niryuu/sni-each-converted huggingface dataset:
Command: eq { hop { argmax { all_rows ; torque ( nm ) / rpm } ; model / engine } ; 2.0 duratec he }, interpretation: select the row whose torque ( nm ) / rpm record of all rows is maximum . the model / engine record of this row is 2.0 duratec he .
Output: yes
Command: eq { hop { argmax { all_rows ; torque ( nm ) / rpm } ; model / engine } ; 2.0 duratec he }, interpretation: select the rows whose opponent record fuzzily matches to murilo bustamante . take the round record of this row . select the rows whose opponent record fuzzily matches to david abbott . take the round record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; opponent ; murilo bustamante } ; round } ; hop { filter_eq { all_rows ; opponent ; david abbott } ; round } }, interpretation: select the rows whose opponent record fuzzily matches to murilo bustamante . take the round record of this row . select the rows whose opponent record fuzzily matches to david abbott . take the round record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; opponent ; murilo bustamante } ; round } ; hop { filter_eq { all_rows ; opponent ; david abbott } ; round } }, interpretation: the 1st maximum crowd record of all rows is 26000 . the venue record of the row with 1st maximum crowd record is punt road oval . the date record of the row with 1st maximum crowd record is 9 august 1941 .
Output: no
Command: and { eq { nth_max { all_rows ; crowd ; 1 } ; 26000 } ; and { eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; punt road oval } ; eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; date } ; 9 august 1941 } } }, interpretation: the 1st maximum crowd record of all rows is 26000 . the venue record of the row with 1st maximum crowd record is punt road oval . the date record of the row with 1st maximum crowd record is 9 august 1941 .
Output: yes
Command: and { eq { nth_max { all_rows ; crowd ; 1 } ; 26000 } ; and { eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; punt road oval } ; eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; date } ; 9 august 1941 } } }, interpretation: for the first elected records of all rows , most of them are equal to 1982 .
Output: no
Command: most_eq { all_rows ; first elected ; 1982 }, interpretation: for the first elected records of all rows , most of them are equal to 1982 .
Output: yes
Command: most_eq { all_rows ; first elected ; 1982 }, interpretation: select the rows whose player record fuzzily matches to tom bladon . take the round record of this row . select the rows whose player record fuzzily matches to jim watson . take the round record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; player ; tom bladon } ; round } ; hop { filter_eq { all_rows ; player ; jim watson } ; round } }, interpretation: select the rows whose player record fuzzily matches to tom bladon . take the round record of this row . select the rows whose player record fuzzily matches to jim watson . take the round record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; player ; tom bladon } ; round } ; hop { filter_eq { all_rows ; player ; jim watson } ; round } }, interpretation: 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 .
Output: no
Command: greater { hop { filter_eq { all_rows ; player ; nick price } ; wins } ; hop { filter_eq { all_rows ; player ; fred couples } ; wins } }, interpretation: 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 .
Output: yes
Command: greater { hop { filter_eq { all_rows ; player ; nick price } ; wins } ; hop { filter_eq { all_rows ; player ; fred couples } ; wins } }, interpretation: select the row whose 2010 record of all rows is maximum . the channel record of this row is rai 1 .
Output: no
Command: eq { hop { argmax { all_rows ; 2010 } ; channel } ; rai 1 }, interpretation: select the row whose 2010 record of all rows is maximum . the channel record of this row is rai 1 .
Output: yes
Command: eq { hop { argmax { all_rows ; 2010 } ; channel } ; rai 1 }, interpretation: select the rows whose country record fuzzily matches to fiji . there is only one such row in the table . the player record of this unqiue row is vijay singh .
Output: no
Command: and { only { filter_eq { all_rows ; country ; fiji } } ; eq { hop { filter_eq { all_rows ; country ; fiji } ; player } ; vijay singh } }, interpretation: select the rows whose country record fuzzily matches to fiji . there is only one such row in the table . the player record of this unqiue row is vijay singh .
Output: yes
Command: and { only { filter_eq { all_rows ; country ; fiji } } ; eq { hop { filter_eq { all_rows ; country ; fiji } ; player } ; vijay singh } }, interpretation: select the rows whose total matches record is equal to 5 . the number of such rows is 6 .
Output: no
Command: eq { count { filter_eq { all_rows ; total matches ; 5 } } ; 6 }, interpretation: select the rows whose total matches record is equal to 5 . the number of such rows is 6 .
Output: yes
Command: eq { count { filter_eq { all_rows ; total matches ; 5 } } ; 6 }, interpretation: select the rows whose tv station record fuzzily matches to fuji tv . select the row whose average ratings record of these rows is minimum . the romaji title record of this row is rikon bengoshi ii ~ handsome woman ~ .
Output: no
Command: eq { hop { argmin { filter_eq { all_rows ; tv station ; fuji tv } ; average ratings } ; romaji title } ; rikon bengoshi ii ~ handsome woman ~ }, interpretation: select the rows whose tv station record fuzzily matches to fuji tv . select the row whose average ratings record of these rows is minimum . the romaji title record of this row is rikon bengoshi ii ~ handsome woman ~ .
Output: yes
Command: eq { hop { argmin { filter_eq { all_rows ; tv station ; fuji tv } ; average ratings } ; romaji title } ; rikon bengoshi ii ~ handsome woman ~ }, interpretation: select the row whose package version record of all rows is 2nd maximum . the carrier record of this row is vodafone au .
Output: no
Command: eq { hop { nth_argmax { all_rows ; package version ; 2 } ; carrier } ; vodafone au }, interpretation: select the row whose package version record of all rows is 2nd maximum . the carrier record of this row is vodafone au .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; package version ; 2 } ; carrier } ; vodafone au }, interpretation: select the row whose winners share record of all rows is maximum . the year record of this row is 2010 .
Output: no
Command: eq { hop { argmax { all_rows ; winners share } ; year } ; 2010 }, interpretation: select the row whose winners share record of all rows is maximum . the year record of this row is 2010 .
Output: yes
Command: eq { hop { argmax { all_rows ; winners share } ; year } ; 2010 }, interpretation: select the row whose date record of all rows is 3rd maximum . the tournament record of this row is westende .
Output: no
Command: eq { hop { nth_argmax { all_rows ; date ; 3 } ; tournament } ; westende }, interpretation: select the row whose date record of all rows is 3rd maximum . the tournament record of this row is westende .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; date ; 3 } ; tournament } ; westende }, interpretation: for the viewing figure records of all rows , most of them are greater than 7 .
Output: no
Command: most_greater { all_rows ; viewing figure ; 7 }, interpretation: for the viewing figure records of all rows , most of them are greater than 7 .
Output: yes
Command: most_greater { all_rows ; viewing figure ; 7 }, interpretation: select the row whose time record of all rows is 3rd minimum . the rider record of this row is ross johnson .
Output: no
Command: eq { hop { nth_argmin { all_rows ; time ; 3 } ; rider } ; ross johnson }, interpretation: select the row whose time record of all rows is 3rd minimum . the rider record of this row is ross johnson .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; time ; 3 } ; rider } ; ross johnson }, interpretation: 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 .
Output: no
Command: less { hop { filter_eq { all_rows ; notes ; enid } ; built } ; hop { filter_eq { all_rows ; notes ; snowdon } ; built } }, interpretation: 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 .
Output: yes
Command: less { hop { filter_eq { all_rows ; notes ; enid } ; built } ; hop { filter_eq { all_rows ; notes ; snowdon } ; built } }, interpretation: select the row whose attendance record of all rows is maximum . the date record of this row is september 30 , 1990 .
Output: no
Command: eq { hop { argmax { all_rows ; attendance } ; date } ; september 30 , 1990 }, interpretation: select the row whose attendance record of all rows is maximum . the date record of this row is september 30 , 1990 .
Output: yes
Command: eq { hop { argmax { all_rows ; attendance } ; date } ; september 30 , 1990 }, interpretation: select the row whose entered service record of all rows is 3rd minimum . the locomotive record of this row is c503 .
Output: no
Command: eq { hop { nth_argmin { all_rows ; entered service ; 3 } ; locomotive } ; c503 }, interpretation: select the row whose entered service record of all rows is 3rd minimum . the locomotive record of this row is c503 .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; entered service ; 3 } ; locomotive } ; c503 }, interpretation: for the gender records of all rows , all of them fuzzily match to coed .
Output: no
Command: all_eq { all_rows ; gender ; coed }, interpretation: for the home records of all rows , most of them fuzzily match to philadelphia .
Output: no
Command: most_eq { all_rows ; home ; philadelphia }, interpretation: for the home records of all rows , most of them fuzzily match to philadelphia .
Output: yes
Command: most_eq { all_rows ; home ; philadelphia }, interpretation: the average of the to par record of all rows is -5.6 .
Output: no
Command: round_eq { avg { all_rows ; to par } ; -5.6 }, interpretation: the average of the to par record of all rows is -5.6 .
Output: yes
Command: round_eq { avg { all_rows ; to par } ; -5.6 }, interpretation: select the rows whose original album record fuzzily matches to previously unreleased . among these rows , select the rows whose time record is greater than 4:00 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_greater { filter_eq { all_rows ; original album ; previously unreleased } ; time ; 4:00 } } ; 2 }, interpretation: select the rows whose original album record fuzzily matches to previously unreleased . among these rows , select the rows whose time record is greater than 4:00 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_greater { filter_eq { all_rows ; original album ; previously unreleased } ; time ; 4:00 } } ; 2 }, interpretation: select the rows whose score record fuzzily matches to w . among these rows , select the rows whose high points record fuzzily matches to kevin martin . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; score ; w } ; high points ; kevin martin } } ; 3 }, interpretation: select the rows whose score record fuzzily matches to w . among these rows , select the rows whose high points record fuzzily matches to kevin martin . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; score ; w } ; high points ; kevin martin } } ; 3 }, interpretation: select the rows whose year record is equal to 1987 . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; year ; 1987 } } ; 5 }, interpretation: select the rows whose year record is equal to 1987 . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; year ; 1987 } } ; 5 }, interpretation: select the rows whose crowd record is less than 10000 . there is only one such row in the table . the home team record of this unqiue row is st kilda .
Output: no
Command: and { only { filter_less { all_rows ; crowd ; 10000 } } ; eq { hop { filter_less { all_rows ; crowd ; 10000 } ; home team } ; st kilda } }, interpretation: select the rows whose crowd record is less than 10000 . there is only one such row in the table . the home team record of this unqiue row is st kilda .
Output: yes
Command: and { only { filter_less { all_rows ; crowd ; 10000 } } ; eq { hop { filter_less { all_rows ; crowd ; 10000 } ; home team } ; st kilda } }, interpretation: select the row whose score record of all rows is maximum . the date record of this row is june 24 .
Output: no
Command: eq { hop { argmax { all_rows ; score } ; date } ; june 24 }, interpretation: select the row whose score record of all rows is maximum . the date record of this row is june 24 .
Output: yes
Command: eq { hop { argmax { all_rows ; score } ; date } ; june 24 }, interpretation: for the date records of all rows , all of them fuzzily match to december .
Output: no
Command: all_eq { all_rows ; date ; december }, interpretation: for the date records of all rows , all of them fuzzily match to december .
Output: yes
Command: all_eq { all_rows ; date ; december }, interpretation: select the row whose 2011 ( imf ) record of all rows is 3rd maximum . the nation record of this row is uruguay .
Output: no
Command: eq { hop { nth_argmax { all_rows ; 2011 ( imf ) ; 3 } ; nation } ; uruguay }, interpretation: select the row whose 2011 ( imf ) record of all rows is 3rd maximum . the nation record of this row is uruguay .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; 2011 ( imf ) ; 3 } ; nation } ; uruguay }, interpretation: select the rows whose 2003 - 04 pts record is equal to - . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; 2003 - 04 pts ; - } } ; 4 }, interpretation: select the rows whose 2003 - 04 pts record is equal to - . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; 2003 - 04 pts ; - } } ; 4 }, interpretation: select the rows whose country record fuzzily matches to russia . the sum of the uci points record of these rows is 90 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; country ; russia } ; uci points } ; 90 }, interpretation: select the rows whose country record fuzzily matches to russia . the sum of the uci points record of these rows is 90 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; country ; russia } ; uci points } ; 90 }, interpretation: select the rows whose home team record fuzzily matches to melbourne . among these rows , select the rows whose crowd record is greater than 10000 . there is only one such row in the table . the venue record of this unqiue row is mcg .
Output: no
Command: and { only { filter_greater { filter_eq { all_rows ; home team ; melbourne } ; crowd ; 10000 } } ; eq { hop { filter_greater { filter_eq { all_rows ; home team ; melbourne } ; crowd ; 10000 } ; venue } ; mcg } }, interpretation: select the rows whose home team record fuzzily matches to melbourne . among these rows , select the rows whose crowd record is greater than 10000 . there is only one such row in the table . the venue record of this unqiue row is mcg .
Output: yes
Command: and { only { filter_greater { filter_eq { all_rows ; home team ; melbourne } ; crowd ; 10000 } } ; eq { hop { filter_greater { filter_eq { all_rows ; home team ; melbourne } ; crowd ; 10000 } ; venue } ; mcg } }, interpretation: select the rows whose height record fuzzily matches to 6 ' 4 . among these rows , select the rows whose round record is equal to 2 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; height ; 6 ' 4 } ; round ; 2 } } ; 2 }, interpretation: select the rows whose height record fuzzily matches to 6 ' 4 . among these rows , select the rows whose round record is equal to 2 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; height ; 6 ' 4 } ; round ; 2 } } ; 2 }, interpretation: the 3rd maximum years for jazz record of all rows is 1982 - 85 . the player record of the row with 3rd maximum years for jazz record is john drew .
Output: no
Command: and { eq { nth_max { all_rows ; years for jazz ; 3 } ; 1982 - 85 } ; eq { hop { nth_argmax { all_rows ; years for jazz ; 3 } ; player } ; john drew } }, interpretation: the 3rd maximum years for jazz record of all rows is 1982 - 85 . the player record of the row with 3rd maximum years for jazz record is john drew .
Output: yes
Command: and { eq { nth_max { all_rows ; years for jazz ; 3 } ; 1982 - 85 } ; eq { hop { nth_argmax { all_rows ; years for jazz ; 3 } ; player } ; john drew } }, interpretation: select the row whose goals scored ( gf ) record of all rows is maximum . the team ( equipo ) record of this row is arabe unido .
Output: no
Command: eq { hop { argmax { all_rows ; goals scored ( gf ) } ; team ( equipo ) } ; arabe unido }, interpretation: select the row whose goals scored ( gf ) record of all rows is maximum . the team ( equipo ) record of this row is arabe unido .
Output: yes
Command: eq { hop { argmax { all_rows ; goals scored ( gf ) } ; team ( equipo ) } ; arabe unido }, interpretation: select the rows whose type of game record fuzzily matches to friendly . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; type of game ; friendly } } ; 5 }, interpretation: select the rows whose type of game record fuzzily matches to friendly . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; type of game ; friendly } } ; 5 }, interpretation: select the rows whose tournament record fuzzily matches to brazil . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; tournament ; brazil } } ; 3 }, interpretation: select the rows whose tournament record fuzzily matches to brazil . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; tournament ; brazil } } ; 3 }, interpretation: select the row whose date record of all rows is 1st minimum . the title record of this row is the love letter .
Output: no
Command: eq { hop { nth_argmin { all_rows ; date ; 1 } ; title } ; the love letter }, interpretation: select the row whose date record of all rows is 1st minimum . the title record of this row is the love letter .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; date ; 1 } ; title } ; the love letter }, interpretation: select the rows whose district record fuzzily matches to sagar . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; district ; sagar } } ; 3 }, interpretation: select the rows whose district record fuzzily matches to sagar . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; district ; sagar } } ; 3 }, interpretation: 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 .
Output: no
Command: and { only { filter_greater { all_rows ; gold ; 100 } } ; eq { hop { filter_greater { all_rows ; gold ; 100 } ; province } ; bangkok } }, interpretation: 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 .
Output: yes
Command: and { only { filter_greater { all_rows ; gold ; 100 } } ; eq { hop { filter_greater { all_rows ; gold ; 100 } ; province } ; bangkok } }, interpretation: select the row whose vessels record of all rows is 2nd maximum . the ship name record of this row is theofilos .
Output: no
Command: eq { hop { nth_argmax { all_rows ; vessels ; 2 } ; ship name } ; theofilos }, interpretation: select the row whose vessels record of all rows is 2nd maximum . the ship name record of this row is theofilos .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; vessels ; 2 } ; ship name } ; theofilos }, interpretation: select the rows whose artist record fuzzily matches to hind . take the points record of this row . select the rows whose artist record fuzzily matches to gisela . take the points record of this row . the first record is 5 larger than the second record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; artist ; hind } ; points } ; hop { filter_eq { all_rows ; artist ; gisela } ; points } } ; 5 }, interpretation: select the rows whose artist record fuzzily matches to hind . take the points record of this row . select the rows whose artist record fuzzily matches to gisela . take the points record of this row . the first record is 5 larger than the second record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; artist ; hind } ; points } ; hop { filter_eq { all_rows ; artist ; gisela } ; points } } ; 5 }, interpretation: select the rows whose year record fuzzily matches to 2005 . take the points record of this row . select the rows whose year record fuzzily matches to 2007 . take the points record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; year ; 2005 } ; points } ; hop { filter_eq { all_rows ; year ; 2007 } ; points } }, interpretation: select the rows whose year record fuzzily matches to 2005 . take the points record of this row . select the rows whose year record fuzzily matches to 2007 . take the points record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; year ; 2005 } ; points } ; hop { filter_eq { all_rows ; year ; 2007 } ; points } }, interpretation: select the row whose attendance record of all rows is 2nd maximum . the opponent record of this row is rhein fire .
Output: no
Command: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; opponent } ; rhein fire }, interpretation: select the row whose attendance record of all rows is 2nd maximum . the opponent record of this row is rhein fire .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; opponent } ; rhein fire }, interpretation: select the rows whose coach record fuzzily matches to bj coleman . there is only one such row in the table . the episode summary record of this unqiue row is chris is made into a celebrity assistant .
Output: no
Command: and { only { filter_eq { all_rows ; coach ; bj coleman } } ; eq { hop { filter_eq { all_rows ; coach ; bj coleman } ; episode summary } ; chris is made into a celebrity assistant } }, interpretation: select the rows whose coach record fuzzily matches to bj coleman . there is only one such row in the table . the episode summary record of this unqiue row is chris is made into a celebrity assistant .
Output: yes
Command: and { only { filter_eq { all_rows ; coach ; bj coleman } } ; eq { hop { filter_eq { all_rows ; coach ; bj coleman } ; episode summary } ; chris is made into a celebrity assistant } }, interpretation: select the rows whose project record fuzzily matches to özhan canaydın project . take the capacity record of this row . select the rows whose project record fuzzily matches to faruk süren project . take the capacity record of this row . the first record is greater than the second record . the capacity record of the first row is 52652 . the capacity record of the second row is 40482 .
Output: no
Command: and { greater { hop { filter_eq { all_rows ; project ; özhan canaydın project } ; capacity } ; hop { filter_eq { all_rows ; project ; faruk süren project } ; capacity } } ; and { eq { hop { filter_eq { all_rows ; project ; özhan canaydın project } ; capacity } ; 52652 } ; eq { hop { filter_eq { all_rows ; project ; faruk süren project } ; capacity } ; 40482 } } }, interpretation: select the rows whose project record fuzzily matches to özhan canaydın project . take the capacity record of this row . select the rows whose project record fuzzily matches to faruk süren project . take the capacity record of this row . the first record is greater than the second record . the capacity record of the first row is 52652 . the capacity record of the second row is 40482 .
Output: yes
Command: and { greater { hop { filter_eq { all_rows ; project ; özhan canaydın project } ; capacity } ; hop { filter_eq { all_rows ; project ; faruk süren project } ; capacity } } ; and { eq { hop { filter_eq { all_rows ; project ; özhan canaydın project } ; capacity } ; 52652 } ; eq { hop { filter_eq { all_rows ; project ; faruk süren project } ; capacity } ; 40482 } } }, interpretation: for the killed records of all rows , most of them are greater than 13 .
Output: no
Command: most_greater { all_rows ; killed ; 13 }, interpretation: for the killed records of all rows , most of them are greater than 13 .
Output: yes
Command: most_greater { all_rows ; killed ; 13 }, interpretation: select the rows whose receiving tds record is equal to 0 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; receiving tds ; 0 } } ; 2 }, interpretation: select the rows whose receiving tds record is equal to 0 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; receiving tds ; 0 } } ; 2 }, interpretation: the average of the gold record of all rows is 0.4375 .
Output: no
Command: round_eq { avg { all_rows ; gold } ; 0.4375 }, interpretation: the average of the gold record of all rows is 0.4375 .
Output: yes
Command: round_eq { avg { all_rows ; gold } ; 0.4375 }, interpretation: select the rows whose surface record fuzzily matches to carpet . there is only one such row in the table . the date record of this unqiue row is 28 october 2007 .
Output: no
Command: and { only { filter_eq { all_rows ; surface ; carpet } } ; eq { hop { filter_eq { all_rows ; surface ; carpet } ; date } ; 28 october 2007 } }, interpretation: select the rows whose surface record fuzzily matches to carpet . there is only one such row in the table . the date record of this unqiue row is 28 october 2007 .
Output: yes
Command: and { only { filter_eq { all_rows ; surface ; carpet } } ; eq { hop { filter_eq { all_rows ; surface ; carpet } ; date } ; 28 october 2007 } }, interpretation: select the row whose date successor seated record of all rows is maximum . the district record of this row is ohio 6th .
Output: no
Command: eq { hop { argmax { all_rows ; date successor seated } ; district } ; ohio 6th }, interpretation: select the row whose date successor seated record of all rows is maximum . the district record of this row is ohio 6th .
Output: yes
Command: eq { hop { argmax { all_rows ; date successor seated } ; district } ; ohio 6th }, interpretation: for the result records of all rows , most of them fuzzily match to re - elected .
Output: no
Command: most_eq { all_rows ; result ; re - elected }, interpretation: for the result records of all rows , most of them fuzzily match to re - elected .
Output: yes
Command: most_eq { all_rows ; result ; re - elected }, interpretation: select the rows whose shooter record fuzzily matches to usa . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; shooter ; usa } } ; 2 }, interpretation: select the rows whose shooter record fuzzily matches to usa . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; shooter ; usa } } ; 2 }, interpretation: select the rows whose city or town record fuzzily matches to istanbul . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; city or town ; istanbul } } ; 4 }, interpretation: select the rows whose city or town record fuzzily matches to istanbul . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; city or town ; istanbul } } ; 4 }, interpretation: select the rows whose opponent record fuzzily matches to pittsburgh penguins . among these rows , select the rows whose location / attendance record fuzzily matches to consol energy center . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; opponent ; pittsburgh penguins } ; location / attendance ; consol energy center } } ; 2 }, interpretation: select the rows whose opponent record fuzzily matches to pittsburgh penguins . among these rows , select the rows whose location / attendance record fuzzily matches to consol energy center . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; opponent ; pittsburgh penguins } ; location / attendance ; consol energy center } } ; 2 }, interpretation: select the rows whose children together record fuzzily matches to 1 son . there is only one such row in the table . the name record of this unqiue row is norris church mailer . the deceased spouse record of this unqiue row is norman mailer .
Output: no
Command: and { only { filter_eq { all_rows ; children together ; 1 son } } ; and { eq { hop { filter_eq { all_rows ; children together ; 1 son } ; name } ; norris church mailer } ; eq { hop { filter_eq { all_rows ; children together ; 1 son } ; deceased spouse } ; norman mailer } } }, interpretation: select the rows whose children together record fuzzily matches to 1 son . there is only one such row in the table . the name record of this unqiue row is norris church mailer . the deceased spouse record of this unqiue row is norman mailer .
Output: yes
Command: and { only { filter_eq { all_rows ; children together ; 1 son } } ; and { eq { hop { filter_eq { all_rows ; children together ; 1 son } ; name } ; norris church mailer } ; eq { hop { filter_eq { all_rows ; children together ; 1 son } ; deceased spouse } ; norman mailer } } }, interpretation: select the rows whose partner record fuzzily matches to stephanie vogt . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; partner ; stephanie vogt } } ; 2 }, interpretation: select the rows whose partner record fuzzily matches to stephanie vogt . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; partner ; stephanie vogt } } ; 2 }, interpretation: for the result records of all rows , most of them fuzzily match to safe .
Output: no
Command: most_eq { all_rows ; result ; safe }, interpretation: for the result records of all rows , most of them fuzzily match to safe .
Output: yes
Command: most_eq { all_rows ; result ; safe }, interpretation: select the rows whose director record fuzzily matches to pamela fryman . among these rows , select the rows whose original air date record fuzzily matches to april . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { filter_eq { all_rows ; director ; pamela fryman } ; original air date ; april } } ; 3 }, interpretation: select the rows whose director record fuzzily matches to pamela fryman . among these rows , select the rows whose original air date record fuzzily matches to april . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { filter_eq { all_rows ; director ; pamela fryman } ; original air date ; april } } ; 3 }, interpretation: for the result records of all rows , most of them fuzzily match to aus .
Output: no
Command: most_eq { all_rows ; result ; aus }, interpretation: for the result records of all rows , most of them fuzzily match to aus .
Output: yes
Command: most_eq { all_rows ; result ; aus }, interpretation: for the duration records of all rows , most of them are greater than 4:00 .
Output: no
Command: most_greater { all_rows ; duration ; 4:00 }, interpretation: for the duration records of all rows , most of them are greater than 4:00 .
Output: yes
Command: most_greater { all_rows ; duration ; 4:00 }, interpretation: for the date records of all rows , all of them fuzzily match to 26 august 1972 .
Output: no
Command: all_eq { all_rows ; date ; 26 august 1972 }, interpretation: for the date records of all rows , all of them fuzzily match to 26 august 1972 .
Output: yes
Command: all_eq { all_rows ; date ; 26 august 1972 }, interpretation: select the rows whose name record fuzzily matches to vilson ahmeti . take the term start record of this row . select the rows whose name record fuzzily matches to ilir meta . take the term start record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; name ; vilson ahmeti } ; term start } ; hop { filter_eq { all_rows ; name ; ilir meta } ; term start } }, interpretation: select the rows whose name record fuzzily matches to vilson ahmeti . take the term start record of this row . select the rows whose name record fuzzily matches to ilir meta . take the term start record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; name ; vilson ahmeti } ; term start } ; hop { filter_eq { all_rows ; name ; ilir meta } ; term start } }, interpretation: select the row whose 1st run record of all rows is 2nd minimum . the name record of this row is donny robinson ( usa ) .
Output: no
Command: eq { hop { nth_argmin { all_rows ; 1st run ; 2 } ; name } ; donny robinson ( usa ) }, interpretation: select the row whose 1st run record of all rows is 2nd minimum . the name record of this row is donny robinson ( usa ) .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; 1st run ; 2 } ; name } ; donny robinson ( usa ) }, interpretation: select the row whose juries record of all rows is maximum . the artist record of this row is danny saucedo .
Output: no
Command: eq { hop { argmax { all_rows ; juries } ; artist } ; danny saucedo }, interpretation: select the row whose juries record of all rows is maximum . the artist record of this row is danny saucedo .
Output: yes
Command: eq { hop { argmax { all_rows ; juries } ; artist } ; danny saucedo }, interpretation: select the rows whose name record fuzzily matches to tyler hansbrough . take the height record of this row . select the rows whose name record fuzzily matches to wes miller . take the height record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; name ; tyler hansbrough } ; height } ; hop { filter_eq { all_rows ; name ; wes miller } ; height } }, interpretation: select the rows whose name record fuzzily matches to tyler hansbrough . take the height record of this row . select the rows whose name record fuzzily matches to wes miller . take the height record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; name ; tyler hansbrough } ; height } ; hop { filter_eq { all_rows ; name ; wes miller } ; height } }, interpretation: select the rows whose opponent record fuzzily matches to alexey ignashov . take the round record of this row . select the rows whose opponent record fuzzily matches to lee hasdell . take the round record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; opponent ; alexey ignashov } ; round } ; hop { filter_eq { all_rows ; opponent ; lee hasdell } ; round } }, interpretation: select the rows whose opponent record fuzzily matches to alexey ignashov . take the round record of this row . select the rows whose opponent record fuzzily matches to lee hasdell . take the round record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; opponent ; alexey ignashov } ; round } ; hop { filter_eq { all_rows ; opponent ; lee hasdell } ; round } }, interpretation: select the row whose date of vacancy record of all rows is 1st minimum . the outgoing manager record of this row is thomas von heesen .
Output: no
Command: eq { hop { nth_argmin { all_rows ; date of vacancy ; 1 } ; outgoing manager } ; thomas von heesen }, interpretation: select the row whose date of vacancy record of all rows is 1st minimum . the outgoing manager record of this row is thomas von heesen .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; date of vacancy ; 1 } ; outgoing manager } ; thomas von heesen }, interpretation: the average of the attendance record of all rows is 22526 .
Output: no
Command: round_eq { avg { all_rows ; attendance } ; 22526 }, interpretation: the average of the attendance record of all rows is 22526 .
Output: yes
Command: round_eq { avg { all_rows ; attendance } ; 22526 }, interpretation: select the rows whose games record is greater than 48 . among these rows , select the rows whose goals record is equal to 27 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { filter_greater { all_rows ; games ; 48 } ; goals ; 27 } } ; 2 }, interpretation: select the rows whose games record is greater than 48 . among these rows , select the rows whose goals record is equal to 27 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { filter_greater { all_rows ; games ; 48 } ; goals ; 27 } } ; 2 }, interpretation: select the rows whose home team record fuzzily matches to fitzroy . take the home team score record of this row . select the rows whose home team record fuzzily matches to north melbourne . take the home team score record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; home team ; fitzroy } ; home team score } ; hop { filter_eq { all_rows ; home team ; north melbourne } ; home team score } }, interpretation: select the rows whose home team record fuzzily matches to fitzroy . take the home team score record of this row . select the rows whose home team record fuzzily matches to north melbourne . take the home team score record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; home team ; fitzroy } ; home team score } ; hop { filter_eq { all_rows ; home team ; north melbourne } ; home team score } }, interpretation: for the city of license records of all rows , all of them fuzzily match to texas .
Output: no
Command: all_eq { all_rows ; city of license ; texas }, interpretation: for the city of license records of all rows , all of them fuzzily match to texas .
Output: yes
Command: all_eq { all_rows ; city of license ; texas }, interpretation: the average of the time record of all rows is 1:09.00 .
Output: no
Command: round_eq { avg { all_rows ; time } ; 1:09.00 }, interpretation: the average of the time record of all rows is 1:09.00 .
Output: yes
Command: round_eq { avg { all_rows ; time } ; 1:09.00 }, interpretation: select the rows whose city record fuzzily matches to mansuriyah . there is only one such row in the table . the club record of this unqiue row is arabi .
Output: no
Command: and { only { filter_eq { all_rows ; city ; mansuriyah } } ; eq { hop { filter_eq { all_rows ; city ; mansuriyah } ; club } ; arabi } }, interpretation: select the rows whose city record fuzzily matches to mansuriyah . there is only one such row in the table . the club record of this unqiue row is arabi .
Output: yes
Command: and { only { filter_eq { all_rows ; city ; mansuriyah } } ; eq { hop { filter_eq { all_rows ; city ; mansuriyah } ; club } ; arabi } }, interpretation: select the rows whose class / type record fuzzily matches to passenger ship . there is only one such row in the table . the builder record of this unqiue row is john roach and son .
Output: no
Command: and { only { filter_eq { all_rows ; class / type ; passenger ship } } ; eq { hop { filter_eq { all_rows ; class / type ; passenger ship } ; builder } ; john roach and son } }, interpretation: select the rows whose class / type record fuzzily matches to passenger ship . there is only one such row in the table . the builder record of this unqiue row is john roach and son .
Output: yes
Command: and { only { filter_eq { all_rows ; class / type ; passenger ship } } ; eq { hop { filter_eq { all_rows ; class / type ; passenger ship } ; builder } ; john roach and son } }, interpretation: select the row whose pts record of all rows is maximum . the team record of this row is castres olympique .
Output: no
Command: eq { hop { argmax { all_rows ; pts } ; team } ; castres olympique }, interpretation: select the row whose pts record of all rows is maximum . the team record of this row is castres olympique .
Output: yes
Command: eq { hop { argmax { all_rows ; pts } ; team } ; castres olympique }, interpretation: select the rows whose competition record fuzzily matches to friendly . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; competition ; friendly } } ; 3 }, interpretation: select the rows whose competition record fuzzily matches to friendly . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; competition ; friendly } } ; 3 }, interpretation: for the type records of all rows , all of them fuzzily match to private .
Output: no
Command: all_eq { all_rows ; type ; private }, interpretation: select the row whose date record of all rows is minimum . the location attendance record of this row is verizon center 20173 .
Output: no
Command: eq { hop { argmin { all_rows ; date } ; location attendance } ; verizon center 20173 }, interpretation: select the row whose date record of all rows is minimum . the location attendance record of this row is verizon center 20173 .
Output: yes
Command: eq { hop { argmin { all_rows ; date } ; location attendance } ; verizon center 20173 }, interpretation: select the row whose total units sold record of all rows is 2nd maximum . the title record of this row is wii fit .
Output: no
Command: eq { hop { nth_argmax { all_rows ; total units sold ; 2 } ; title } ; wii fit }, interpretation: select the row whose total units sold record of all rows is 2nd maximum . the title record of this row is wii fit .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; total units sold ; 2 } ; title } ; wii fit }, interpretation: select the row whose time record of all rows is minimum . the athlete record of this row is veronica campbell - brown .
Output: no
Command: eq { hop { argmin { all_rows ; time } ; athlete } ; veronica campbell - brown }, interpretation: select the row whose time record of all rows is minimum . the athlete record of this row is veronica campbell - brown .
Output: yes
Command: eq { hop { argmin { all_rows ; time } ; athlete } ; veronica campbell - brown }, interpretation: select the rows whose directed by record fuzzily matches to levie isaacks . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; directed by ; levie isaacks } } ; 5 }, interpretation: select the rows whose directed by record fuzzily matches to levie isaacks . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; directed by ; levie isaacks } } ; 5 }, interpretation: select the rows whose laps record is equal to 39 . there is only one such row in the table . the year record of this unqiue row is 1949 .
Output: no
Command: and { only { filter_eq { all_rows ; laps ; 39 } } ; eq { hop { filter_eq { all_rows ; laps ; 39 } ; year } ; 1949 } }, interpretation: select the rows whose laps record is equal to 39 . there is only one such row in the table . the year record of this unqiue row is 1949 .
Output: yes
Command: and { only { filter_eq { all_rows ; laps ; 39 } } ; eq { hop { filter_eq { all_rows ; laps ; 39 } ; year } ; 1949 } }, interpretation: select the rows whose ratings record is greater than 2.00 million . there is only one such row in the table . the episode record of this unqiue row is 1 .
Output: no
Command: and { only { filter_greater { all_rows ; ratings ; 2.00 million } } ; eq { hop { filter_greater { all_rows ; ratings ; 2.00 million } ; episode } ; 1 } }, interpretation: select the rows whose ratings record is greater than 2.00 million . there is only one such row in the table . the episode record of this unqiue row is 1 .
Output: yes
Command: and { only { filter_greater { all_rows ; ratings ; 2.00 million } } ; eq { hop { filter_greater { all_rows ; ratings ; 2.00 million } ; episode } ; 1 } }, interpretation: for the court surface records of all rows , most of them fuzzily match to hard .
Output: no
Command: most_eq { all_rows ; court surface ; hard }, interpretation: for the court surface records of all rows , most of them fuzzily match to hard .
Output: yes
Command: most_eq { all_rows ; court surface ; hard }, interpretation: select the rows whose written by record fuzzily matches to amy engelberg & wendy engelberg . there is only one such row in the table . the title record of this unqiue row is dead model walking .
Output: no
Command: and { only { filter_eq { all_rows ; written by ; amy engelberg & wendy engelberg } } ; eq { hop { filter_eq { all_rows ; written by ; amy engelberg & wendy engelberg } ; title } ; dead model walking } }, interpretation: select the rows whose written by record fuzzily matches to amy engelberg & wendy engelberg . there is only one such row in the table . the title record of this unqiue row is dead model walking .
Output: yes
Command: and { only { filter_eq { all_rows ; written by ; amy engelberg & wendy engelberg } } ; eq { hop { filter_eq { all_rows ; written by ; amy engelberg & wendy engelberg } ; title } ; dead model walking } }, interpretation: select the rows whose championship record fuzzily matches to world snooker championship . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; championship ; world snooker championship } } ; 2 }, interpretation: select the rows whose championship record fuzzily matches to world snooker championship . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; championship ; world snooker championship } } ; 2 }, interpretation: select the rows whose result record is equal to 0 . there is only one such row in the table . the week record of this unqiue row is 15 .
Output: no
Command: and { only { filter_eq { all_rows ; result ; 0 } } ; eq { hop { filter_eq { all_rows ; result ; 0 } ; week } ; 15 } }, interpretation: select the rows whose result record is equal to 0 . there is only one such row in the table . the week record of this unqiue row is 15 .
Output: yes
Command: and { only { filter_eq { all_rows ; result ; 0 } } ; eq { hop { filter_eq { all_rows ; result ; 0 } ; week } ; 15 } }, interpretation: select the rows whose batting partners record fuzzily matches to tim curtis and tom moody . take the runs record of this row . select the rows whose batting partners record fuzzily matches to chris read and andrew harris . take the runs record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; batting partners ; tim curtis and tom moody } ; runs } ; hop { filter_eq { all_rows ; batting partners ; chris read and andrew harris } ; runs } }, interpretation: select the rows whose batting partners record fuzzily matches to tim curtis and tom moody . take the runs record of this row . select the rows whose batting partners record fuzzily matches to chris read and andrew harris . take the runs record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; batting partners ; tim curtis and tom moody } ; runs } ; hop { filter_eq { all_rows ; batting partners ; chris read and andrew harris } ; runs } }, interpretation: select the rows whose game site record fuzzily matches to forbes field . the minimum date record of these rows is sunday september 20 .
Output: no
Command: eq { min { filter_eq { all_rows ; game site ; forbes field } ; date } ; sunday september 20 }, interpretation: select the rows whose game site record fuzzily matches to forbes field . the minimum date record of these rows is sunday september 20 .
Output: yes