niryuu/sni-each-converted huggingface dataset:
Command: and { only { filter_eq { filter_eq { all_rows ; years ; k - 6 } ; suburb / town ; villawood } } ; eq { hop { filter_eq { filter_eq { all_rows ; years ; k - 6 } ; suburb / town ; villawood } ; founded } ; 1955 } }, interpretation: select the rows whose years record fuzzily matches to k - 6 . among these rows , select the rows whose suburb / town record fuzzily matches to villawood . there is only one such row in the table . the founded record of this unqiue row is 1955 .
Output: yes
Command: and { only { filter_eq { filter_eq { all_rows ; years ; k - 6 } ; suburb / town ; villawood } } ; eq { hop { filter_eq { filter_eq { all_rows ; years ; k - 6 } ; suburb / town ; villawood } ; founded } ; 1955 } }, interpretation: select the rows whose location in park record fuzzily matches to baja ridge . select the row whose year first opened record of these rows is 1st minimum . the current name record of this row is revolution .
Output: no
Command: eq { hop { nth_argmin { filter_eq { all_rows ; location in park ; baja ridge } ; year first opened ; 1 } ; current name } ; revolution }, interpretation: select the rows whose location in park record fuzzily matches to baja ridge . select the row whose year first opened record of these rows is 1st minimum . the current name record of this row is revolution .
Output: yes
Command: eq { hop { nth_argmin { filter_eq { all_rows ; location in park ; baja ridge } ; year first opened ; 1 } ; current name } ; revolution }, interpretation: select the rows whose county record fuzzily matches to mobile . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; county ; mobile } } ; 3 }, interpretation: select the rows whose county record fuzzily matches to mobile . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; county ; mobile } } ; 3 }, interpretation: select the rows whose series record fuzzily matches to australian formula 3 championship - national class . the average of the points record of these rows is 160.5 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; series ; australian formula 3 championship - national class } ; points } ; 160.5 }, interpretation: select the rows whose series record fuzzily matches to australian formula 3 championship - national class . the average of the points record of these rows is 160.5 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; series ; australian formula 3 championship - national class } ; points } ; 160.5 }, interpretation: select the rows whose to par record fuzzily matches to +1 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; to par ; +1 } } ; 2 }, interpretation: select the rows whose to par record fuzzily matches to +1 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; to par ; +1 } } ; 2 }, interpretation: select the rows whose high points record fuzzily matches to m williams . there is only one such row in the table . the date record of this unqiue row is may 2 .
Output: no
Command: and { only { filter_eq { all_rows ; high points ; m williams } } ; eq { hop { filter_eq { all_rows ; high points ; m williams } ; date } ; may 2 } }, interpretation: select the rows whose high points record fuzzily matches to m williams . there is only one such row in the table . the date record of this unqiue row is may 2 .
Output: yes
Command: and { only { filter_eq { all_rows ; high points ; m williams } } ; eq { hop { filter_eq { all_rows ; high points ; m williams } ; date } ; may 2 } }, interpretation: select the rows whose wood record fuzzily matches to green . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; wood ; green } }, interpretation: select the rows whose wood record fuzzily matches to green . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; wood ; green } }, interpretation: select the row whose races record of all rows is 2nd minimum . the season record of this row is 2010 .
Output: no
Command: eq { hop { nth_argmin { all_rows ; races ; 2 } ; season } ; 2010 }, interpretation: select the row whose races record of all rows is 2nd minimum . the season record of this row is 2010 .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; races ; 2 } ; season } ; 2010 }, interpretation: the average of the crowd record of all rows is 14000 .
Output: no
Command: round_eq { avg { all_rows ; crowd } ; 14000 }, interpretation: the average of the crowd record of all rows is 14000 .
Output: yes
Command: round_eq { avg { all_rows ; crowd } ; 14000 }, interpretation: select the rows whose call sign record fuzzily matches to ktaw . take the frequency mhz record of this row . select the rows whose call sign record fuzzily matches to ktml . take the frequency mhz record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; call sign ; ktaw } ; frequency mhz } ; hop { filter_eq { all_rows ; call sign ; ktml } ; frequency mhz } }, interpretation: select the rows whose call sign record fuzzily matches to ktaw . take the frequency mhz record of this row . select the rows whose call sign record fuzzily matches to ktml . take the frequency mhz record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; call sign ; ktaw } ; frequency mhz } ; hop { filter_eq { all_rows ; call sign ; ktml } ; frequency mhz } }, interpretation: select the row whose year established record of all rows is 2nd maximum . the members record of this row is university of otago .
Output: no
Command: eq { hop { nth_argmax { all_rows ; year established ; 2 } ; members } ; university of otago }, interpretation: select the row whose year established record of all rows is 2nd maximum . the members record of this row is university of otago .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; year established ; 2 } ; members } ; university of otago }, interpretation: select the rows whose opponent record fuzzily matches to iowa . take the date record of this row . select the rows whose opponent record fuzzily matches to 4 michigan . take the date record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; opponent ; iowa } ; date } ; hop { filter_eq { all_rows ; opponent ; 4 michigan } ; date } }, interpretation: select the rows whose opponent record fuzzily matches to iowa . take the date record of this row . select the rows whose opponent record fuzzily matches to 4 michigan . take the date record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; opponent ; iowa } ; date } ; hop { filter_eq { all_rows ; opponent ; 4 michigan } ; date } }, interpretation: select the rows whose erp w record is equal to 10 . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; erp w ; 10 } } ; 4 }, interpretation: select the rows whose erp w record is equal to 10 . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; erp w ; 10 } } ; 4 }, interpretation: for the surface records of all rows , most of them fuzzily match to clay .
Output: no
Command: most_eq { all_rows ; surface ; clay }, interpretation: select the rows whose director record fuzzily matches to silvio caiozzi . the minimum year ( ceremony ) record of these rows is 1990 : ( 63rd ) .
Output: no
Command: eq { min { filter_eq { all_rows ; director ; silvio caiozzi } ; year ( ceremony ) } ; 1990 : ( 63rd ) }, interpretation: select the rows whose director record fuzzily matches to silvio caiozzi . the minimum year ( ceremony ) record of these rows is 1990 : ( 63rd ) .
Output: yes
Command: eq { min { filter_eq { all_rows ; director ; silvio caiozzi } ; year ( ceremony ) } ; 1990 : ( 63rd ) }, interpretation: select the row whose us viewers ( million ) record of all rows is 2nd maximum . the title record of this row is weekend at bobby 's .
Output: no
Command: eq { hop { nth_argmax { all_rows ; us viewers ( million ) ; 2 } ; title } ; weekend at bobby 's }, interpretation: select the row whose us viewers ( million ) record of all rows is 2nd maximum . the title record of this row is weekend at bobby 's .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; us viewers ( million ) ; 2 } ; title } ; weekend at bobby 's }, interpretation: select the rows whose partner record fuzzily matches to helena suková . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; partner ; helena suková } } ; 3 }, interpretation: select the rows whose partner record fuzzily matches to helena suková . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; partner ; helena suková } } ; 3 }, interpretation: select the rows whose of candidates nominated record is greater than 50 . there is only one such row in the table . the election record of this unqiue row is 2008 .
Output: no
Command: and { only { filter_greater { all_rows ; of candidates nominated ; 50 } } ; eq { hop { filter_greater { all_rows ; of candidates nominated ; 50 } ; election } ; 2008 } }, interpretation: select the rows whose of candidates nominated record is greater than 50 . there is only one such row in the table . the election record of this unqiue row is 2008 .
Output: yes
Command: and { only { filter_greater { all_rows ; of candidates nominated ; 50 } } ; eq { hop { filter_greater { all_rows ; of candidates nominated ; 50 } ; election } ; 2008 } }, interpretation: select the rows whose year record is equal to 2012 . select the row whose worldwide gross record of these rows is maximum . the movie record of this row is ek tha tiger .
Output: no
Command: eq { hop { argmax { filter_eq { all_rows ; year ; 2012 } ; worldwide gross } ; movie } ; ek tha tiger }, interpretation: select the rows whose year record is equal to 2012 . select the row whose worldwide gross record of these rows is maximum . the movie record of this row is ek tha tiger .
Output: yes
Command: eq { hop { argmax { filter_eq { all_rows ; year ; 2012 } ; worldwide gross } ; movie } ; ek tha tiger }, interpretation: select the rows whose candidates record fuzzily matches to unopposed . there is only one such row in the table . the incumbent record of this unqiue row is bob brady .
Output: no
Command: and { only { filter_eq { all_rows ; candidates ; unopposed } } ; eq { hop { filter_eq { all_rows ; candidates ; unopposed } ; incumbent } ; bob brady } }, interpretation: select the rows whose candidates record fuzzily matches to unopposed . there is only one such row in the table . the incumbent record of this unqiue row is bob brady .
Output: yes
Command: and { only { filter_eq { all_rows ; candidates ; unopposed } } ; eq { hop { filter_eq { all_rows ; candidates ; unopposed } ; incumbent } ; bob brady } }, interpretation: select the rows whose viewers ( households in millions ) record is less than 18 . the number of such rows is 3 .
Output: no
Command: eq { count { filter_less { all_rows ; viewers ( households in millions ) ; 18 } } ; 3 }, interpretation: select the rows whose viewers ( households in millions ) record is less than 18 . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_less { all_rows ; viewers ( households in millions ) ; 18 } } ; 3 }, interpretation: select the rows whose result record fuzzily matches to lose . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; result ; lose } } ; 5 }, interpretation: select the rows whose result record fuzzily matches to lose . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; result ; lose } } ; 5 }, interpretation: select the row whose silver record of all rows is 2nd maximum . the nation record of this row is kazakhstan .
Output: no
Command: eq { hop { nth_argmax { all_rows ; silver ; 2 } ; nation } ; kazakhstan }, interpretation: select the row whose silver record of all rows is 2nd maximum . the nation record of this row is kazakhstan .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; silver ; 2 } ; nation } ; kazakhstan }, interpretation: select the rows whose position record fuzzily matches to tight end . the average of the height record of these rows is 6 ' 4 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; position ; tight end } ; height } ; 6 ' 4 }, interpretation: select the rows whose position record fuzzily matches to tight end . the average of the height record of these rows is 6 ' 4 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; position ; tight end } ; height } ; 6 ' 4 }, interpretation: for the crowd records of all rows , most of them are greater than 10000 .
Output: no
Command: most_greater { all_rows ; crowd ; 10000 }, interpretation: for the crowd records of all rows , most of them are greater than 10000 .
Output: yes
Command: most_greater { all_rows ; crowd ; 10000 }, interpretation: select the row whose total record of all rows is 1st maximum . the nation record of this row is china .
Output: no
Command: eq { hop { nth_argmax { all_rows ; total ; 1 } ; nation } ; china }, interpretation: select the row whose total record of all rows is 1st maximum . the nation record of this row is china .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; total ; 1 } ; nation } ; china }, interpretation: select the rows whose season record fuzzily matches to 2010 - 11 . take the rank ( league / conference ) record of this row . select the rows whose season record fuzzily matches to 2012 - 13 . take the rank ( league / conference ) record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; season ; 2010 - 11 } ; rank ( league / conference ) } ; hop { filter_eq { all_rows ; season ; 2012 - 13 } ; rank ( league / conference ) } }, interpretation: select the rows whose season record fuzzily matches to 2010 - 11 . take the rank ( league / conference ) record of this row . select the rows whose season record fuzzily matches to 2012 - 13 . take the rank ( league / conference ) record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; season ; 2010 - 11 } ; rank ( league / conference ) } ; hop { filter_eq { all_rows ; season ; 2012 - 13 } ; rank ( league / conference ) } }, interpretation: select the rows whose away team record fuzzily matches to south melbourne . take the away team score record of this row . select the rows whose away team record fuzzily matches to north melbourne . take the away team score record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; away team ; south melbourne } ; away team score } ; hop { filter_eq { all_rows ; away team ; north melbourne } ; away team score } }, interpretation: select the rows whose away team record fuzzily matches to south melbourne . take the away team score record of this row . select the rows whose away team record fuzzily matches to north melbourne . take the away team score record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; away team ; south melbourne } ; away team score } ; hop { filter_eq { all_rows ; away team ; north melbourne } ; away team score } }, interpretation: select the rows whose home team score record is greater than 12 . among these rows , select the rows whose crowd record is less than 20000 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_less { filter_greater { all_rows ; home team score ; 12 } ; crowd ; 20000 } } ; 2 }, interpretation: select the rows whose home team score record is greater than 12 . among these rows , select the rows whose crowd record is less than 20000 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_less { filter_greater { all_rows ; home team score ; 12 } ; crowd ; 20000 } } ; 2 }, interpretation: for the type records of all rows , most of them fuzzily match to private .
Output: no
Command: most_eq { all_rows ; type ; private }, interpretation: for the type records of all rows , most of them fuzzily match to private .
Output: yes
Command: most_eq { all_rows ; type ; private }, interpretation: select the rows whose attendance record fuzzily matches to n / a . there is only one such row in the table . the date record of this unqiue row is october 25 .
Output: no
Command: and { only { filter_eq { all_rows ; attendance ; n / a } } ; eq { hop { filter_eq { all_rows ; attendance ; n / a } ; date } ; october 25 } }, interpretation: select the rows whose attendance record fuzzily matches to n / a . there is only one such row in the table . the date record of this unqiue row is october 25 .
Output: yes
Command: and { only { filter_eq { all_rows ; attendance ; n / a } } ; eq { hop { filter_eq { all_rows ; attendance ; n / a } ; date } ; october 25 } }, interpretation: for the nationality records of all rows , all of them fuzzily match to united states .
Output: no
Command: all_eq { all_rows ; nationality ; united states }, interpretation: for the nationality records of all rows , all of them fuzzily match to united states .
Output: yes
Command: all_eq { all_rows ; nationality ; united states }, interpretation: the sum of the 1995 regional record of all rows is 63.6 .
Output: no
Command: round_eq { sum { all_rows ; 1995 regional } ; 63.6 }, interpretation: the sum of the 1995 regional record of all rows is 63.6 .
Output: yes
Command: round_eq { sum { all_rows ; 1995 regional } ; 63.6 }, interpretation: for the played records of all rows , all of them are equal to 18 .
Output: no
Command: all_eq { all_rows ; played ; 18 }, interpretation: for the played records of all rows , all of them are equal to 18 .
Output: yes
Command: all_eq { all_rows ; played ; 18 }, interpretation: select the row whose founded record of all rows is 2nd maximum . the institution record of this row is high point university .
Output: no
Command: eq { hop { nth_argmax { all_rows ; founded ; 2 } ; institution } ; high point university }, interpretation: select the row whose founded record of all rows is 2nd maximum . the institution record of this row is high point university .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; founded ; 2 } ; institution } ; high point university }, interpretation: select the rows whose high rebounds record fuzzily matches to m cage . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; high rebounds ; m cage } } ; 3 }, interpretation: select the rows whose high rebounds record fuzzily matches to m cage . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; high rebounds ; m cage } } ; 3 }, interpretation: select the rows whose game site record fuzzily matches to gillette stadium . the average of the result record of these rows is 26 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; game site ; gillette stadium } ; result } ; 26 }, interpretation: select the rows whose game site record fuzzily matches to gillette stadium . the average of the result record of these rows is 26 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; game site ; gillette stadium } ; result } ; 26 }, interpretation: select the rows whose team record fuzzily matches to team penske . the number of such rows is 5 .
Output: no
Command: eq { count { filter_eq { all_rows ; team ; team penske } } ; 5 }, interpretation: select the rows whose team record fuzzily matches to team penske . the number of such rows is 5 .
Output: yes
Command: eq { count { filter_eq { all_rows ; team ; team penske } } ; 5 }, interpretation: select the row whose attendance record of all rows is 1st maximum . the date record of this row is june 25 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; date } ; june 25 }, interpretation: select the row whose attendance record of all rows is 1st maximum . the date record of this row is june 25 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; date } ; june 25 }, interpretation: the average of the number of seats record of all rows is 44 .
Output: no
Command: round_eq { avg { all_rows ; number of seats } ; 44 }, interpretation: the average of the number of seats record of all rows is 44 .
Output: yes
Command: round_eq { avg { all_rows ; number of seats } ; 44 }, interpretation: select the row whose winnings record of all rows is maximum . the year record of this row is 2004 .
Output: no
Command: eq { hop { argmax { all_rows ; winnings } ; year } ; 2004 }, interpretation: select the row whose winnings record of all rows is maximum . the year record of this row is 2004 .
Output: yes
Command: eq { hop { argmax { all_rows ; winnings } ; year } ; 2004 }, interpretation: select the row whose assistant professors record of all rows is minimum . the lecturers record of this row is 7 .
Output: no
Command: eq { hop { argmin { all_rows ; assistant professors } ; lecturers } ; 7 }, interpretation: select the row whose assistant professors record of all rows is minimum . the lecturers record of this row is 7 .
Output: yes
Command: eq { hop { argmin { all_rows ; assistant professors } ; lecturers } ; 7 }, interpretation: the sum of the podiums record of all rows is 18 .
Output: no
Command: round_eq { sum { all_rows ; podiums } ; 18 }, interpretation: the sum of the podiums record of all rows is 18 .
Output: yes
Command: round_eq { sum { all_rows ; podiums } ; 18 }, interpretation: select the rows whose riaa certification record fuzzily matches to gold . there is only one such row in the table . the year record of this unqiue row is 1979 .
Output: no
Command: and { only { filter_eq { all_rows ; riaa certification ; gold } } ; eq { hop { filter_eq { all_rows ; riaa certification ; gold } ; year } ; 1979 } }, interpretation: select the rows whose riaa certification record fuzzily matches to gold . there is only one such row in the table . the year record of this unqiue row is 1979 .
Output: yes
Command: and { only { filter_eq { all_rows ; riaa certification ; gold } } ; eq { hop { filter_eq { all_rows ; riaa certification ; gold } ; year } ; 1979 } }, interpretation: select the row whose gold record of all rows is 2nd maximum . the nation record of this row is japan .
Output: no
Command: eq { hop { nth_argmax { all_rows ; gold ; 2 } ; nation } ; japan }, interpretation: select the row whose gold record of all rows is 2nd maximum . the nation record of this row is japan .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; gold ; 2 } ; nation } ; japan }, interpretation: select the rows whose artist record fuzzily matches to demos beke . take the points record of this row . select the rows whose artist record fuzzily matches to lucas christodolou . 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 ; artist ; demos beke } ; points } ; hop { filter_eq { all_rows ; artist ; lucas christodolou } ; points } }, interpretation: select the rows whose artist record fuzzily matches to demos beke . take the points record of this row . select the rows whose artist record fuzzily matches to lucas christodolou . 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 ; artist ; demos beke } ; points } ; hop { filter_eq { all_rows ; artist ; lucas christodolou } ; points } }, interpretation: select the rows whose fa cup record is greater than 0 . select the row whose championship record of these rows is 2nd maximum . the name record of this row is stern john .
Output: no
Command: eq { hop { nth_argmax { filter_greater { all_rows ; fa cup ; 0 } ; championship ; 2 } ; name } ; stern john }, interpretation: select the rows whose fa cup record is greater than 0 . select the row whose championship record of these rows is 2nd maximum . the name record of this row is stern john .
Output: yes
Command: eq { hop { nth_argmax { filter_greater { all_rows ; fa cup ; 0 } ; championship ; 2 } ; name } ; stern john }, interpretation: select the rows whose opponent record fuzzily matches to dallas cowboys . take the attendance record of this row . select the rows whose opponent record fuzzily matches to philadelphia eagles . take the attendance record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; opponent ; dallas cowboys } ; attendance } ; hop { filter_eq { all_rows ; opponent ; philadelphia eagles } ; attendance } }, interpretation: select the rows whose opponent record fuzzily matches to dallas cowboys . take the attendance record of this row . select the rows whose opponent record fuzzily matches to philadelphia eagles . take the attendance record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; opponent ; dallas cowboys } ; attendance } ; hop { filter_eq { all_rows ; opponent ; philadelphia eagles } ; attendance } }, interpretation: for the played records of all rows , all of them are equal to 34 .
Output: no
Command: all_eq { all_rows ; played ; 34 }, interpretation: for the played records of all rows , all of them are equal to 34 .
Output: yes
Command: all_eq { all_rows ; played ; 34 }, interpretation: for the award ceremony records of all rows , most of them fuzzily match to drama desk award .
Output: no
Command: most_eq { all_rows ; award ceremony ; drama desk award }, interpretation: for the award ceremony records of all rows , most of them fuzzily match to drama desk award .
Output: yes
Command: most_eq { all_rows ; award ceremony ; drama desk award }, interpretation: select the rows whose country record fuzzily matches to india . the number of such rows is 4 .
Output: no
Command: eq { count { filter_eq { all_rows ; country ; india } } ; 4 }, interpretation: select the rows whose country record fuzzily matches to india . the number of such rows is 4 .
Output: yes
Command: eq { count { filter_eq { all_rows ; country ; india } } ; 4 }, interpretation: select the row whose silver record of all rows is 2nd maximum . the nation record of this row is canada .
Output: no
Command: eq { hop { nth_argmax { all_rows ; silver ; 2 } ; nation } ; canada }, interpretation: select the row whose silver record of all rows is 2nd maximum . the nation record of this row is canada .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; silver ; 2 } ; nation } ; canada }, interpretation: select the row whose attendance record of all rows is 3rd maximum . the game record of this row is 6 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; game } ; 6 }, interpretation: select the row whose attendance record of all rows is 3rd maximum . the game record of this row is 6 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; game } ; 6 }, interpretation: select the rows whose christians record fuzzily matches to 41 % . there is only one such row in the table . the ethnic group record of this unqiue row is kunama .
Output: no
Command: and { only { filter_eq { all_rows ; christians ; 41 % } } ; eq { hop { filter_eq { all_rows ; christians ; 41 % } ; ethnic group } ; kunama } }, interpretation: select the rows whose christians record fuzzily matches to 41 % . there is only one such row in the table . the ethnic group record of this unqiue row is kunama .
Output: yes
Command: and { only { filter_eq { all_rows ; christians ; 41 % } } ; eq { hop { filter_eq { all_rows ; christians ; 41 % } ; ethnic group } ; kunama } }, interpretation: select the row whose uncut run time record of all rows is 2nd maximum . the title record of this row is addio zio tom .
Output: no
Command: eq { hop { nth_argmax { all_rows ; uncut run time ; 2 } ; title } ; addio zio tom }, interpretation: select the row whose uncut run time record of all rows is 2nd maximum . the title record of this row is addio zio tom .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; uncut run time ; 2 } ; title } ; addio zio tom }, interpretation: select the row whose enrollment ( 2010 ) record of all rows is 2nd maximum . the school record of this row is heritage .
Output: no
Command: eq { hop { nth_argmax { all_rows ; enrollment ( 2010 ) ; 2 } ; school } ; heritage }, interpretation: select the row whose enrollment ( 2010 ) record of all rows is 2nd maximum . the school record of this row is heritage .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; enrollment ( 2010 ) ; 2 } ; school } ; heritage }, interpretation: select the rows whose date ( s ) record fuzzily matches to september 2006 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; date ( s ) ; september 2006 } } ; 2 }, interpretation: select the rows whose date ( s ) record fuzzily matches to september 2006 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; date ( s ) ; september 2006 } } ; 2 }, interpretation: for the long records of all rows , most of them are less than 10 .
Output: no
Command: most_less { all_rows ; long ; 10 }, interpretation: for the long records of all rows , most of them are less than 10 .
Output: yes
Command: most_less { all_rows ; long ; 10 }, interpretation: select the rows whose nation record is arbitrary . the number of such rows is 9 .
Output: no
Command: eq { count { filter_all { all_rows ; nation } } ; 9 }, interpretation: select the rows whose isbn us record fuzzily matches to unknown . there is only one such row in the table . the title record of this unqiue row is mission to venus .
Output: no
Command: and { only { filter_eq { all_rows ; isbn us ; unknown } } ; eq { hop { filter_eq { all_rows ; isbn us ; unknown } ; title } ; mission to venus } }, interpretation: select the rows whose isbn us record fuzzily matches to unknown . there is only one such row in the table . the title record of this unqiue row is mission to venus .
Output: yes
Command: and { only { filter_eq { all_rows ; isbn us ; unknown } } ; eq { hop { filter_eq { all_rows ; isbn us ; unknown } ; title } ; mission to venus } }, interpretation: select the rows whose directed by record fuzzily matches to john kretchmer . there is only one such row in the table .
Output: no
Command: only { filter_eq { all_rows ; directed by ; john kretchmer } }, interpretation: select the rows whose directed by record fuzzily matches to john kretchmer . there is only one such row in the table .
Output: yes
Command: only { filter_eq { all_rows ; directed by ; john kretchmer } }, interpretation: the sum of the enrollment record of all rows is 8,600 .
Output: no
Command: round_eq { sum { all_rows ; enrollment } ; 8,600 }, interpretation: the sum of the enrollment record of all rows is 8,600 .
Output: yes
Command: round_eq { sum { all_rows ; enrollment } ; 8,600 }, interpretation: select the rows whose opponent record fuzzily matches to toronto maple leafs . the sum of the attendance record of these rows is 38,002 .
Output: no
Command: round_eq { sum { filter_eq { all_rows ; opponent ; toronto maple leafs } ; attendance } ; 38,002 }, interpretation: select the rows whose opponent record fuzzily matches to toronto maple leafs . the sum of the attendance record of these rows is 38,002 .
Output: yes
Command: round_eq { sum { filter_eq { all_rows ; opponent ; toronto maple leafs } ; attendance } ; 38,002 }, interpretation: for the outcome records of all rows , most of them fuzzily match to winner .
Output: no
Command: most_eq { all_rows ; outcome ; winner }, interpretation: select the rows whose titles record is equal to 1 . the number of such rows is 2 .
Output: no
Command: eq { count { filter_eq { all_rows ; titles ; 1 } } ; 2 }, interpretation: select the rows whose titles record is equal to 1 . the number of such rows is 2 .
Output: yes
Command: eq { count { filter_eq { all_rows ; titles ; 1 } } ; 2 }, interpretation: select the rows whose location record fuzzily matches to tifton . there is only one such row in the table . the institution record of this unqiue row is abraham baldwin agricultural college .
Output: no
Command: and { only { filter_eq { all_rows ; location ; tifton } } ; eq { hop { filter_eq { all_rows ; location ; tifton } ; institution } ; abraham baldwin agricultural college } }, interpretation: select the rows whose location record fuzzily matches to tifton . there is only one such row in the table . the institution record of this unqiue row is abraham baldwin agricultural college .
Output: yes
Command: and { only { filter_eq { all_rows ; location ; tifton } } ; eq { hop { filter_eq { all_rows ; location ; tifton } ; institution } ; abraham baldwin agricultural college } }, interpretation: for the class records of all rows , most of them fuzzily match to cat a .
Output: no
Command: most_eq { all_rows ; class ; cat a }, interpretation: for the class records of all rows , most of them fuzzily match to cat a .
Output: yes
Command: most_eq { all_rows ; class ; cat a }, interpretation: select the rows whose surface record fuzzily matches to clay . the number of such rows is 3 .
Output: no
Command: eq { count { filter_eq { all_rows ; surface ; clay } } ; 3 }, interpretation: select the rows whose surface record fuzzily matches to clay . the number of such rows is 3 .
Output: yes
Command: eq { count { filter_eq { all_rows ; surface ; clay } } ; 3 }, interpretation: select the rows whose vessel record fuzzily matches to smeaton array . take the max speed record of this row . select the rows whose vessel record fuzzily matches to marianarray . take the max speed record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; vessel ; smeaton array } ; max speed } ; hop { filter_eq { all_rows ; vessel ; marianarray } ; max speed } }, interpretation: select the rows whose vessel record fuzzily matches to smeaton array . take the max speed record of this row . select the rows whose vessel record fuzzily matches to marianarray . take the max speed record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; vessel ; smeaton array } ; max speed } ; hop { filter_eq { all_rows ; vessel ; marianarray } ; max speed } }, interpretation: select the rows whose opponent record fuzzily matches to debi purcell . take the round record of this row . select the rows whose opponent record fuzzily matches to tomomi sunaba . 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 ; debi purcell } ; round } ; hop { filter_eq { all_rows ; opponent ; tomomi sunaba } ; round } }, interpretation: select the rows whose opponent record fuzzily matches to debi purcell . take the round record of this row . select the rows whose opponent record fuzzily matches to tomomi sunaba . 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 ; debi purcell } ; round } ; hop { filter_eq { all_rows ; opponent ; tomomi sunaba } ; round } }, interpretation: for the games played records of all rows , all of them are equal to 36 .
Output: no
Command: all_eq { all_rows ; games played ; 36 }, interpretation: for the games played records of all rows , all of them are equal to 36 .
Output: yes
Command: all_eq { all_rows ; games played ; 36 }, interpretation: select the row whose roll record of all rows is 2nd maximum . the name record of this row is albany school .
Output: no
Command: eq { hop { nth_argmax { all_rows ; roll ; 2 } ; name } ; albany school }, interpretation: select the row whose roll record of all rows is 2nd maximum . the name record of this row is albany school .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; roll ; 2 } ; name } ; albany school }, interpretation: select the row whose date record of all rows is 2nd maximum . the tournament record of this row is makarska , croatia itf 75000 .
Output: no
Command: eq { hop { nth_argmax { all_rows ; date ; 2 } ; tournament } ; makarska , croatia itf 75000 }, interpretation: select the row whose date record of all rows is 2nd maximum . the tournament record of this row is makarska , croatia itf 75000 .
Output: yes
Command: eq { hop { nth_argmax { all_rows ; date ; 2 } ; tournament } ; makarska , croatia itf 75000 }, interpretation: for the result records of all rows , most of them fuzzily match to won .
Output: no
Command: most_eq { all_rows ; result ; won }, interpretation: select the rows whose colony record fuzzily matches to chandernagore . take the de facto transfer record of this row . select the rows whose colony record fuzzily matches to karikal . take the de facto transfer record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; colony ; chandernagore } ; de facto transfer } ; hop { filter_eq { all_rows ; colony ; karikal } ; de facto transfer } }, interpretation: select the rows whose colony record fuzzily matches to chandernagore . take the de facto transfer record of this row . select the rows whose colony record fuzzily matches to karikal . take the de facto transfer record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; colony ; chandernagore } ; de facto transfer } ; hop { filter_eq { all_rows ; colony ; karikal } ; de facto transfer } }, interpretation: for the result records of all rows , most of them fuzzily match to won .
Output: no
Command: most_eq { all_rows ; result ; won }, interpretation: for the transmission records of all rows , most of them fuzzily match to analog & digital .
Output: no
Command: most_eq { all_rows ; transmission ; analog & digital }, interpretation: for the transmission records of all rows , most of them fuzzily match to analog & digital .
Output: yes
Command: most_eq { all_rows ; transmission ; analog & digital }, interpretation: select the rows whose tournament record fuzzily matches to australian international . take the year record of this row . select the rows whose tournament record fuzzily matches to canadian open . take the year record of this row . the first record is 1 year larger than the second record .
Output: no
Command: eq { diff { hop { filter_eq { all_rows ; tournament ; australian international } ; year } ; hop { filter_eq { all_rows ; tournament ; canadian open } ; year } } ; 1 year }, interpretation: select the rows whose tournament record fuzzily matches to australian international . take the year record of this row . select the rows whose tournament record fuzzily matches to canadian open . take the year record of this row . the first record is 1 year larger than the second record .
Output: yes
Command: eq { diff { hop { filter_eq { all_rows ; tournament ; australian international } ; year } ; hop { filter_eq { all_rows ; tournament ; canadian open } ; year } } ; 1 year }, interpretation: for the venue records of all rows , most of them fuzzily match to estadio cuscatlán , san salvador , el salvador .
Output: no
Command: most_eq { all_rows ; venue ; estadio cuscatlán , san salvador , el salvador }, interpretation: for the venue records of all rows , most of them fuzzily match to estadio cuscatlán , san salvador , el salvador .
Output: yes
Command: most_eq { all_rows ; venue ; estadio cuscatlán , san salvador , el salvador }, interpretation: select the rows whose team record fuzzily matches to america . the average of the average fencers rank record of these rows is 6.58 .
Output: no
Command: round_eq { avg { filter_eq { all_rows ; team ; america } ; average fencers rank } ; 6.58 }, interpretation: select the rows whose team record fuzzily matches to america . the average of the average fencers rank record of these rows is 6.58 .
Output: yes
Command: round_eq { avg { filter_eq { all_rows ; team ; america } ; average fencers rank } ; 6.58 }, interpretation: select the row whose vuelta wins record of all rows is maximum . the country record of this row is spain .
Output: no
Command: eq { hop { argmax { all_rows ; vuelta wins } ; country } ; spain }, interpretation: select the row whose vuelta wins record of all rows is maximum . the country record of this row is spain .
Output: yes
Command: eq { hop { argmax { all_rows ; vuelta wins } ; country } ; spain }, interpretation: for the language records of all rows , most of them fuzzily match to hindi .
Output: no
Command: most_eq { all_rows ; language ; hindi }, interpretation: for the language records of all rows , most of them fuzzily match to hindi .
Output: yes
Command: most_eq { all_rows ; language ; hindi }, interpretation: for the current streak records of all rows , most of them fuzzily match to w .
Output: no
Command: most_eq { all_rows ; current streak ; w }, interpretation: for the current streak records of all rows , most of them fuzzily match to w .
Output: yes
Command: most_eq { all_rows ; current streak ; w }, interpretation: select the rows whose tournament record fuzzily matches to masters tournament . take the top - 5 record of this row . select the rows whose tournament record fuzzily matches to us open . take the top - 5 record of this row . the first record is greater than the second record .
Output: no
Command: greater { hop { filter_eq { all_rows ; tournament ; masters tournament } ; top - 5 } ; hop { filter_eq { all_rows ; tournament ; us open } ; top - 5 } }, interpretation: select the rows whose tournament record fuzzily matches to masters tournament . take the top - 5 record of this row . select the rows whose tournament record fuzzily matches to us open . take the top - 5 record of this row . the first record is greater than the second record .
Output: yes
Command: greater { hop { filter_eq { all_rows ; tournament ; masters tournament } ; top - 5 } ; hop { filter_eq { all_rows ; tournament ; us open } ; top - 5 } }, interpretation: select the rows whose general classification record fuzzily matches to igor antón . among these rows , select the rows whose winner record fuzzily matches to markus fothen . there is only one such row in the table . the stage record of this unqiue row is 5 .
Output: no
Command: and { only { filter_eq { filter_eq { all_rows ; general classification ; igor antón } ; winner ; markus fothen } } ; eq { hop { filter_eq { filter_eq { all_rows ; general classification ; igor antón } ; winner ; markus fothen } ; stage } ; 5 } }, interpretation: select the rows whose general classification record fuzzily matches to igor antón . among these rows , select the rows whose winner record fuzzily matches to markus fothen . there is only one such row in the table . the stage record of this unqiue row is 5 .
Output: yes
Command: and { only { filter_eq { filter_eq { all_rows ; general classification ; igor antón } ; winner ; markus fothen } } ; eq { hop { filter_eq { filter_eq { all_rows ; general classification ; igor antón } ; winner ; markus fothen } ; stage } ; 5 } }, interpretation: for the wheels records of all rows , most of them fuzzily match to 0-4 .
Output: no
Command: most_eq { all_rows ; wheels ; 0-4 }, interpretation: for the wheels records of all rows , most of them fuzzily match to 0-4 .
Output: yes
Command: most_eq { all_rows ; wheels ; 0-4 }, interpretation: select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the tournament record of this unqiue row is trivandrum .
Output: no
Command: and { only { filter_eq { all_rows ; surface ; clay } } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; tournament } ; trivandrum } }, interpretation: select the rows whose surface record fuzzily matches to clay . there is only one such row in the table . the tournament record of this unqiue row is trivandrum .
Output: yes
Command: and { only { filter_eq { all_rows ; surface ; clay } } ; eq { hop { filter_eq { all_rows ; surface ; clay } ; tournament } ; trivandrum } }, interpretation: select the row whose year record of all rows is 4th minimum . the against record of this row is georgia .
Output: no
Command: eq { hop { nth_argmin { all_rows ; year ; 4 } ; against } ; georgia }, interpretation: select the row whose year record of all rows is 4th minimum . the against record of this row is georgia .
Output: yes
Command: eq { hop { nth_argmin { all_rows ; year ; 4 } ; against } ; georgia }, interpretation: select the row whose attendance record of all rows is maximum . the opponent record of this row is college all - stars at chicago .
Output: no
Command: eq { hop { argmax { all_rows ; attendance } ; opponent } ; college all - stars at chicago }, interpretation: select the row whose attendance record of all rows is maximum . the opponent record of this row is college all - stars at chicago .
Output: yes
Command: eq { hop { argmax { all_rows ; attendance } ; opponent } ; college all - stars at chicago }, interpretation: the average of the goals per match record of all rows is 1.052 .
Output: no
Command: round_eq { avg { all_rows ; goals per match } ; 1.052 }, interpretation: the average of the goals per match record of all rows is 1.052 .
Output: yes
Command: round_eq { avg { all_rows ; goals per match } ; 1.052 }, interpretation: select the rows whose location record fuzzily matches to arnhem . take the date ( from ) record of this row . select the rows whose location record fuzzily matches to nijmegen . take the date ( from ) record of this row . the first record is less than the second record .
Output: no
Command: less { hop { filter_eq { all_rows ; location ; arnhem } ; date ( from ) } ; hop { filter_eq { all_rows ; location ; nijmegen } ; date ( from ) } }, interpretation: select the rows whose location record fuzzily matches to arnhem . take the date ( from ) record of this row . select the rows whose location record fuzzily matches to nijmegen . take the date ( from ) record of this row . the first record is less than the second record .
Output: yes
Command: less { hop { filter_eq { all_rows ; location ; arnhem } ; date ( from ) } ; hop { filter_eq { all_rows ; location ; nijmegen } ; date ( from ) } }, interpretation: select the rows whose no record is arbitrary . the number of such rows is 11 .
Output: no
Command: eq { count { filter_all { all_rows ; no } } ; 11 }, interpretation: select the rows whose no record is arbitrary . the number of such rows is 11 .
Output: yes
Command: eq { count { filter_all { all_rows ; no } } ; 11 }, interpretation: the average of the goals against record of all rows is 53 .
Output: no
Command: round_eq { avg { all_rows ; goals against } ; 53 }, interpretation: the average of the goals against record of all rows is 53 .
Output: yes
Command: round_eq { avg { all_rows ; goals against } ; 53 }, interpretation: select the rows whose college record fuzzily matches to clark university . there is only one such row in the table . the player record of this unqiue row is millard hamilton .
Output: no
Command: and { only { filter_eq { all_rows ; college ; clark university } } ; eq { hop { filter_eq { all_rows ; college ; clark university } ; player } ; millard hamilton } }, interpretation: select the rows whose college record fuzzily matches to clark university . there is only one such row in the table . the player record of this unqiue row is millard hamilton .
Output: yes
Command: and { only { filter_eq { all_rows ; college ; clark university } } ; eq { hop { filter_eq { all_rows ; college ; clark university } ; player } ; millard hamilton } }, interpretation: the average of the total record of all rows is 19.71 .
Output: no
Command: round_eq { avg { all_rows ; total } ; 19.71 }, interpretation: the average of the total record of all rows is 19.71 .
Output: yes
Command: round_eq { avg { all_rows ; total } ; 19.71 }, interpretation: select the rows whose venue record fuzzily matches to mcg . take the crowd record of this row . select the rows whose venue record fuzzily matches to vfl park . take the crowd record of this row . the first record is greater than the second record .
Output: no