 )', 'west coast', '10.11 ( 71 )', 'marrara stadium', '9078', 'sunday , 25 february 1996', '7:05 pm'], ['fitzroy', '12.15 ( 87 )', 'footscray', '16.15 ( 111 )', 'waverley park', '4818', 'monday , 26 february 1996', '8:00 pm'], ['collingwood', '14.10 ( 94 )', 'richmond', '8.14 ( 62 )', 'waverley park', '13307', 'wednesday 25 february 1996', '8:00 pm'], ['sydney', '20.8 ( 128 )', 'north melbourne', '22.18 ( 150 )', 'bruce stadium', '9405', 'sunday , 2 march 1996', '2:00 pm'], ['carlton', '14.12 ( 96 )', 'essendon', '8.14 ( 62 )', 'waverley park', '23837', 'saturday , 2 march 1996', '8:00 pm'], ['brisbane', '14 . 25 ( 109 )', 'geelong', '9.9 ( 63 )', 'the gabba', '18325', 'monday , 4 march 1996', '7:00 pm']] |
salvatore bettiol | https://en.wikipedia.org/wiki/Salvatore_Bettiol | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15671752-1.html.csv | aggregation | salvatore bettiol 's total time in the year of 1987 was over 4:00:00 . | {'scope': 'subset', 'col': '6', 'type': 'sum', 'result': '4:27:46', 'subset': {'col': '1', 'criterion': 'equal', 'value': '1987'}} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'year', '1987'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; year ; 1987 }', 'tointer': 'select the rows whose year record is equal to 1987 .'}, 'notes'], 'result': '4:27:46', 'ind': 1, 'tostr': 'sum { filter_eq { all_rows ; year ; 1987 } ; notes }'}, '4:27:46'], 'result': True, 'ind': 2, 'tostr': 'round_eq { sum { filter_eq { all_rows ; year ; 1987 } ; notes } ; 4:27:46 } = true', 'tointer': 'select the rows whose year record is equal to 1987 . the sum of the notes record of these rows is 4:27:46 .'} | round_eq { sum { filter_eq { all_rows ; year ; 1987 } ; notes } ; 4:27:46 } = true | select the rows whose year record is equal to 1987 . the sum of the notes record of these rows is 4:27:46 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'sum_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'year_5': 5, '1987_6': 6, 'notes_7': 7, '4:27:46_8': 8} | {'eq_2': 'eq', 'result_3': 'true', 'sum_1': 'sum', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'year_5': 'year', '1987_6': '1987', 'notes_7': 'notes', '4:27:46_8': '4:27:46'} | {'eq_2': [3], 'result_3': [], 'sum_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'year_5': [0], '1987_6': [0], 'notes_7': [1], '4:27:46_8': [2]} | ['year', 'competition', 'venue', 'position', 'event', 'notes'] | [['1986', 'venice marathon', 'venice , italy', '1st', 'marathon', '2:18:44'], ['1987', 'world championships', 'rome , italy', '13th', 'marathon', '2:17:45'], ['1987', 'venice marathon', 'venice , italy', '1st', 'marathon', '2:10:01'], ['1990', 'european championships', 'split , fr yugoslavia', '4th', 'marathon', '2:17:45'], ['1991', 'world championships', 'tokyo , japan', '6th', 'marathon', '2:15:58'], ['1992', 'olympic games', 'barcelona , spain', '5th', 'marathon', '2:14:15'], ['1993', 'world championships', 'stuttgart , germany', 'n / a', 'marathon', 'dnf'], ['1996', 'olympic games', 'atlanta , united states', '20th', 'marathon', '2:17:27']] |
lexus ls ( xf40 ) | https://en.wikipedia.org/wiki/Lexus_LS_%28XF40%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-21530474-1.html.csv | unique | the l110f cvt drivetrain was only used 1 time in the hybrid model . | {'scope': 'all', 'row': '7', 'col': '5', 'col_other': '6', 'criterion': 'equal', 'value': 'l110f cvt', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'transmission', 'l110f cvt'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose transmission record fuzzily matches to l110f cvt .', 'tostr': 'filter_eq { all_rows ; transmission ; l110f cvt }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; transmission ; l110f cvt } }', 'tointer': 'select the rows whose transmission record fuzzily matches to l110f cvt . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'transmission', 'l110f cvt'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose transmission record fuzzily matches to l110f cvt .', 'tostr': 'filter_eq { all_rows ; transmission ; l110f cvt }'}, 'engine type'], 'result': '5.0 l hybrid v8', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; transmission ; l110f cvt } ; engine type }'}, '5.0 l hybrid v8'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; transmission ; l110f cvt } ; engine type } ; 5.0 l hybrid v8 }', 'tointer': 'the engine type record of this unqiue row is 5.0 l hybrid v8 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; transmission ; l110f cvt } } ; eq { hop { filter_eq { all_rows ; transmission ; l110f cvt } ; engine type } ; 5.0 l hybrid v8 } } = true', 'tointer': 'select the rows whose transmission record fuzzily matches to l110f cvt . there is only one such row in the table . the engine type record of this unqiue row is 5.0 l hybrid v8 .'} | and { only { filter_eq { all_rows ; transmission ; l110f cvt } } ; eq { hop { filter_eq { all_rows ; transmission ; l110f cvt } ; engine type } ; 5.0 l hybrid v8 } } = true | select the rows whose transmission record fuzzily matches to l110f cvt . there is only one such row in the table . the engine type record of this unqiue row is 5.0 l hybrid v8 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'transmission_7': 7, 'l110f cvt_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'engine type_9': 9, '5.0 l hybrid v8_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'transmission_7': 'transmission', 'l110f cvt_8': 'l110f cvt', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'engine type_9': 'engine type', '5.0 l hybrid v8_10': '5.0 l hybrid v8'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'transmission_7': [0], 'l110f cvt_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'engine type_9': [2], '5.0 l hybrid v8_10': [3]} | ['chassis code', 'model no', 'production years', 'drivetrain', 'transmission', 'engine type', 'engine code', 'region ( s )'] | [['usf40 ( japanese )', 'ls 460', '2006 -', 'rwd', '8 - speed aa80e at', '4.6 l petrol v8', '1ur - fse', 'n america , asia , europe , oceania'], ['usf40 ( japanese )', 'ls 460', '2006 -', 'rwd', '8 - speed aa80e at', '4.6 l petrol v8', '1ur - fe', 'middle east'], ['usf41', 'ls 460 l', '2006 -', 'rwd', '8 - speed aa80e at', '4.6 l petrol v8', '1ur - fse', 'n america , asia , europe'], ['usf41', 'ls 460 l', '2006 -', 'rwd', '8 - speed aa80e at', '4.6 l petrol v8', '1ur - fe', 'middle east'], ['usf45', 'ls 460 awd', '2007 -', 'awd', '8 - speed aa80e at', '4.6 l petrol v8', '1ur - fse', 'n america'], ['usf46', 'ls 460 l awd', '2007 -', 'awd', '8 - speed aa80e at', '4.6 l petrol v8', '1ur - fse', 'n america'], ['uvf45 ( japanese )', 'ls 600h', '2007 -', 'awd', 'l110f cvt', '5.0 l hybrid v8', '2ur - fse', 'asia , europe']] |
royal canadian mint numismatic coins ( 2000s ) | https://en.wikipedia.org/wiki/Royal_Canadian_Mint_numismatic_coins_%282000s%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-11916083-14.html.csv | aggregation | the average mintage for royal canadian mint numismatic coins ( 2000s ) was 22800 . | {'scope': 'all', 'col': '5', 'type': 'average', 'result': '22800', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'mintage'], 'result': '22800', 'ind': 0, 'tostr': 'avg { all_rows ; mintage }'}, '22800'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; mintage } ; 22800 } = true', 'tointer': 'the average of the mintage record of all rows is 22800 .'} | round_eq { avg { all_rows ; mintage } ; 22800 } = true | the average of the mintage record of all rows is 22800 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'mintage_4': 4, '22800_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'mintage_4': 'mintage', '22800_5': '22800'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'mintage_4': [0], '22800_5': [1]} | ['year', 'animal', 'artist', 'finish', 'mintage', 'issue price'] | [['2007', 'ruby - throated hummingbird', 'arnold nogy', 'specimen ( with selective colouring )', '25000', '24.95'], ['2007', 'red breasted nuthatch', 'arnold nogy', 'specimen ( with selective colouring )', '25000', '24.95'], ['2008', 'downy woodpecker', 'arnold nogy', 'specimen ( with selective colouring )', '25000', '24.95'], ['2008', 'northern cardinal', 'arnold nogy', 'specimen ( with selective colouring )', '25000', '24.95'], ['2010', 'american goldfinch', 'arnold nogy ( reverse ) , susanna blunt ( obverse )', 'specimen ( with selective colouring )', '14000', '24.95']] |
chad little | https://en.wikipedia.org/wiki/Chad_Little | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-1875157-2.html.csv | superlative | 1995 was the most succesful in terms of wins for chad little . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '4', 'value_mentioned': 'no', 'max_or_min': 'max', 'other_col': '1', 'subset': None} | {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'argmax', 'args': ['all_rows', 'wins'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; wins }'}, 'year'], 'result': '1995', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; wins } ; year }'}, '1995'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; wins } ; year } ; 1995 } = true', 'tointer': 'select the row whose wins record of all rows is maximum . the year record of this row is 1995 .'} | eq { hop { argmax { all_rows ; wins } ; year } ; 1995 } = true | select the row whose wins record of all rows is maximum . the year record of this row is 1995 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'num_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'wins_5': 5, 'year_6': 6, '1995_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'num_hop_1': 'num_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'wins_5': 'wins', 'year_6': 'year', '1995_7': '1995'} | {'eq_2': [3], 'result_3': [], 'num_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'wins_5': [0], 'year_6': [1], '1995_7': [2]} | ['year', 'starts', 'wins', 'top 5', 'top 10', 'poles', 'avg start', 'avg finish', 'winnings', 'position', 'team ( s )'] | [['1992', '1', '0', '0', '0', '0', '29.0', '29.0', '1400', '120th', '37 little racing'], ['1993', '12', '0', '2', '3', '0', '22.1', '22.6', '56508', '32nd', '23 mark rypien motorsports'], ['1994', '28', '0', '10', '14', '0', '21.0', '11.9', '234022', '3rd', '23 mark rypien motorsports'], ['1995', '26', '6', '11', '13', '0', '15.5', '14.5', '529056', '2nd', '23 mark rypien motorsports'], ['1996', '26', '0', '2', '7', '1', '15.3', '16.5', '317394', '5th', '23 mark rypien motorsports'], ['1998', '1', '0', '0', '0', '0', '6.0', '30.0', '4380', '108th', '9 roush racing'], ['2001', '33', '0', '2', '6', '0', '24.8', '16.0', '690321', '9th', '74 bace motorsports']] |
2004 belarusian premier league | https://en.wikipedia.org/wiki/2004_Belarusian_Premier_League | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14749151-1.html.csv | comparative | of the venues for the 2004 belarusian premier league , neman has a higher capacity than darida . | {'row_1': '7', 'row_2': '13', 'col': '4', 'col_other': '3', 'relation': 'greater', 'record_mentioned': 'no', 'diff_result': None} | {'func': 'greater', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'neman'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose venue record fuzzily matches to neman .', 'tostr': 'filter_eq { all_rows ; venue ; neman }'}, 'capacity'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; venue ; neman } ; capacity }', 'tointer': 'select the rows whose venue record fuzzily matches to neman . take the capacity record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'venue', 'darida'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose venue record fuzzily matches to darida .', 'tostr': 'filter_eq { all_rows ; venue ; darida }'}, 'capacity'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; venue ; darida } ; capacity }', 'tointer': 'select the rows whose venue record fuzzily matches to darida . take the capacity record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'greater { hop { filter_eq { all_rows ; venue ; neman } ; capacity } ; hop { filter_eq { all_rows ; venue ; darida } ; capacity } } = true', 'tointer': 'select the rows whose venue record fuzzily matches to neman . take the capacity record of this row . select the rows whose venue record fuzzily matches to darida . take the capacity record of this row . the first record is greater than the second record .'} | greater { hop { filter_eq { all_rows ; venue ; neman } ; capacity } ; hop { filter_eq { all_rows ; venue ; darida } ; capacity } } = true | select the rows whose venue record fuzzily matches to neman . take the capacity record of this row . select the rows whose venue record fuzzily matches to darida . take the capacity 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, 'venue_7': 7, 'neman_8': 8, 'capacity_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'venue_11': 11, 'darida_12': 12, 'capacity_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', 'venue_7': 'venue', 'neman_8': 'neman', 'capacity_9': 'capacity', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'venue_11': 'venue', 'darida_12': 'darida', 'capacity_13': 'capacity'} | {'greater_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'venue_7': [0], 'neman_8': [0], 'capacity_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'venue_11': [1], 'darida_12': [1], 'capacity_13': [3]} | ['team', 'location', 'venue', 'capacity', 'position in 2003'] | [['gomel', 'gomel', 'central , gomel', '11800', '1'], ['bate', 'borisov', 'city stadium , borisov', '5500', '2'], ['dinamo minsk', 'minsk', 'dinamo , minsk', '41040', '3'], ['torpedo - ska', 'minsk', 'torpedo , minsk', '5200', '4'], ['shakhtyor', 'soligorsk', 'stroitel', '5000', '5'], ['torpedo', 'zhodino', 'torpedo , zhodino', '3020', '6'], ['neman', 'grodno', 'neman', '6300', '7'], ['naftan', 'novopolotsk', 'atlant', '6500', '8'], ['dnepr - transmash', 'mogilev', 'spartak , mogilev', '11200', '9'], ['belshina', 'bobruisk', 'spartak , bobruisk', '3550', '10'], ['dinamo brest', 'brest', 'osk brestskiy', '10080', '11'], ['zvezda - va - bgu', 'minsk', 'traktor', '17600', '12'], ['darida', 'minsk raion', 'darida', '6000', '13'], ['slavia', 'mozyr', 'yunost', '5500', '14'], ['lokomotiv', 'vitebsk', 'central , vitebsk', '8300', 'first league , 1'], ['mtz - ripo', 'minsk', 'traktor', '17600', 'first league , 2']] |
patty schnyder | https://en.wikipedia.org/wiki/Patty_Schnyder | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1547798-4.html.csv | unique | the only time that patty schyder played on a clay surface was in 1998 . | {'scope': 'all', 'row': '1', 'col': '3', 'col_other': '1', 'criterion': 'fuzzily_match', 'value': 'clay', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'surface', 'clay'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose surface record fuzzily matches to clay .', 'tostr': 'filter_eq { all_rows ; surface ; clay }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; surface ; clay } }', 'tointer': 'select the rows whose surface record fuzzily matches to clay . 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', 'clay'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose surface record fuzzily matches to clay .', 'tostr': 'filter_eq { all_rows ; surface ; clay }'}, 'date'], 'result': '3 may 1998', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; surface ; clay } ; date }'}, '3 may 1998'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; surface ; clay } ; date } ; 3 may 1998 }', 'tointer': 'the date record of this unqiue row is 3 may 1998 .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; surface ; clay } } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; date } ; 3 may 1998 } } = true', 'tointer': 'select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the date record of this unqiue row is 3 may 1998 .'} | and { only { filter_eq { all_rows ; surface ; clay } } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; date } ; 3 may 1998 } } = true | select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the date record of this unqiue row is 3 may 1998 . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'surface_7': 7, 'clay_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'date_9': 9, '3 may 1998_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', 'clay_8': 'clay', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'date_9': 'date', '3 may 1998_10': '3 may 1998'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'surface_7': [0], 'clay_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'date_9': [2], '3 may 1998_10': [3]} | ['date', 'tournament', 'surface', 'partner', 'opponent in the final', 'score'] | [['3 may 1998', 'hamburg , germany', 'clay', 'barbara schett', 'martina hingis jana novotná', '7 - 6 , 3 - 6 , 6 - 3'], ['17 february 2002', 'antwerp , belgium', 'carpet', 'magdalena maleeva', 'nathalie dechy meilen tu', '6 - 3 , 6 - 7 , 6 - 3'], ['9 february 2003', 'paris , france', 'carpet', 'barbara schett', 'marion bartoli stéphanie cohen - aloro', '2 - 6 , 6 - 2 , 7 - 6'], ['15 february 2004', 'paris , france', 'carpet', 'barbara schett', 'silvia farina elia francesca schiavone', '6 - 3 , 6 - 2'], ['5 october 2008', 'stuttgart , germany', 'hard', 'anna - lena grönefeld', 'květa peschke rennae stubbs', '6 - 2 , 6 - 4']] |
forest hill railway station | https://en.wikipedia.org/wiki/Forest_Hill_railway_station | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1569516-1.html.csv | comparative | the forest hill railway station train whose destination is highbury & islington is on platform 1 while the train whose destination is west croydon is on platform 2 . | {'row_1': '1', 'row_2': '5', 'col': '1', 'col_other': '3', 'relation': 'not_equal', 'record_mentioned': 'yes', 'diff_result': None} | {'func': 'and', 'args': [{'func': 'not_eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'destination', 'highbury & islington'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose destination record fuzzily matches to highbury & islington .', 'tostr': 'filter_eq { all_rows ; destination ; highbury & islington }'}, 'platform'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; destination ; highbury & islington } ; platform }', 'tointer': 'select the rows whose destination record fuzzily matches to highbury & islington . take the platform record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'destination', 'west croydon'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose destination record fuzzily matches to west croydon .', 'tostr': 'filter_eq { all_rows ; destination ; west croydon }'}, 'platform'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; destination ; west croydon } ; platform }', 'tointer': 'select the rows whose destination record fuzzily matches to west croydon . take the platform record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'not_eq { hop { filter_eq { all_rows ; destination ; highbury & islington } ; platform } ; hop { filter_eq { all_rows ; destination ; west croydon } ; platform } }', 'tointer': 'select the rows whose destination record fuzzily matches to highbury & islington . take the platform record of this row . select the rows whose destination record fuzzily matches to west croydon . take the platform record of this row . the first record is not equal to the second record .'}, {'func': 'and', 'args': [{'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'destination', 'highbury & islington'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose destination record fuzzily matches to highbury & islington .', 'tostr': 'filter_eq { all_rows ; destination ; highbury & islington }'}, 'platform'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; destination ; highbury & islington } ; platform }', 'tointer': 'select the rows whose destination record fuzzily matches to highbury & islington . take the platform record of this row .'}, '1'], 'result': True, 'ind': 5, 'tostr': 'eq { hop { filter_eq { all_rows ; destination ; highbury & islington } ; platform } ; 1 }', 'tointer': 'the platform record of the first row is 1 .'}, {'func': 'eq', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'destination', 'west croydon'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose destination record fuzzily matches to west croydon .', 'tostr': 'filter_eq { all_rows ; destination ; west croydon }'}, 'platform'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; destination ; west croydon } ; platform }', 'tointer': 'select the rows whose destination record fuzzily matches to west croydon . take the platform record of this row .'}, '2'], 'result': True, 'ind': 6, 'tostr': 'eq { hop { filter_eq { all_rows ; destination ; west croydon } ; platform } ; 2 }', 'tointer': 'the platform record of the second row is 2 .'}], 'result': True, 'ind': 7, 'tostr': 'and { eq { hop { filter_eq { all_rows ; destination ; highbury & islington } ; platform } ; 1 } ; eq { hop { filter_eq { all_rows ; destination ; west croydon } ; platform } ; 2 } }', 'tointer': 'the platform record of the first row is 1 . the platform record of the second row is 2 .'}], 'result': True, 'ind': 8, 'tostr': 'and { not_eq { hop { filter_eq { all_rows ; destination ; highbury & islington } ; platform } ; hop { filter_eq { all_rows ; destination ; west croydon } ; platform } } ; and { eq { hop { filter_eq { all_rows ; destination ; highbury & islington } ; platform } ; 1 } ; eq { hop { filter_eq { all_rows ; destination ; west croydon } ; platform } ; 2 } } } = true', 'tointer': 'select the rows whose destination record fuzzily matches to highbury & islington . take the platform record of this row . select the rows whose destination record fuzzily matches to west croydon . take the platform record of this row . the first record is not equal to the second record . the platform record of the first row is 1 . the platform record of the second row is 2 .'} | and { not_eq { hop { filter_eq { all_rows ; destination ; highbury & islington } ; platform } ; hop { filter_eq { all_rows ; destination ; west croydon } ; platform } } ; and { eq { hop { filter_eq { all_rows ; destination ; highbury & islington } ; platform } ; 1 } ; eq { hop { filter_eq { all_rows ; destination ; west croydon } ; platform } ; 2 } } } = true | select the rows whose destination record fuzzily matches to highbury & islington . take the platform record of this row . select the rows whose destination record fuzzily matches to west croydon . take the platform record of this row . the first record is not equal to the second record . the platform record of the first row is 1 . the platform record of the second row is 2 . | 13 | 9 | {'and_8': 8, 'result_9': 9, 'not_eq_4': 4, 'num_hop_2': 2, 'filter_str_eq_0': 0, 'all_rows_10': 10, 'destination_11': 11, 'highbury & islington_12': 12, 'platform_13': 13, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_14': 14, 'destination_15': 15, 'west croydon_16': 16, 'platform_17': 17, 'and_7': 7, 'eq_5': 5, '1_18': 18, 'eq_6': 6, '2_19': 19} | {'and_8': 'and', 'result_9': 'true', 'not_eq_4': 'not_eq', 'num_hop_2': 'num_hop', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_10': 'all_rows', 'destination_11': 'destination', 'highbury & islington_12': 'highbury & islington', 'platform_13': 'platform', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_14': 'all_rows', 'destination_15': 'destination', 'west croydon_16': 'west croydon', 'platform_17': 'platform', 'and_7': 'and', 'eq_5': 'eq', '1_18': '1', 'eq_6': 'eq', '2_19': '2'} | {'and_8': [9], 'result_9': [], 'not_eq_4': [8], 'num_hop_2': [4, 5], 'filter_str_eq_0': [2], 'all_rows_10': [0], 'destination_11': [0], 'highbury & islington_12': [0], 'platform_13': [2], 'num_hop_3': [4, 6], 'filter_str_eq_1': [3], 'all_rows_14': [1], 'destination_15': [1], 'west croydon_16': [1], 'platform_17': [3], 'and_7': [8], 'eq_5': [7], '1_18': [5], 'eq_6': [7], '2_19': [6]} | ['platform', 'frequency ( per hour )', 'destination', 'service pattern', 'operator', 'line'] | [['1', '4', 'highbury & islington', 'all stations via shoreditch high street', 'london overground', 'east london'], ['1', '4', 'dalston junction', 'all stations via shoreditch high street', 'london overground', 'east london'], ['1', '4', 'london bridge', 'all stations', 'southern', 'metro'], ['2', '4', 'crystal palace', 'all stations', 'london overground', 'east london'], ['2', '4', 'west croydon', 'all stations', 'london overground', 'east london'], ['2', '2', 'london victoria ( mon - sat )', 'all stations via clapham junction', 'southern', 'metro'], ['2', '2', 'caterham ( mon - sat )', 'all stations via east croydon', 'southern', 'metro'], ['2', '2', 'west croydon ( peaks & sun only )', 'sydenham then fast to norwood junction', 'southern', 'metro'], ['2', '2', 'tattenham corner ( sun only )', 'all stations via east croydon', 'southern', 'metro']] |
2007 - 08 fis ski jumping world cup | https://en.wikipedia.org/wiki/2007%E2%80%9308_FIS_Ski_Jumping_World_Cup | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-14407512-23.html.csv | unique | gregor schlierenzauer was the only participant in the 2007 - 08 fis ski jumping world cup with austrian nationality . | {'scope': 'all', 'row': '4', 'col': '3', 'col_other': '2', 'criterion': 'equal', 'value': 'aut', 'subset': None} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'aut'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to aut .', 'tostr': 'filter_eq { all_rows ; nationality ; aut }'}], 'result': True, 'ind': 1, 'tostr': 'only { filter_eq { all_rows ; nationality ; aut } }', 'tointer': 'select the rows whose nationality record fuzzily matches to aut . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'nationality', 'aut'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose nationality record fuzzily matches to aut .', 'tostr': 'filter_eq { all_rows ; nationality ; aut }'}, 'name'], 'result': 'gregor schlierenzauer', 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; nationality ; aut } ; name }'}, 'gregor schlierenzauer'], 'result': True, 'ind': 3, 'tostr': 'eq { hop { filter_eq { all_rows ; nationality ; aut } ; name } ; gregor schlierenzauer }', 'tointer': 'the name record of this unqiue row is gregor schlierenzauer .'}], 'result': True, 'ind': 4, 'tostr': 'and { only { filter_eq { all_rows ; nationality ; aut } } ; eq { hop { filter_eq { all_rows ; nationality ; aut } ; name } ; gregor schlierenzauer } } = true', 'tointer': 'select the rows whose nationality record fuzzily matches to aut . there is only one such row in the table . the name record of this unqiue row is gregor schlierenzauer .'} | and { only { filter_eq { all_rows ; nationality ; aut } } ; eq { hop { filter_eq { all_rows ; nationality ; aut } ; name } ; gregor schlierenzauer } } = true | select the rows whose nationality record fuzzily matches to aut . there is only one such row in the table . the name record of this unqiue row is gregor schlierenzauer . | 6 | 5 | {'and_4': 4, 'result_5': 5, 'only_1': 1, 'filter_str_eq_0': 0, 'all_rows_6': 6, 'nationality_7': 7, 'aut_8': 8, 'str_eq_3': 3, 'str_hop_2': 2, 'name_9': 9, 'gregor schlierenzauer_10': 10} | {'and_4': 'and', 'result_5': 'true', 'only_1': 'only', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_6': 'all_rows', 'nationality_7': 'nationality', 'aut_8': 'aut', 'str_eq_3': 'str_eq', 'str_hop_2': 'str_hop', 'name_9': 'name', 'gregor schlierenzauer_10': 'gregor schlierenzauer'} | {'and_4': [5], 'result_5': [], 'only_1': [4], 'filter_str_eq_0': [1, 2], 'all_rows_6': [0], 'nationality_7': [0], 'aut_8': [0], 'str_eq_3': [4], 'str_hop_2': [3], 'name_9': [2], 'gregor schlierenzauer_10': [3]} | ['rank', 'name', 'nationality', '1st ( m )', '2nd ( m )', 'points', 'overall nt points', 'overall wc points ( rank )'] | [['1', 'janne ahonen', 'fin', '122.5', '126.0', '248.3', '378.7 ( 2 )', '1098 ( 3 )'], ['2', 'anders bardal', 'nor', '117.5', '128.0', '240.4', '373.0 ( 5 )', '788 ( 5 )'], ['3', 'tom hilde', 'nor', '121.5', '122.5', '237.2', '373.2 ( 4 )', '1027 ( 4 )'], ['4', 'gregor schlierenzauer', 'aut', '114.5', '129.0', '236.8', '374.4 ( 3 )', '1161 ( 2 )'], ['5', 'janne happonen', 'fin', '118.0', '125.5', '236.3', '378.9 ( 1 )', '554 ( 11 )']] |
2011 cfl draft | https://en.wikipedia.org/wiki/2011_CFL_Draft | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-30108930-6.html.csv | count | two of the players drafted played the rb position . | {'scope': 'all', 'criterion': 'equal', 'value': 'rb', 'result': '2', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'position', 'rb'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose position record fuzzily matches to rb .', 'tostr': 'filter_eq { all_rows ; position ; rb }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; position ; rb } }', 'tointer': 'select the rows whose position record fuzzily matches to rb . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; position ; rb } } ; 2 } = true', 'tointer': 'select the rows whose position record fuzzily matches to rb . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; position ; rb } } ; 2 } = true | select the rows whose position record fuzzily matches to rb . 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, 'position_5': 5, 'rb_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', 'position_5': 'position', 'rb_6': 'rb', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'position_5': [0], 'rb_6': [0], '2_7': [2]} | ['pick', 'cfl team', 'player', 'position', 'college'] | [['32', 'winnipeg blue bombers ( via edmonton via winnipeg )', 'carl volny', 'rb', 'central michigan'], ['33', 'hamilton tiger - cats ( via edmonton )', 'patrick jean - mary', 'lb', 'howard'], ['34', 'calgary stampeders ( via bc )', 'matt walter', 'rb', 'calgary'], ['35', 'toronto argonauts', 'gregory alexandre', 'dl', 'montrãal'], ['36', 'hamilton tiger - cats', 'tyrell francisco', 'te', 'weber state'], ['37', 'bc lions ( via calgary )', 'yannick sage', 'ol', 'sherbrooke'], ['38', 'toronto argonauts ( via saskatchewan )', 'julian feoli gudino', 'wr', 'laval']] |
gartell light railway | https://en.wikipedia.org/wiki/Gartell_Light_Railway | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1160735-1.html.csv | comparative | andrew was incorporated into the gartell light railway before amanda was . | {'row_1': '2', 'row_2': '1', 'col': '5', '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', 'name', 'andrew'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to andrew .', 'tostr': 'filter_eq { all_rows ; name ; andrew }'}, 'date'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; andrew } ; date }', 'tointer': 'select the rows whose name record fuzzily matches to andrew . take the date record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'amanda'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to amanda .', 'tostr': 'filter_eq { all_rows ; name ; amanda }'}, 'date'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; amanda } ; date }', 'tointer': 'select the rows whose name record fuzzily matches to amanda . take the date record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; name ; andrew } ; date } ; hop { filter_eq { all_rows ; name ; amanda } ; date } } = true', 'tointer': 'select the rows whose name record fuzzily matches to andrew . take the date record of this row . select the rows whose name record fuzzily matches to amanda . take the date record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; name ; andrew } ; date } ; hop { filter_eq { all_rows ; name ; amanda } ; date } } = true | select the rows whose name record fuzzily matches to andrew . take the date record of this row . select the rows whose name record fuzzily matches to amanda . take the date 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, 'name_7': 7, 'andrew_8': 8, 'date_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'name_11': 11, 'amanda_12': 12, 'date_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', 'name_7': 'name', 'andrew_8': 'andrew', 'date_9': 'date', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'name_11': 'name', 'amanda_12': 'amanda', 'date_13': 'date'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'name_7': [0], 'andrew_8': [0], 'date_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'name_11': [1], 'amanda_12': [1], 'date_13': [3]} | ['number', 'name', 'builder', 'type', 'date'] | [['1', 'amanda', 'gartell light railway', 'bo - bodh', '2000'], ['2', 'andrew', 'baguley - drewry', '4wdh', '1973'], ['5', 'alison', 'alan keef', '4wdh', '1993'], ['6', 'mr g', 'north dorset locomotive works', '0 - 4 - 2t', '1998'], ['9', 'jean', 'north dorset locomotive works', '0 - 4 - 0', '2008']] |
miss usa 1980 | https://en.wikipedia.org/wiki/Miss_USA_1980 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-15532342-2.html.csv | aggregation | the average swimsuit score of contestants in miss usa 1980 was 8.255 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '8.255', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'swimsuit'], 'result': '8.255', 'ind': 0, 'tostr': 'avg { all_rows ; swimsuit }'}, '8.255'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; swimsuit } ; 8.255 } = true', 'tointer': 'the average of the swimsuit record of all rows is 8.255 .'} | round_eq { avg { all_rows ; swimsuit } ; 8.255 } = true | the average of the swimsuit record of all rows is 8.255 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'swimsuit_4': 4, '8.255_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'swimsuit_4': 'swimsuit', '8.255_5': '8.255'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'swimsuit_4': [0], '8.255_5': [1]} | ['state', 'preliminary average', 'interview', 'swimsuit', 'evening gown', 'semifinal average'] | [['nebraska', '8.450 ( 5 )', '7.938 ( 10 )', '7.489 ( 11 )', '7.832 ( 8 )', '7.753 ( 10 )'], ['arizona', '8.317 ( 8 )', '8.950 ( 4 )', '8.670 ( 4 )', '8.701 ( 2 )', '8.774 ( 2 )'], ['south carolina', '9.086 ( 1 )', '9.082 ( 1 )', '9.097 ( 1 )', '9.567 ( 1 )', '9.249 ( 1 )'], ['minnesota', '8.083 ( 12 )', '7.858 ( 11 )', '7.031 ( 12 )', '7.518 ( 12 )', '7.469 ( 12 )'], ['texas', '8.503 ( 4 )', '7.417 ( 12 )', '8.008 ( 9 )', '7.703 ( 10 )', '7.709 ( 11 )'], ['florida', '8.924 ( 3 )', '9.029 ( 2 )', '8.953 ( 2 )', '8.313 ( 4 )', '8.765 ( 3 )'], ['alabama', '8.334 ( 7 )', '8.822 ( 5 )', '8.775 ( 3 )', '8.268 ( 5 )', '8.621 ( 4 )'], ['new mexico', '8.998 ( 2 )', '8.804 ( 6 )', '8.438 ( 5 )', '7.968 ( 7 )', '8.403 ( 6 )'], ['maryland', '8.344 ( 6 )', '8.498 ( 7 )', '8.167 ( 8 )', '8.176 ( 6 )', '8.280 ( 7 )'], ['new hampshire', '8.104 ( 11 )', '8.029 ( 9 )', '7.826 ( 10 )', '7.625 ( 11 )', '7.827 ( 9 )'], ['kentucky', '8.247 ( 9 )', '8.989 ( 3 )', '8.347 ( 6 )', '8.457 ( 3 )', '8.598 ( 5 )']] |
volleyball at the 2004 summer olympics - men 's team rosters | https://en.wikipedia.org/wiki/Volleyball_at_the_2004_Summer_Olympics_%E2%80%93_Men%27s_team_rosters | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15859432-12.html.csv | comparative | on the men 's volleyball team at the 2004 summer olympics , kevin barnett weighed 9 less than gabriel gardner . | {'row_1': '11', 'row_2': '12', 'col': '4', 'col_other': '1', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '9', 'bigger': 'row2'}} | {'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'kevin barnett'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name record fuzzily matches to kevin barnett .', 'tostr': 'filter_eq { all_rows ; name ; kevin barnett }'}, 'weight'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name ; kevin barnett } ; weight }', 'tointer': 'select the rows whose name record fuzzily matches to kevin barnett . take the weight record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name', 'gabriel gardner'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name record fuzzily matches to gabriel gardner .', 'tostr': 'filter_eq { all_rows ; name ; gabriel gardner }'}, 'weight'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name ; gabriel gardner } ; weight }', 'tointer': 'select the rows whose name record fuzzily matches to gabriel gardner . take the weight record of this row .'}], 'result': '-9', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; name ; kevin barnett } ; weight } ; hop { filter_eq { all_rows ; name ; gabriel gardner } ; weight } }'}, '-9'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; name ; kevin barnett } ; weight } ; hop { filter_eq { all_rows ; name ; gabriel gardner } ; weight } } ; -9 } = true', 'tointer': 'select the rows whose name record fuzzily matches to kevin barnett . take the weight record of this row . select the rows whose name record fuzzily matches to gabriel gardner . take the weight record of this row . the second record is 9 larger than the first record .'} | eq { diff { hop { filter_eq { all_rows ; name ; kevin barnett } ; weight } ; hop { filter_eq { all_rows ; name ; gabriel gardner } ; weight } } ; -9 } = true | select the rows whose name record fuzzily matches to kevin barnett . take the weight record of this row . select the rows whose name record fuzzily matches to gabriel gardner . take the weight record of this row . the second record is 9 larger than the first 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, 'name_8': 8, 'kevin barnett_9': 9, 'weight_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'name_12': 12, 'gabriel gardner_13': 13, 'weight_14': 14, '-9_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', 'name_8': 'name', 'kevin barnett_9': 'kevin barnett', 'weight_10': 'weight', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'name_12': 'name', 'gabriel gardner_13': 'gabriel gardner', 'weight_14': 'weight', '-9_15': '-9'} | {'eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'name_8': [0], 'kevin barnett_9': [0], 'weight_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'name_12': [1], 'gabriel gardner_13': [1], 'weight_14': [3], '-9_15': [5]} | ['name', 'date of birth', 'height', 'weight', 'spike', 'block'] | [['lloy ball', '17.02.1972', '203', '95', '351', '316'], ['erik sullivan', '09.08.1972', '193', '86', '340', '320'], ['phillip eatherton', '02.01.1974', '206', '101', '356', '335'], ['donald suxho', '21.02.1976', '196', '98', '337', '319'], ['william priddy', '01.10.1977', '196', '89', '353', '330'], ['ryan millar', '22.01.1978', '204', '98', '354', '326'], ['riley salmon', '02.07.1976', '197', '89', '345', '331'], ['brook billings', '30.04.1980', '196', '95', '351', '331'], ['thomas hoff', '09.06.1973', '198', '94', '353', '333'], ['clayton stanley', '20.01.1978', '205', '104', '357', '332'], ['kevin barnett', '14.05.1974', '198', '94', '353', '340'], ['gabriel gardner', '18.03.1976', '209', '103', '353', '335']] |
1995 - 96 winnipeg jets season | https://en.wikipedia.org/wiki/1995%E2%80%9396_Winnipeg_Jets_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14052745-12.html.csv | comparative | jason diog was picked in an earlier round in the 1995-96 winnipeg jets season than robert deciantis . | {'row_1': '3', 'row_2': '11', 'col': '1', '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', 'player', 'jason doig'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose player record fuzzily matches to jason doig .', 'tostr': 'filter_eq { all_rows ; player ; jason doig }'}, 'round'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; player ; jason doig } ; round }', 'tointer': 'select the rows whose player record fuzzily matches to jason doig . take the round record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'player', 'robert deciantis'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose player record fuzzily matches to robert deciantis .', 'tostr': 'filter_eq { all_rows ; player ; robert deciantis }'}, 'round'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; player ; robert deciantis } ; round }', 'tointer': 'select the rows whose player record fuzzily matches to robert deciantis . take the round record of this row .'}], 'result': True, 'ind': 4, 'tostr': 'less { hop { filter_eq { all_rows ; player ; jason doig } ; round } ; hop { filter_eq { all_rows ; player ; robert deciantis } ; round } } = true', 'tointer': 'select the rows whose player record fuzzily matches to jason doig . take the round record of this row . select the rows whose player record fuzzily matches to robert deciantis . take the round record of this row . the first record is less than the second record .'} | less { hop { filter_eq { all_rows ; player ; jason doig } ; round } ; hop { filter_eq { all_rows ; player ; robert deciantis } ; round } } = true | select the rows whose player record fuzzily matches to jason doig . take the round record of this row . select the rows whose player record fuzzily matches to robert deciantis . take the round 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, 'player_7': 7, 'jason doig_8': 8, 'round_9': 9, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_10': 10, 'player_11': 11, 'robert deciantis_12': 12, 'round_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', 'player_7': 'player', 'jason doig_8': 'jason doig', 'round_9': 'round', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_10': 'all_rows', 'player_11': 'player', 'robert deciantis_12': 'robert deciantis', 'round_13': 'round'} | {'less_4': [5], 'result_5': [], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_6': [0], 'player_7': [0], 'jason doig_8': [0], 'round_9': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_10': [1], 'player_11': [1], 'robert deciantis_12': [1], 'round_13': [3]} | ['round', 'player', 'position', 'nationality', 'college / junior / club team'] | [['1', 'shane doan', 'centre', 'canada', 'kamloops blazers ( whl )'], ['2', 'marc chouinard', 'centre', 'canada', 'beauport harfangs ( qmjhl )'], ['2', 'jason doig', 'defence', 'canada', 'laval titan collège français ( qmjhl )'], ['3', 'brad isbister', 'defence', 'canada', 'portland winter hawks ( whl )'], ['4', 'justin kurtz', 'defence', 'canada', 'brandon wheat kings ( whl )'], ['5', 'brian elder', 'goaltender', 'canada', 'brandon wheat kings ( whl )'], ['6', 'sylvain daigle', 'goaltender', 'canada', 'shawinigan cataractes ( qmjhl )'], ['7', 'paul traynor', 'defence', 'canada', 'kitchener rangers ( ohl )'], ['8', 'jaroslav obsut', 'right wing', 'slovakia', 'battlefords north stars ( sjhl )'], ['8', 'fredrik loven', 'defence', 'sweden', 'djurgardens if ( sel )'], ['9', 'robert deciantis', 'centre', 'canada', 'kitchener rangers ( ohl )']] |
2004 brazilian grand prix | https://en.wikipedia.org/wiki/2004_Brazilian_Grand_Prix | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1099518-2.html.csv | unique | in the 2004 brazilian grand prix , of the drivers that completed 71 laps , the only one that had a renault as a constructor was fernando alonso . | {'scope': 'subset', 'row': '4', 'col': '2', 'col_other': '1', 'criterion': 'equal', 'value': 'renault', 'subset': {'col': '3', 'criterion': 'equal', 'value': '71'}} | {'func': 'and', 'args': [{'func': 'only', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'laps', '71'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; laps ; 71 }', 'tointer': 'select the rows whose laps record is equal to 71 .'}, 'constructor', 'renault'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose laps record is equal to 71 . among these rows , select the rows whose constructor record fuzzily matches to renault .', 'tostr': 'filter_eq { filter_eq { all_rows ; laps ; 71 } ; constructor ; renault }'}], 'result': True, 'ind': 2, 'tostr': 'only { filter_eq { filter_eq { all_rows ; laps ; 71 } ; constructor ; renault } }', 'tointer': 'select the rows whose laps record is equal to 71 . among these rows , select the rows whose constructor record fuzzily matches to renault . there is only one such row in the table .'}, {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'filter_str_eq', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'laps', '71'], 'result': None, 'ind': 0, 'tostr': 'filter_eq { all_rows ; laps ; 71 }', 'tointer': 'select the rows whose laps record is equal to 71 .'}, 'constructor', 'renault'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose laps record is equal to 71 . among these rows , select the rows whose constructor record fuzzily matches to renault .', 'tostr': 'filter_eq { filter_eq { all_rows ; laps ; 71 } ; constructor ; renault }'}, 'driver'], 'result': 'fernando alonso', 'ind': 3, 'tostr': 'hop { filter_eq { filter_eq { all_rows ; laps ; 71 } ; constructor ; renault } ; driver }'}, 'fernando alonso'], 'result': True, 'ind': 4, 'tostr': 'eq { hop { filter_eq { filter_eq { all_rows ; laps ; 71 } ; constructor ; renault } ; driver } ; fernando alonso }', 'tointer': 'the driver record of this unqiue row is fernando alonso .'}], 'result': True, 'ind': 5, 'tostr': 'and { only { filter_eq { filter_eq { all_rows ; laps ; 71 } ; constructor ; renault } } ; eq { hop { filter_eq { filter_eq { all_rows ; laps ; 71 } ; constructor ; renault } ; driver } ; fernando alonso } } = true', 'tointer': 'select the rows whose laps record is equal to 71 . among these rows , select the rows whose constructor record fuzzily matches to renault . there is only one such row in the table . the driver record of this unqiue row is fernando alonso .'} | and { only { filter_eq { filter_eq { all_rows ; laps ; 71 } ; constructor ; renault } } ; eq { hop { filter_eq { filter_eq { all_rows ; laps ; 71 } ; constructor ; renault } ; driver } ; fernando alonso } } = true | select the rows whose laps record is equal to 71 . among these rows , select the rows whose constructor record fuzzily matches to renault . there is only one such row in the table . the driver record of this unqiue row is fernando alonso . | 8 | 6 | {'and_5': 5, 'result_6': 6, 'only_2': 2, 'filter_str_eq_1': 1, 'filter_eq_0': 0, 'all_rows_7': 7, 'laps_8': 8, '71_9': 9, 'constructor_10': 10, 'renault_11': 11, 'str_eq_4': 4, 'str_hop_3': 3, 'driver_12': 12, 'fernando alonso_13': 13} | {'and_5': 'and', 'result_6': 'true', 'only_2': 'only', 'filter_str_eq_1': 'filter_str_eq', 'filter_eq_0': 'filter_eq', 'all_rows_7': 'all_rows', 'laps_8': 'laps', '71_9': '71', 'constructor_10': 'constructor', 'renault_11': 'renault', 'str_eq_4': 'str_eq', 'str_hop_3': 'str_hop', 'driver_12': 'driver', 'fernando alonso_13': 'fernando alonso'} | {'and_5': [6], 'result_6': [], 'only_2': [5], 'filter_str_eq_1': [2, 3], 'filter_eq_0': [1], 'all_rows_7': [0], 'laps_8': [0], '71_9': [0], 'constructor_10': [1], 'renault_11': [1], 'str_eq_4': [5], 'str_hop_3': [4], 'driver_12': [3], 'fernando alonso_13': [4]} | ['driver', 'constructor', 'laps', 'time / retired', 'grid'] | [['juan pablo montoya', 'williams - bmw', '71', '1:28:01.451', '2'], ['kimi räikkönen', 'mclaren - mercedes', '71', '+ 1.022', '3'], ['rubens barrichello', 'ferrari', '71', '+ 24.099', '1'], ['fernando alonso', 'renault', '71', '+ 48.508', '8'], ['ralf schumacher', 'williams - bmw', '71', '+ 49.740', '7'], ['takuma sato', 'bar - honda', '71', '+ 50.248', '6'], ['michael schumacher', 'ferrari', '71', '+ 50.626', '18'], ['felipe massa', 'sauber - petronas', '71', '+ 1:02.310', '4'], ['giancarlo fisichella', 'sauber - petronas', '71', '+ 1:03.842', '10'], ['jacques villeneuve', 'renault', '70', '+ 1 lap', '13'], ['david coulthard', 'mclaren - mercedes', '70', '+ 1 lap', '12'], ['jarno trulli', 'toyota', '70', '+ 1 lap', '9'], ['ricardo zonta', 'toyota', '70', '+ 1 lap', '14'], ['christian klien', 'jaguar - cosworth', '69', '+ 2 lap', '15'], ['timo glock', 'jordan - ford', '69', '+ 2 lap', '17'], ['zsolt baumgartner', 'minardi - cosworth', '67', '+ 4 lap', '19'], ['gianmaria bruni', 'minardi - cosworth', '67', '+ 4 laps', '20'], ['mark webber', 'jaguar - cosworth', '23', 'collision', '11'], ['nick heidfeld', 'jordan - ford', '15', 'clutch', '16'], ['jenson button', 'bar - honda', '3', 'engine', '5']] |
madawaska county , new brunswick | https://en.wikipedia.org/wiki/Madawaska_County%2C_New_Brunswick | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-171250-2.html.csv | aggregation | the parishes of madawaska county , new brunswick , have a total population of 9617 . | {'scope': 'all', 'col': '4', 'type': 'sum', 'result': '9617', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'population'], 'result': '9617', 'ind': 0, 'tostr': 'sum { all_rows ; population }'}, '9617'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; population } ; 9617 } = true', 'tointer': 'the sum of the population record of all rows is 9617 .'} | round_eq { sum { all_rows ; population } ; 9617 } = true | the sum of the population record of all rows is 9617 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'population_4': 4, '9617_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'population_4': 'population', '9617_5': '9617'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'population_4': [0], '9617_5': [1]} | ['official name', 'status', 'area km 2', 'population', 'census ranking'] | [['saint - joseph', 'parish', '321.87', '1696', '1472 of 5008'], ['saint - jacques', 'parish', '298.82', '1607', '1531 of 5008'], ['sainte - anne', 'parish', '369.25', '1081', '1942 of 5008'], ['saint - léonard', 'parish', '343.95', '1039', '2011 of 5008'], ['saint - basile', 'parish', '129.73', '799', '2364 of 5008'], ['rivière - verte', 'parish', '715.58', '791', '2384 of 5008'], ['saint - françois', 'parish', '344.70', '754', '2458 of 5008'], ['lac - baker', 'parish', '57.38', '566', '2847 of 5008'], ['saint - hilaire', 'parish', '41.55', '531', '2928 of 5008'], ['notre - dame - de - lourdes', 'parish', '188.63', '284', '3729 of 5008'], ['clair', 'parish', '44.29', '282', '3737 of 5008'], ['baker brook', 'parish', '125.69', '177', '4103 of 5008'], ['madawaska', 'parish', '173.32', '10', '4889 of 5008']] |
ect mainline rail | https://en.wikipedia.org/wiki/ECT_Mainline_Rail | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15805928-1.html.csv | count | three of the rails have fragonset black as their livery . | {'scope': 'all', 'criterion': 'equal', 'value': 'fragonset black', 'result': '3', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'livery', 'fragonset black'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose livery record fuzzily matches to fragonset black .', 'tostr': 'filter_eq { all_rows ; livery ; fragonset black }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; livery ; fragonset black } }', 'tointer': 'select the rows whose livery record fuzzily matches to fragonset black . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; livery ; fragonset black } } ; 3 } = true', 'tointer': 'select the rows whose livery record fuzzily matches to fragonset black . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; livery ; fragonset black } } ; 3 } = true | select the rows whose livery record fuzzily matches to fragonset black . 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, 'livery_5': 5, 'fragonset black_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', 'livery_5': 'livery', 'fragonset black_6': 'fragonset black', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'livery_5': [0], 'fragonset black_6': [0], '3_7': [2]} | ['number', 'class', 'name', 'livery', 'notes'] | [['31128', '31', 'charybdis', 'fragonset black', 'now operated by nemesis rail'], ['31452', '31', 'minotaur', 'fragonset black', 'now operated by network rail'], ['31454', '31', 'the heart of wessex', 'intercity swallow', 'now operated by network rail'], ['31468', '31', 'hydra', 'fragonset black', 'now operated by network rail'], ['31601', '31', 'gauge o guild', 'wessex trains pink', 'now operated by network rail']] |
list of csi : ny characters | https://en.wikipedia.org/wiki/List_of_CSI%3A_NY_characters | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-11240028-1.html.csv | majority | most of the characters on csi : ny had a last appearance on today is life . | {'scope': 'all', 'col': '4', 'most_or_all': 'most', 'criterion': 'equal', 'value': 'today is life', 'subset': None} | {'func': 'most_str_eq', 'args': ['all_rows', 'last appearance', 'today is life'], 'result': True, 'ind': 0, 'tointer': 'for the last appearance records of all rows , most of them fuzzily match to today is life .', 'tostr': 'most_eq { all_rows ; last appearance ; today is life } = true'} | most_eq { all_rows ; last appearance ; today is life } = true | for the last appearance records of all rows , most of them fuzzily match to today is life . | 1 | 1 | {'most_str_eq_0': 0, 'result_1': 1, 'all_rows_2': 2, 'last appearance_3': 3, 'today is life_4': 4} | {'most_str_eq_0': 'most_str_eq', 'result_1': 'true', 'all_rows_2': 'all_rows', 'last appearance_3': 'last appearance', 'today is life_4': 'today is life'} | {'most_str_eq_0': [1], 'result_1': [], 'all_rows_2': [0], 'last appearance_3': [0], 'today is life_4': [0]} | ['character', 'portrayed by', 'first appearance', 'last appearance', 'duration', 'episodes'] | [['mac taylor csi detective', 'gary sinise', 'blink 1 , 2 , 3', 'today is life', '1.01 - 9.17', '197'], ['jo danville csi detective', 'sela ward', 'the 34th floor', 'today is life', '7.01 - 9.17', '57'], ['danny messer csi detective', 'carmine giovinazzo', 'blink 1', 'today is life', '1.01 - 9.17', '197'], ['lindsay monroe messer csi detective', 'anna belknap', 'zoo york', 'today is life', '2.03 - 9.17', '172 4'], ['dr sid hammerback chief medical examiner', 'robert joy', 'dancing with the fishes', 'today is life', '2.05 - 9.17', '168 4'], ['adam ross lab technician', 'a j buckley', 'bad beat', 'today is life', '2.08 - 9.17', '141 4'], ['dr sheldon hawkes csi', 'hill harper', 'blink 1', 'today is life', '1.01 - 9.17', '197'], ['don flack homicide detective', 'eddie cahill', 'blink', 'today is life', '1.01 - 9.17', '197'], ['aiden burn csi detective', 'vanessa ferlito', 'blink 1', 'heroes', '1.01 - 2.02 , 2.23', '26']] |
international cricket in 2008 - 09 | https://en.wikipedia.org/wiki/International_cricket_in_2008%E2%80%9309 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17324788-32.html.csv | superlative | the earliest game in international cricket in 2008-09 was on january 27th . | {'scope': 'all', 'col_superlative': '1', 'row_superlative': '1', 'value_mentioned': 'yes', 'max_or_min': 'min', 'other_col': 'n/a', 'subset': None} | {'func': 'eq', 'args': [{'func': 'min', 'args': ['all_rows', 'date'], 'result': '27 january', 'ind': 0, 'tostr': 'min { all_rows ; date }', 'tointer': 'the minimum date record of all rows is 27 january .'}, '27 january'], 'result': True, 'ind': 1, 'tostr': 'eq { min { all_rows ; date } ; 27 january } = true', 'tointer': 'the minimum date record of all rows is 27 january .'} | eq { min { all_rows ; date } ; 27 january } = true | the minimum date record of all rows is 27 january . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'min_0': 0, 'all_rows_3': 3, 'date_4': 4, '27 january_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'min_0': 'min', 'all_rows_3': 'all_rows', 'date_4': 'date', '27 january_5': '27 january'} | {'eq_1': [2], 'result_2': [], 'min_0': [1], 'all_rows_3': [0], 'date_4': [0], '27 january_5': [1]} | ['date', 'home captain', 'away captain', 'venue', 'result'] | [['27 january', 'steve tikolo', 'prosper utseya', 'mombasa sports club , mombasa', 'by 109 runs'], ['29 january', 'steve tikolo', 'prosper utseya', 'mombasa sports club , mombasa', 'by 151 runs'], ['31 january', 'steve tikolo', 'prosper utseya', 'nairobi gymkhana club , nairobi', 'by 4 wickets'], ['1 february', 'steve tikolo', 'prosper utseya', 'nairobi gymkhana club , nairobi', 'by 66 runs'], ['4 february', 'steve tikolo', 'prosper utseya', 'nairobi gymkhana club , nairobi', 'by 7 wickets']] |
david sigachev | https://en.wikipedia.org/wiki/David_Sigachev | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25421463-1.html.csv | aggregation | the average number of races per series for david sagachev is 7.6 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '7.6', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'races'], 'result': '7.6', 'ind': 0, 'tostr': 'avg { all_rows ; races }'}, '7.6'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; races } ; 7.6 } = true', 'tointer': 'the average of the races record of all rows is 7.6 .'} | round_eq { avg { all_rows ; races } ; 7.6 } = true | the average of the races record of all rows is 7.6 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'races_4': 4, '7.6_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'races_4': 'races', '7.6_5': '7.6'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'races_4': [0], '7.6_5': [1]} | ['season', 'series', 'team name', 'races', 'wins', 'points', 'final placing'] | [['2007', 'formula renault 2.0 nec', 'sl formula racing', '16', '0', '67', '21st'], ['2007', 'eurocup formula renault 2.0', 'sl formula racing', '2', '0', 'n / a', 'nc'], ['2009', 'porsche carrera cup germany', 'tolimit seyffarth motorsport', '9', '0', '38', '13th'], ['2009', 'porsche supercup', 'tolimit seyffarth motorsport', '2', '0', 'n / a', 'nc'], ['2010', 'porsche carrera cup germany', 'seyffarth motorsport', '9', '0', '42', '14th']] |
comparison of cad , cam and cae file viewers | https://en.wikipedia.org/wiki/Comparison_of_CAD%2C_CAM_and_CAE_file_viewers | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18913138-1.html.csv | count | there are two file viewers that do not support 3d . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'no', 'result': '2', 'col': '3', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', '3d support', 'no'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose 3d support record fuzzily matches to no .', 'tostr': 'filter_eq { all_rows ; 3d support ; no }'}], 'result': '2', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; 3d support ; no } }', 'tointer': 'select the rows whose 3d support record fuzzily matches to no . the number of such rows is 2 .'}, '2'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; 3d support ; no } } ; 2 } = true', 'tointer': 'select the rows whose 3d support record fuzzily matches to no . the number of such rows is 2 .'} | eq { count { filter_eq { all_rows ; 3d support ; no } } ; 2 } = true | select the rows whose 3d support record fuzzily matches to no . 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, '3d support_5': 5, 'no_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', '3d support_5': '3d support', 'no_6': 'no', '2_7': '2'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], '3d support_5': [0], 'no_6': [0], '2_7': [2]} | ['latest stable release', 'developer', '3d support', 'runs on posix style systems', 'runs on windows', 'user interface language ( s )'] | [['1.0', 'eethal inc', 'yes', 'no', 'yes', 'en'], ['6.5', 'data design system asa', 'yes', 'no', 'yes', 'en'], ['v 2.13.15 ( 04 / 2013 )', 'dxf viewer', 'yes', 'yes ( java )', 'yes ( java )', 'en , de'], ['3.46', 'autodwg', 'yes', 'no', 'yes', 'en'], ['14', 'advanced computer solutions', 'yes', 'no', 'yes', 'en'], ['1.1.5', 'andãor', 'yes', 'yes', 'yes', 'zh , de , en , fr , it , ja , es'], ['2018', 'trial systems ltd', 'yes', 'no', 'yes', 'en'], ['6.1', 'cadmatic oy', 'yes', 'no', 'yes', 'en'], ['2012', 'ironcad llc', 'yes', 'no', 'yes', 'en'], ['2009', 'progecad', 'no', 'no', 'yes', 'en'], ['2012 v1', 'varicad', 'yes', 'yes linux', 'yes', 'en , de , jp , pt , cn'], ['08.11 ( v8i )', 'bentley systems', 'yes', 'yes', 'yes', 'en'], ['3.9.0 ( 06 / 2013 )', 'open design alliance', 'yes', 'yes', 'yes', 'en'], ['1.7 ( 2009 )', 'sescoi', 'yes', 'no', 'yes', 'en , fr , de , es , it , jp , cn'], ['1.4 ( 2012 )', 'geometric limited', 'yes', 'no', 'yes', 'en'], ['1.0.2 ( 05 / 2012 )', 'librecad', 'no', 'yes', 'yes', 'cn , en , es , fr , de , hu , it , jp , ru , other']] |
1994 - 95 cleveland cavaliers season | https://en.wikipedia.org/wiki/1994%E2%80%9395_Cleveland_Cavaliers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-16188254-7.html.csv | count | chris mills had five leading scorer performances in the 1994 - 95 cleveland cavaliers season . | {'scope': 'all', 'criterion': 'fuzzily_match', 'value': 'chris mills', 'result': '5', 'col': '5', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'leading scorer', 'chris mills'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose leading scorer record fuzzily matches to chris mills .', 'tostr': 'filter_eq { all_rows ; leading scorer ; chris mills }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; leading scorer ; chris mills } }', 'tointer': 'select the rows whose leading scorer record fuzzily matches to chris mills . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; leading scorer ; chris mills } } ; 5 } = true', 'tointer': 'select the rows whose leading scorer record fuzzily matches to chris mills . the number of such rows is 5 .'} | eq { count { filter_eq { all_rows ; leading scorer ; chris mills } } ; 5 } = true | select the rows whose leading scorer record fuzzily matches to chris mills . the number of such rows is 5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_str_eq_0': 0, 'all_rows_4': 4, 'leading scorer_5': 5, 'chris mills_6': 6, '5_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_str_eq_0': 'filter_str_eq', 'all_rows_4': 'all_rows', 'leading scorer_5': 'leading scorer', 'chris mills_6': 'chris mills', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'leading scorer_5': [0], 'chris mills_6': [0], '5_7': [2]} | ['date', 'visitor', 'score', 'home', 'leading scorer', 'attendance', 'record'] | [['march 2', 'cleveland', '84 - 90', 'dallas', 'chris mills , 16 points', 'reunion arena 12194', '33 - 23'], ['march 4', 'new york', '89 - 76', 'cleveland', 'hot rod williams , 20 points', 'gund arena 20562', '33 - 24'], ['march 7', 'detroit', '81 - 89', 'cleveland', 'chris mills , 24 points', 'gund arena 20562', '34 - 24'], ['march 9', 'san antonio', '100 - 98', 'cleveland', 'terrell brandon , 24 points', 'gund arena 20562', '34 - 25'], ['march 10', 'cleveland', '76 - 99', 'chicago', 'tyrone hill , 13 points', 'united center 22362', '34 - 26'], ['march 12', 'cleveland', '92 - 72', 'philadelphia', '3 way tie , 14 points', 'corestates spectrum 10221', '35 - 26'], ['march 16', 'utah', '85 - 93', 'cleveland', 'bobby phills , 24 points', 'gund arena 20562', '36 - 26'], ['march 17', 'cleveland', '77 - 80', 'minnesota', 'mark price , 18 points', 'target center 14222', '36 - 27'], ['march 19', 'cleveland', '90 - 96', 'washington', 'mark price , 16 points', 'usair arena 17110', '36 - 28'], ['march 20', 'dallas', '102 - 100', 'cleveland', 'tyrone hill , 29 points', 'gund arena 20562', '36 - 29'], ['march 22', 'sacramento', '89 - 101', 'cleveland', 'mark price , 23 points', 'gund arena 20562', '37 - 29'], ['march 24', 'atlanta', '74 - 75', 'cleveland', 'tyrone hill , 24 points', 'gund arena 20562', '38 - 29'], ['march 25', 'cleveland', '97 - 105', 'charlotte', 'chris mills , 26 points', 'charlotte coliseum 23698', '38 - 30'], ['march 29', 'cleveland', '96 - 107', 'indiana', 'chris mills , 22 points', 'market square arena 16619', '38 - 31'], ['march 31', 'washington', '88 - 98', 'cleveland', 'chris mills , 24 points', 'gund arena 20562', '39 - 31']] |
2003 u.s. bank cleveland grand prix | https://en.wikipedia.org/wiki/2003_U.S._Bank_Cleveland_Grand_Prix | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18943126-1.html.csv | superlative | paul tracy had the fastest time for the 1st qualifier at the 2003 u.s. bank cleveland grand prix . | {'scope': 'all', 'col_superlative': '3', 'row_superlative': '2', 'value_mentioned': 'no', 'max_or_min': 'min', 'other_col': '1', 'subset': None} | {'func': 'str_eq', 'args': [{'func': 'str_hop', 'args': [{'func': 'argmin', 'args': ['all_rows', 'qual 1'], 'result': None, 'ind': 0, 'tostr': 'argmin { all_rows ; qual 1 }'}, 'name'], 'result': 'paul tracy', 'ind': 1, 'tostr': 'hop { argmin { all_rows ; qual 1 } ; name }'}, 'paul tracy'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmin { all_rows ; qual 1 } ; name } ; paul tracy } = true', 'tointer': 'select the row whose qual 1 record of all rows is minimum . the name record of this row is paul tracy .'} | eq { hop { argmin { all_rows ; qual 1 } ; name } ; paul tracy } = true | select the row whose qual 1 record of all rows is minimum . the name record of this row is paul tracy . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmin_0': 0, 'all_rows_4': 4, 'qual 1_5': 5, 'name_6': 6, 'paul tracy_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmin_0': 'argmin', 'all_rows_4': 'all_rows', 'qual 1_5': 'qual 1', 'name_6': 'name', 'paul tracy_7': 'paul tracy'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmin_0': [1], 'all_rows_4': [0], 'qual 1_5': [0], 'name_6': [1], 'paul tracy_7': [2]} | ['name', 'team', 'qual 1', 'qual 2', 'best'] | [['sébastien bourdais', 'newman / haas racing', '59.163', '58.014', '58.014'], ['paul tracy', "team player 's", '58.405', '1:01.294', '58.405'], ['patrick carpentier', "team player 's", '58.868', '58.449', '58.449'], ['oriol servià', 'patrick racing', '59.186', '58.502', '58.502'], ['bruno junqueira', 'newman / haas racing', '59.804', '58.506', '58.506'], ['michel jourdain , jr', 'team rahal', '59.223', '58.700', '58.700'], ['alex tagliani', 'rocketsports racing', '59.247', '58.718', '58.718'], ['mario domínguez', 'herdez competition', '59.535', '58.724', '58.724'], ['roberto moreno', 'herdez competition', '59.954', '58.845', '58.845'], ['jimmy vasser', 'american spirit team johansson', '59.382', '58.861', '58.861'], ['ryan hunter - reay', 'american spirit team johansson', '59.989', '59.073', '59.073'], ['mario haberfeld', 'mi - jack conquest racing', '1:00.333', '59.141', '59.141'], ['darren manning', 'walker racing', '59.776', '59.167', '59.167'], ['adrian fernández', 'fernández racing', '59.340', '59.306', '59.306'], ['rodolfo lavín', 'walker racing', '1:00.670', '59.531', '59.531'], ['tiago monteiro', 'fittipaldi - dingman racing', '1:00.003', '59.822', '59.822'], ['gualter salles', 'dale coyne racing', '1:01.778', '59.968', '59.968'], ['max papis', 'pk racing', '1:00.020', '1:00.080', '1:00.020'], ['geoff boss', 'dale coyne racing', '1:01.103', '1:01.525', '1:01.103']] |
united states house of representatives elections , 1864 | https://en.wikipedia.org/wiki/United_States_House_of_Representatives_elections%2C_1864 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1434834-3.html.csv | ordinal | in the us house of representatives at the time of the 1864 election , george h pendleton was the first representative from ohio to have been originally elected to the house . | {'row': '1', 'col': '4', 'order': '1', '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', 'first elected', '1'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; first elected ; 1 }'}, 'incumbent'], 'result': 'george h pendleton', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent }'}, 'george h pendleton'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; george h pendleton } = true', 'tointer': 'select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is george h pendleton .'} | eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; george h pendleton } = true | select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is george h pendleton . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'first elected_5': 5, '1_6': 6, 'incumbent_7': 7, 'george h pendleton_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', 'first elected_5': 'first elected', '1_6': '1', 'incumbent_7': 'incumbent', 'george h pendleton_8': 'george h pendleton'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'first elected_5': [0], '1_6': [0], 'incumbent_7': [1], 'george h pendleton_8': [2]} | ['district', 'incumbent', 'party', 'first elected', 'result'] | [['ohio 1', 'george h pendleton', 'democratic', '1856', 'retired republican gain'], ['ohio 2', 'alexander long', 'democratic', '1862', 'lost re - nomination republican gain'], ['ohio 3', 'robert c schenck', 'republican', '1862', 're - elected'], ['ohio 4', 'john f mckinney', 'democratic', '1862', 'lost re - election republican gain'], ['ohio 5', 'francis c le blond', 'democratic', '1862', 're - elected'], ['ohio 6', 'chilton a white', 'democratic', '1860', 'lost re - election republican gain'], ['ohio 7', 'samuel s cox', 'democratic', '1862', 'lost re - election republican gain'], ['ohio 8', 'william johnston', 'democratic', '1862', 'lost re - election republican gain'], ['ohio 9', 'warren p noble', 'democratic', '1860', 'lost re - election republican gain'], ['ohio 10', 'james m ashley', 'republican', '1862', 're - elected'], ['ohio 11', 'wells a hutchins', 'democratic', '1862', 'lost re - election republican gain'], ['ohio 12', 'william e finck', 'democratic', '1862', 're - elected'], ['ohio 13', "john o'neill", 'democratic', '1862', 'retired republican gain'], ['ohio 14', 'george bliss', 'democratic', '1862', 'lost re - election republican gain'], ['ohio 15', 'james r morris', 'democratic', '1862', 'lost re - election republican gain'], ['ohio 16', 'joseph w white', 'democratic', '1882', 'lost re - election republican gain'], ['ohio 17', 'ephraim r eckley', 'republican', '1862', 're - elected'], ['ohio 18', 'rufus p spalding', 'republican', '1862', 're - elected'], ['ohio 19', 'james a garfield', 'republican', '1862', 're - elected']] |
the whole thing 's started | https://en.wikipedia.org/wiki/The_Whole_Thing%27s_Started | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17071146-1.html.csv | aggregation | the median length of the 7 " single releases of the album the whole thing 's started , rounded to the nearest second , is 3:43 . | {'scope': 'all', 'col': '3', 'type': 'average', 'result': '3:43', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'length'], 'result': '3:43', 'ind': 0, 'tostr': 'avg { all_rows ; length }'}, '3:43'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; length } ; 3:43 } = true', 'tointer': 'the average of the length record of all rows is 3:43 .'} | round_eq { avg { all_rows ; length } ; 3:43 } = true | the average of the length record of all rows is 3:43 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'length_4': 4, '3:43_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'length_4': 'length', '3:43_5': '3:43'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'length_4': [0], '3:43_5': [1]} | ['date', 'tracks', 'length', 'label', 'catalog'] | [['1977', 'do what you do', '3:47', 'cbs', 'ba 222304'], ['1977', "it 's automatic", '2:57', 'cbs', 'ba 222304'], ['1977', "that 's how the whole thing started", '4:03', 'cbs', 'ba 222325'], ['1977', "there 's nothing i can do", '3:38', 'cbs', 'ba 222325'], ['1978', 'do it again', '3:35', 'columbia', 'c4 - 8217'], ['1978', 'empty pages', '4:20', 'columbia', 'c4 - 8217']] |
russia women 's national rugby union team | https://en.wikipedia.org/wiki/Russia_women%27s_national_rugby_union_team | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-13106281-1.html.csv | count | the russia women 's national rugby union team did n't lose a game in 5 different years . | {'scope': 'all', 'criterion': 'equal', 'value': '0', 'result': '5', 'col': '4', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_eq', 'args': ['all_rows', 'lost', '0'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose lost record is equal to 0 .', 'tostr': 'filter_eq { all_rows ; lost ; 0 }'}], 'result': '5', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; lost ; 0 } }', 'tointer': 'select the rows whose lost record is equal to 0 . the number of such rows is 5 .'}, '5'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; lost ; 0 } } ; 5 } = true', 'tointer': 'select the rows whose lost record is equal to 0 . the number of such rows is 5 .'} | eq { count { filter_eq { all_rows ; lost ; 0 } } ; 5 } = true | select the rows whose lost record is equal to 0 . the number of such rows is 5 . | 3 | 3 | {'eq_2': 2, 'result_3': 3, 'count_1': 1, 'filter_eq_0': 0, 'all_rows_4': 4, 'lost_5': 5, '0_6': 6, '5_7': 7} | {'eq_2': 'eq', 'result_3': 'true', 'count_1': 'count', 'filter_eq_0': 'filter_eq', 'all_rows_4': 'all_rows', 'lost_5': 'lost', '0_6': '0', '5_7': '5'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_eq_0': [1], 'all_rows_4': [0], 'lost_5': [0], '0_6': [0], '5_7': [2]} | ['first game', 'played', 'drawn', 'lost', 'percentage'] | [['2006', '3', '0', '0', '100.00 %'], ['2005', '2', '0', '0', '100.00 %'], ['1994', '2', '0', '2', '0.00 %'], ['2008', '3', '0', '0', '100.00 %'], ['2010', '1', '0', '0', '100.00 %'], ['1998', '4', '0', '4', '0.00 %'], ['1997', '4', '0', '1', '75.00 %'], ['1998', '4', '0', '3', '25.00 %'], ['2005', '3', '0', '0', '100.00 %'], ['1997', '2', '0', '2', '0.00 %'], ['1994', '4', '0', '4', '0.00 %'], ['1998', '1', '0', '1', '0.00 %'], ['1994', '39', '0', '21', '46.15 %']] |
katarina srebotnik | https://en.wikipedia.org/wiki/Katarina_Srebotnik | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-1729366-4.html.csv | majority | most of the tournaments took place in the first decade of the 2000 's . | {'scope': 'all', 'col': '2', 'most_or_all': 'most', 'criterion': 'greater_than', 'value': '2000', 'subset': None} | {'func': 'most_greater', 'args': ['all_rows', 'date', '2000'], 'result': True, 'ind': 0, 'tointer': 'for the date records of all rows , most of them are greater than 2000 .', 'tostr': 'most_greater { all_rows ; date ; 2000 } = true'} | most_greater { all_rows ; date ; 2000 } = true | for the date records of all rows , most of them are greater than 2000 . | 1 | 1 | {'most_greater_0': 0, 'result_1': 1, 'all_rows_2': 2, 'date_3': 3, '2000_4': 4} | {'most_greater_0': 'most_greater', 'result_1': 'true', 'all_rows_2': 'all_rows', 'date_3': 'date', '2000_4': '2000'} | {'most_greater_0': [1], 'result_1': [], 'all_rows_2': [0], 'date_3': [0], '2000_4': [0]} | ['outcome', 'date', 'tournament', 'surface', 'opponent in final', 'score in final'] | [['winner', 'april 11 , 1999', 'estoril , portugal', 'clay', 'rita kuti - kis', '6 - 3 , 6 - 1'], ['runner - up', 'february 24 , 2002', 'bogotá , colombia', 'clay', 'fabiola zuluaga', '1 - 6 , 4 - 6'], ['winner', 'march 3 , 2002', 'acapulco , mexico', 'clay', 'paola suárez', '6 - 7 ( 1 - 7 ) , 6 - 4 , 6 - 2'], ['runner - up', 'july 13 , 2003', 'palermo , italy', 'clay', 'dinara safina', '3 - 6 , 4 - 6'], ['winner', 'january 8 , 2005', 'auckland , new zealand', 'hard', 'shinobu asagoe', '5 - 7 , 7 - 5 , 6 - 4'], ['winner', 'august 14 , 2005', 'stockholm , sweden', 'hard', 'anastasia myskina', '7 - 5 , 6 - 2'], ['runner - up', 'september 25 , 2005', 'portorož , slovenia', 'hard', 'klára koukalová', '2 - 6 , 6 - 4 , 3 - 6'], ['runner - up', 'july 25 , 2006', 'cincinnati , united states', 'hard', 'vera zvonareva', '2 - 6 , 4 - 6'], ['runner - up', 'september 23 , 2007', 'portorož , slovenia', 'hard', 'tatiana golovin', '6 - 2 , 4 - 6 , 4 - 6'], ['runner - up', 'may 25 , 2008', 'strasbourg , france', 'clay', 'anabel medina garrigues', '6 - 4 , 6 - 7 ( 4 - 7 ) , 0 - 6']] |
television in thailand | https://en.wikipedia.org/wiki/Television_in_Thailand | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-18987481-3.html.csv | superlative | bbtv ch7 had the highest market share of television in thailand in 2011 1h . | {'scope': 'all', 'col_superlative': '8', 'row_superlative': '1', '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', '2011 1h'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; 2011 1h }'}, 'tv station ( operator )'], 'result': 'bbtv ch7', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; 2011 1h } ; tv station ( operator ) }'}, 'bbtv ch7'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; 2011 1h } ; tv station ( operator ) } ; bbtv ch7 } = true', 'tointer': 'select the row whose 2011 1h record of all rows is maximum . the tv station ( operator ) record of this row is bbtv ch7 .'} | eq { hop { argmax { all_rows ; 2011 1h } ; tv station ( operator ) } ; bbtv ch7 } = true | select the row whose 2011 1h record of all rows is maximum . the tv station ( operator ) record of this row is bbtv ch7 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, '2011 1h_5': 5, 'tv station (operator)_6': 6, 'bbtv ch7_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', '2011 1h_5': '2011 1h', 'tv station (operator)_6': 'tv station ( operator )', 'bbtv ch7_7': 'bbtv ch7'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], '2011 1h_5': [0], 'tv station (operator)_6': [1], 'bbtv ch7_7': [2]} | ['tv station ( operator )', '2005', '2006', '2007', '2008', '2009', '2010', '2011 1h'] | [['bbtv ch7', '42.4', '41.3', '42.0', '44.7', '45.4', '43.8', '47.5'], ['tv3', '24.5', '25.6', '29.5', '26.8', '27.7', '29.5', '29.0'], ['tv5', '8.1', '7.3', '6.7', '7.6', '8.6', '8.0', '6.9'], ['modernine tv', '10.3', '10.2', '9.2', '9.6', '9.9', '9.7', '9.2'], ['nbt', '2.9', '3.0', '2.4', '4.9', '3.4', '3.4', '2.4'], ['thai pbs', '11.8', '12.6', '10.2', '6.1', '4.9', '5.6', '5.0']] |
1985 pga tour | https://en.wikipedia.org/wiki/1985_PGA_Tour | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-14640372-3.html.csv | aggregation | in the 1985 pga tour , the average winnings of the top five finishers was $ 426,356.60 . | {'scope': 'all', 'col': '4', 'type': 'average', 'result': '$ 426,356.60', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'avg', 'args': ['all_rows', 'earnings'], 'result': '$ 426,356.60', 'ind': 0, 'tostr': 'avg { all_rows ; earnings }'}, '$ 426,356.60'], 'result': True, 'ind': 1, 'tostr': 'round_eq { avg { all_rows ; earnings } ; $ 426,356.60 } = true', 'tointer': 'the average of the earnings record of all rows is $ 426,356.60 .'} | round_eq { avg { all_rows ; earnings } ; $ 426,356.60 } = true | the average of the earnings record of all rows is $ 426,356.60 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'avg_0': 0, 'all_rows_3': 3, 'earnings_4': 4, '$426,356.60_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'avg_0': 'avg', 'all_rows_3': 'all_rows', 'earnings_4': 'earnings', '$426,356.60_5': '$ 426,356.60'} | {'eq_1': [2], 'result_2': [], 'avg_0': [1], 'all_rows_3': [0], 'earnings_4': [0], '$426,356.60_5': [1]} | ['rank', 'player', 'country', 'earnings', 'events', 'wins'] | [['1', 'curtis strange', 'united states', '542321', '25', '3'], ['2', 'lanny wadkins', 'united states', '446893', '24', '3'], ['3', 'calvin peete', 'united states', '384489', '22', '2'], ['4', 'jim thorpe', 'united states', '379091', '28', '2'], ['5', 'raymond floyd', 'united states', '378989', '22', '1']] |
1949 - 50 new york rangers season | https://en.wikipedia.org/wiki/1949%E2%80%9350_New_York_Rangers_season | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-17311417-7.html.csv | ordinal | in the 1949-50 new york rangers season , the 2nd to last game had a score of 8-7 . | {'row': '12', 'col': '2', 'order': '12', 'col_other': '4', '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', 'march', '12'], 'result': None, 'ind': 0, 'tostr': 'nth_argmin { all_rows ; march ; 12 }'}, 'score'], 'result': '8 - 7', 'ind': 1, 'tostr': 'hop { nth_argmin { all_rows ; march ; 12 } ; score }'}, '8 - 7'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { nth_argmin { all_rows ; march ; 12 } ; score } ; 8 - 7 } = true', 'tointer': 'select the row whose march record of all rows is 12th minimum . the score record of this row is 8 - 7 .'} | eq { hop { nth_argmin { all_rows ; march ; 12 } ; score } ; 8 - 7 } = true | select the row whose march record of all rows is 12th minimum . the score record of this row is 8 - 7 . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'nth_argmin_0': 0, 'all_rows_4': 4, 'march_5': 5, '12_6': 6, 'score_7': 7, '8 - 7_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', 'march_5': 'march', '12_6': '12', 'score_7': 'score', '8 - 7_8': '8 - 7'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'nth_argmin_0': [1], 'all_rows_4': [0], 'march_5': [0], '12_6': [0], 'score_7': [1], '8 - 7_8': [2]} | ['game', 'march', 'opponent', 'score', 'record'] | [['58', '1', 'detroit red wings', '5 - 2', '24 - 23 - 11'], ['59', '4', 'boston bruins', '5 - 1', '24 - 24 - 11'], ['60', '5', 'toronto maple leafs', '5 - 2', '25 - 24 - 11'], ['61', '8', 'chicago black hawks', '4 - 2', '26 - 24 - 11'], ['62', '9', 'detroit red wings', '3 - 1', '27 - 24 - 11'], ['63', '11', 'toronto maple leafs', '4 - 0', '27 - 25 - 11'], ['64', '12', 'montreal canadiens', '5 - 1', '27 - 26 - 11'], ['65', '15', 'boston bruins', '4 - 1', '27 - 27 - 11'], ['66', '18', 'montreal canadiens', '5 - 3', '27 - 28 - 11'], ['67', '19', 'montreal canadiens', '4 - 2', '27 - 29 - 11'], ['68', '21', 'chicago black hawks', '6 - 3', '27 - 30 - 11'], ['69', '22', 'detroit red wings', '8 - 7', '27 - 31 - 11'], ['70', '26', 'toronto maple leafs', '5 - 3', '28 - 31 - 11']] |
three rivers conference ( indiana ) | https://en.wikipedia.org/wiki/Three_Rivers_Conference_%28Indiana%29 | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-15176211-1.html.csv | count | 3 schools in the three rivers conference are located in wabash . | {'scope': 'all', 'criterion': 'equal', 'value': 'wabash', 'result': '3', 'col': '2', 'subset': None} | {'func': 'eq', 'args': [{'func': 'count', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'location', 'wabash'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose location record fuzzily matches to wabash .', 'tostr': 'filter_eq { all_rows ; location ; wabash }'}], 'result': '3', 'ind': 1, 'tostr': 'count { filter_eq { all_rows ; location ; wabash } }', 'tointer': 'select the rows whose location record fuzzily matches to wabash . the number of such rows is 3 .'}, '3'], 'result': True, 'ind': 2, 'tostr': 'eq { count { filter_eq { all_rows ; location ; wabash } } ; 3 } = true', 'tointer': 'select the rows whose location record fuzzily matches to wabash . the number of such rows is 3 .'} | eq { count { filter_eq { all_rows ; location ; wabash } } ; 3 } = true | select the rows whose location record fuzzily matches to wabash . 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, 'location_5': 5, 'wabash_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', 'location_5': 'location', 'wabash_6': 'wabash', '3_7': '3'} | {'eq_2': [3], 'result_3': [], 'count_1': [2], 'filter_str_eq_0': [1], 'all_rows_4': [0], 'location_5': [0], 'wabash_6': [0], '3_7': [2]} | ['school', 'location', 'mascot', 'enrollment', 'ihsaa class', 'county', 'year joined', 'previous conference'] | [['manchester', 'north manchester', 'squires', '434', 'aa', '85 wabash', '1976', 'northern lakes'], ['northfield', 'wabash', 'norsemen', '380', 'aa', '85 wabash', '1971', 'none ( new school )'], ['north miami', 'denver', 'warriors', '348', 'a', '52 miami', '1971', 'mid - indiana'], ['rochester community', 'rochester', 'zebras', '615', 'aaa', '25 fulton', '1987', 'northern lakes'], ['southwood', 'wabash', 'knights', '427', 'aa', '85 wabash', '1976', 'mid - indiana'], ['tippecanoe valley', 'akron', 'vikings', '600', 'aaa', '43 kosciusko', '1976', 'independents'], ['wabash', 'wabash', 'apaches', '455', 'aa', '85 wabash', '2006', 'central indiana'], ['whitko', 'south whitley', 'wildcats', '613', 'aa', '92 whitley', '1976', 'independents']] |
princess royal | https://en.wikipedia.org/wiki/Princess_Royal | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/2-172426-1.html.csv | comparative | of the princess royals , anne , princess royal 1709 - 1759 , was married 63 years before charlotte , princess royal 1766 - 1828 . | {'row_1': '2', 'row_2': '3', 'col': '5', 'col_other': '2', 'relation': 'diff', 'record_mentioned': 'no', 'diff_result': {'diff_value': '63', 'bigger': 'row2'}} | {'func': 'eq', 'args': [{'func': 'diff', 'args': [{'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name dates', 'anne , princess royal 1709 - 1759'], 'result': None, 'ind': 0, 'tointer': 'select the rows whose name dates record fuzzily matches to anne , princess royal 1709 - 1759 .', 'tostr': 'filter_eq { all_rows ; name dates ; anne , princess royal 1709 - 1759 }'}, 'date married'], 'result': None, 'ind': 2, 'tostr': 'hop { filter_eq { all_rows ; name dates ; anne , princess royal 1709 - 1759 } ; date married }', 'tointer': 'select the rows whose name dates record fuzzily matches to anne , princess royal 1709 - 1759 . take the date married record of this row .'}, {'func': 'num_hop', 'args': [{'func': 'filter_str_eq', 'args': ['all_rows', 'name dates', 'charlotte , princess royal 1766 - 1828'], 'result': None, 'ind': 1, 'tointer': 'select the rows whose name dates record fuzzily matches to charlotte , princess royal 1766 - 1828 .', 'tostr': 'filter_eq { all_rows ; name dates ; charlotte , princess royal 1766 - 1828 }'}, 'date married'], 'result': None, 'ind': 3, 'tostr': 'hop { filter_eq { all_rows ; name dates ; charlotte , princess royal 1766 - 1828 } ; date married }', 'tointer': 'select the rows whose name dates record fuzzily matches to charlotte , princess royal 1766 - 1828 . take the date married record of this row .'}], 'result': '-63', 'ind': 4, 'tostr': 'diff { hop { filter_eq { all_rows ; name dates ; anne , princess royal 1709 - 1759 } ; date married } ; hop { filter_eq { all_rows ; name dates ; charlotte , princess royal 1766 - 1828 } ; date married } }'}, '-63'], 'result': True, 'ind': 5, 'tostr': 'eq { diff { hop { filter_eq { all_rows ; name dates ; anne , princess royal 1709 - 1759 } ; date married } ; hop { filter_eq { all_rows ; name dates ; charlotte , princess royal 1766 - 1828 } ; date married } } ; -63 } = true', 'tointer': 'select the rows whose name dates record fuzzily matches to anne , princess royal 1709 - 1759 . take the date married record of this row . select the rows whose name dates record fuzzily matches to charlotte , princess royal 1766 - 1828 . take the date married record of this row . the second record is 63 larger than the first record .'} | eq { diff { hop { filter_eq { all_rows ; name dates ; anne , princess royal 1709 - 1759 } ; date married } ; hop { filter_eq { all_rows ; name dates ; charlotte , princess royal 1766 - 1828 } ; date married } } ; -63 } = true | select the rows whose name dates record fuzzily matches to anne , princess royal 1709 - 1759 . take the date married record of this row . select the rows whose name dates record fuzzily matches to charlotte , princess royal 1766 - 1828 . take the date married record of this row . the second record is 63 larger than the first 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, 'name dates_8': 8, 'anne , princess royal 1709 - 1759_9': 9, 'date married_10': 10, 'num_hop_3': 3, 'filter_str_eq_1': 1, 'all_rows_11': 11, 'name dates_12': 12, 'charlotte , princess royal 1766 - 1828_13': 13, 'date married_14': 14, '-63_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', 'name dates_8': 'name dates', 'anne , princess royal 1709 - 1759_9': 'anne , princess royal 1709 - 1759', 'date married_10': 'date married', 'num_hop_3': 'num_hop', 'filter_str_eq_1': 'filter_str_eq', 'all_rows_11': 'all_rows', 'name dates_12': 'name dates', 'charlotte , princess royal 1766 - 1828_13': 'charlotte , princess royal 1766 - 1828', 'date married_14': 'date married', '-63_15': '-63'} | {'eq_5': [6], 'result_6': [], 'diff_4': [5], 'num_hop_2': [4], 'filter_str_eq_0': [2], 'all_rows_7': [0], 'name dates_8': [0], 'anne , princess royal 1709 - 1759_9': [0], 'date married_10': [2], 'num_hop_3': [4], 'filter_str_eq_1': [3], 'all_rows_11': [1], 'name dates_12': [1], 'charlotte , princess royal 1766 - 1828_13': [1], 'date married_14': [3], '-63_15': [5]} | ['order', 'name dates', 'princess royal from ( date ) to ( date )', 'parent', 'date married', 'husband dates'] | [['1', 'mary , princess royal 1631 - 1660', '1642 - 1660', 'charles i 1600 - 1649', '1641', 'william ii , prince of orange 1626 - 1650'], ['2', 'anne , princess royal 1709 - 1759', '1727 - 1759', 'george ii 1683 - 1760', '1734', 'william iv , prince of orange 1711 - 1751'], ['3', 'charlotte , princess royal 1766 - 1828', '1789 - 1828', 'george iii 1738 - 1820', '1797', 'king frederick i of württemberg 1754 - 1816'], ['4', 'victoria , princess royal 1840 - 1901', '1841 - 1901', 'victoria 1819 - 1901', '1858', 'frederick iii , german emperor 1831 - 1888'], ['5', 'louise , princess royal 1867 - 1931', '1905 - 1931', 'edward vii 1841 - 1910', '1889', 'alexander duff , 1st duke of fife 1849 - 1912'], ['6', 'mary , princess royal 1897 - 1965', '1932 - 1965', 'george v 1865 - 1936', '1922', 'henry lascelles , 6th earl of harewood 1882 - 1947'], ['7', 'anne , princess royal 1950 -', '1987 - present', 'elizabeth ii 1926 -', '1973 - 1992', 'mark phillips 1948 -'], ['7', 'anne , princess royal 1950 -', '1987 - present', 'elizabeth ii 1926 -', '1992', 'sir timothy laurence 1955 -']] |
north state conference | https://en.wikipedia.org/wiki/North_State_Conference | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-16168849-1.html.csv | superlative | in the north state conference , the highest enrollment is at east carolina university . | {'scope': 'all', 'col_superlative': '5', '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', 'enrollment'], 'result': None, 'ind': 0, 'tostr': 'argmax { all_rows ; enrollment }'}, 'institution'], 'result': 'east carolina university', 'ind': 1, 'tostr': 'hop { argmax { all_rows ; enrollment } ; institution }'}, 'east carolina university'], 'result': True, 'ind': 2, 'tostr': 'eq { hop { argmax { all_rows ; enrollment } ; institution } ; east carolina university } = true', 'tointer': 'select the row whose enrollment record of all rows is maximum . the institution record of this row is east carolina university .'} | eq { hop { argmax { all_rows ; enrollment } ; institution } ; east carolina university } = true | select the row whose enrollment record of all rows is maximum . the institution record of this row is east carolina university . | 3 | 3 | {'str_eq_2': 2, 'result_3': 3, 'str_hop_1': 1, 'argmax_0': 0, 'all_rows_4': 4, 'enrollment_5': 5, 'institution_6': 6, 'east carolina university_7': 7} | {'str_eq_2': 'str_eq', 'result_3': 'true', 'str_hop_1': 'str_hop', 'argmax_0': 'argmax', 'all_rows_4': 'all_rows', 'enrollment_5': 'enrollment', 'institution_6': 'institution', 'east carolina university_7': 'east carolina university'} | {'str_eq_2': [3], 'result_3': [], 'str_hop_1': [2], 'argmax_0': [1], 'all_rows_4': [0], 'enrollment_5': [0], 'institution_6': [1], 'east carolina university_7': [2]} | ['institution', 'location', 'founded', 'type', 'enrollment', 'nickname', 'joined', 'left', 'current conference'] | [['anderson university', 'anderson , south carolina', '1911', 'private', '2907', 'trojans', '1998', '2010', 'sac'], ['appalachian state university', 'boone , north carolina', '1899', 'public', '17589', 'mountaineers', '1930', '1967', 'socon ( sun belt in 2014 ) ( ncaa division i )'], ['catawba college', 'salisbury , north carolina', '1851', 'private', '1300', 'indians', '1930', '1989', 'sac'], ['coker college', 'hartsville , south carolina', '1908', 'private', '1200', 'cobras', '1991', '2013', 'sac'], ['east carolina university', 'greenville , north carolina', '1907', 'public', '27386', 'pirates', '1947', '1962', 'c - usa ( the american in 2014 ) ( ncaa division i )'], ['elon university', 'elon , north carolina', '1889', 'private', '6720', 'phoenix', '1930', '1989', 'socon ( caa in 2014 ) ( ncaa division i )'], ['guilford college', 'greensboro , north carolina', '1837', 'private', '2706', 'quakers', '1930', '1988', 'odac ( ncaa division iii )'], ['high point university', 'high point , north carolina', '1924', 'private', '4519', 'panthers', '1930', '1997', 'big south ( ncaa division i )'], ['lenoirrhyne university', 'hickory , north carolina', '1891', 'private', '1983', 'bears', '1930 , 1985', '1974 , 1989', 'sac'], ['longwood university', 'farmville , virginia', '1839', 'public', '4800', 'lancers', '1995', '2003', 'big south ( ncaa division i )'], ['mars hill college', 'mars hill , north carolina', '1856', 'private', '1370', 'lions', '1973', '1975', 'sac'], ['newberry college', 'newberry , south carolina', '1856', 'private', '949', 'wolves', '1961', '1972', 'sac'], ['university of north carolina at pembroke', 'pembroke , north carolina', '1887', 'public', '6433', 'braves', '1976', '1992', 'peach belt ( pbc )'], ['presbyterian college', 'clinton , south carolina', '1880', 'private', '1300', 'blue hose', '1965', '1972', 'big south ( ncaa division i )'], ['queens university of charlotte', 'charlotte , north carolina', '1857', 'private', '2386', 'royals', '1995', '2013', 'sac'], ['st andrews university', 'laurinburg , north carolina', '1958', 'private', '600', 'knights', '1988', '2012', 'aac ( naia )'], ['western carolina university', 'cullowhee , north carolina', '1889', 'public', '9608', 'catamounts', '1933', '1976', 'socon ( ncaa division i )']] |
nevada gaming area | https://en.wikipedia.org/wiki/Nevada_gaming_area | https://raw.githubusercontent.com/wenhuchen/Table-Fact-Checking/master/data/all_csv/1-25438110-5.html.csv | aggregation | there is a total of 217 casinos in the nevada gaming area . | {'scope': 'all', 'col': '1', 'type': 'sum', 'result': '217', 'subset': None} | {'func': 'round_eq', 'args': [{'func': 'sum', 'args': ['all_rows', 'casinos'], 'result': '217', 'ind': 0, 'tostr': 'sum { all_rows ; casinos }'}, '217'], 'result': True, 'ind': 1, 'tostr': 'round_eq { sum { all_rows ; casinos } ; 217 } = true', 'tointer': 'the sum of the casinos record of all rows is 217 .'} | round_eq { sum { all_rows ; casinos } ; 217 } = true | the sum of the casinos record of all rows is 217 . | 2 | 2 | {'eq_1': 1, 'result_2': 2, 'sum_0': 0, 'all_rows_3': 3, 'casinos_4': 4, '217_5': 5} | {'eq_1': 'eq', 'result_2': 'true', 'sum_0': 'sum', 'all_rows_3': 'all_rows', 'casinos_4': 'casinos', '217_5': '217'} | {'eq_1': [2], 'result_2': [], 'sum_0': [1], 'all_rows_3': [0], 'casinos_4': [0], '217_5': [1]} | ['casinos', 'county', 'road', '1 - jul - 08', 'fy07 millions', 'fy08 millions', 'fy09 millions'] | [['149', 'clark', 'i - 15', '1865746', '10538', '10172', '9081'], ['32', 'washoe', 'i - 80', '410443', '1045', '977', '856'], ['17', 'elko', 'i - 80', '47071', '324', '303', '279'], ['5', 'south lake tahoe', 'route 50', '45180', '283', '307', '264'], ['14', 'carson valley', 'route 395', '54867', '120', '114', '102']] |
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