niryuu/sni-each-converted huggingface dataset:
for the attendance records of all rows , most of them are greater than 51,000 .
Output: most_greater { all_rows ; attendance ; 51,000 }
select the rows whose player record fuzzily matches to laura diaz . take the score record of this row . select the rows whose player record fuzzily matches to kristy mcpherson . take the score record of this row . the first record is less than the second record . the score record of the first row is 66 . the score record of the second row is 67 .
Output: and { less { hop { filter_eq { all_rows ; player ; laura diaz } ; score } ; hop { filter_eq { all_rows ; player ; kristy mcpherson } ; score } } ; and { eq { hop { filter_eq { all_rows ; player ; laura diaz } ; score } ; 66 } ; eq { hop { filter_eq { all_rows ; player ; kristy mcpherson } ; score } ; 67 } } }
select the row whose slalom record of all rows is 3rd maximum . the season record of this row is 1996 .
Output: eq { hop { nth_argmax { all_rows ; slalom ; 3 } ; season } ; 1996 }
the sum of the runs record of all rows is 3423 .
Output: round_eq { sum { all_rows ; runs } ; 3423 }
select the row whose popular votes record of all rows is maximum . the candidate record of this row is colleen bonniwell .
Output: eq { hop { argmax { all_rows ; popular votes } ; candidate } ; colleen bonniwell }
select the row whose capacity record of all rows is 2nd maximum . the stadium record of this row is somerset park .
Output: eq { hop { nth_argmax { all_rows ; capacity ; 2 } ; stadium } ; somerset park }
for the average score records of all rows , most of them are greater than 82 .
Output: most_greater { all_rows ; average score ; 82 }
select the rows whose year record is equal to 1999 . there is only one such row in the table . the version record of this unqiue row is album version .
Output: and { only { filter_eq { all_rows ; year ; 1999 } } ; eq { hop { filter_eq { all_rows ; year ; 1999 } ; version } ; album version } }
select the rows whose recipients and nominees record fuzzily matches to gillian anderson . among these rows , select the rows whose result record fuzzily matches to nominated . the number of such rows is 4 .
Output: eq { count { filter_eq { filter_eq { all_rows ; recipients and nominees ; gillian anderson } ; result ; nominated } } ; 4 }
select the rows whose player record fuzzily matches to mike hercus . for the tries records of these rows , most of them are equal to 0 .
Output: most_eq { filter_eq { all_rows ; player ; mike hercus } ; tries ; 0 }
select the row whose year ( ceremony ) record of all rows is minimum . the film title used in nomination record of this row is the child of man .
Output: eq { hop { argmin { all_rows ; year ( ceremony ) } ; film title used in nomination } ; the child of man }
select the rows whose sixes record is equal to 8 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; sixes ; 8 } } ; 2 }
select the rows whose location record fuzzily matches to london , uk . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; location ; london , uk } } ; 3 }
select the rows whose wins record is less than 5 . there is only one such row in the table . the club record of this unqiue row is jfc olimps rīga .
Output: and { only { filter_less { all_rows ; wins ; 5 } } ; eq { hop { filter_less { all_rows ; wins ; 5 } ; club } ; jfc olimps rīga } }
the minimum original airdate record of all rows is november 02 .
Output: eq { min { all_rows ; original airdate } ; november 02 }
select the rows whose crowd record is greater than 20000 . the number of such rows is 4 .
Output: eq { count { filter_greater { all_rows ; crowd ; 20000 } } ; 4 }
for the sat 29 may records of all rows , all of them fuzzily match to cancelled no time .
Output: all_eq { all_rows ; sat 29 may ; cancelled no time }
select the row whose height in ft record of all rows is maximum . the player record of this row is carroll , joe barry joe barry carroll .
Output: eq { hop { argmax { all_rows ; height in ft } ; player } ; carroll , joe barry joe barry carroll }
select the rows whose home team record fuzzily matches to townsville crocodiles . take the score record of this row . select the rows whose home team record fuzzily matches to cairns taipans . take the score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; home team ; townsville crocodiles } ; score } ; hop { filter_eq { all_rows ; home team ; cairns taipans } ; score } }
select the rows whose year record is equal to 2005 . there is only one such row in the table . the group record of this unqiue row is method fest independent film festival .
Output: and { only { filter_eq { all_rows ; year ; 2005 } } ; eq { hop { filter_eq { all_rows ; year ; 2005 } ; group } ; method fest independent film festival } }
select the rows whose venue record fuzzily matches to beirut . among these rows , select the rows whose result record does not match to win . the number of such rows is 2 .
Output: eq { count { filter_not_eq { filter_eq { all_rows ; venue ; beirut } ; result ; win } } ; 2 }
for the address records of all rows , most of them fuzzily match to gilbert , az .
Output: most_eq { all_rows ; address ; gilbert , az }
the average of the games record of all rows is 26.8 .
Output: round_eq { avg { all_rows ; games } ; 26.8 }
select the row whose took office record of all rows is 2nd maximum . the name record of this row is lamberto zannier .
Output: eq { hop { nth_argmax { all_rows ; took office ; 2 } ; name } ; lamberto zannier }
select the rows whose candidate record fuzzily matches to ron strynadka . take the votes record of this row . select the rows whose candidate record fuzzily matches to patricia cordner . take the votes record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; candidate ; ron strynadka } ; votes } ; hop { filter_eq { all_rows ; candidate ; patricia cordner } ; votes } }
select the row whose first elected record of all rows is 1st minimum . the district record of this row is texas 1 .
Output: eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; district } ; texas 1 }
select the row whose no of installments record of all rows is 2nd maximum . the book series record of this row is the hardy boys .
Output: eq { hop { nth_argmax { all_rows ; no of installments ; 2 } ; book series } ; the hardy boys }
the average of the capacity record of all rows is 4736 .
Output: round_eq { avg { all_rows ; capacity } ; 4736 }
select the rows whose competition record fuzzily matches to friendly match . there is only one such row in the table . the date record of this unqiue row is 23 march 1993 .
Output: and { only { filter_eq { all_rows ; competition ; friendly match } } ; eq { hop { filter_eq { all_rows ; competition ; friendly match } ; date } ; 23 march 1993 } }
for the platforms records of all rows , most of them are equal to 2 .
Output: most_eq { all_rows ; platforms ; 2 }
select the row whose total record of all rows is maximum . the name record of this row is giuseppe meazza .
Output: eq { hop { argmax { all_rows ; total } ; name } ; giuseppe meazza }
select the rows whose week record is less than or equal to 5 . the average of the attendance record of these rows is 74,287 .
Output: round_eq { avg { filter_less_eq { all_rows ; week ; 5 } ; attendance } ; 74,287 }
select the rows whose commissioned record fuzzily matches to 1930 . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; commissioned ; 1930 } } ; 7 }
select the row whose year record of all rows is minimum . the winner record of this row is deep gold .
Output: eq { hop { argmin { all_rows ; year } ; winner } ; deep gold }
select the rows whose original air date record fuzzily matches to february 2010 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; original air date ; february 2010 } } ; 3 }
the sum of the crowd record of all rows is 86100 .
Output: round_eq { sum { all_rows ; crowd } ; 86100 }
the sum of the score record of all rows is 29 .
Output: round_eq { sum { all_rows ; score } ; 29 }
select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is thomas j bliley , jr .
Output: eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; thomas j bliley , jr }
select the rows whose region record fuzzily matches to central . select the row whose population ( 2000 census ) record of these rows is 1st maximum . the village record of this row is mangilao .
Output: eq { hop { nth_argmax { filter_eq { all_rows ; region ; central } ; population ( 2000 census ) ; 1 } ; village } ; mangilao }
select the rows whose gold record is equal to 3 . among these rows , select the rows whose silver record is equal to 4 . there is only one such row in the table . the nation record of this unqiue row is china .
Output: and { only { filter_eq { filter_eq { all_rows ; gold ; 3 } ; silver ; 4 } } ; eq { hop { filter_eq { filter_eq { all_rows ; gold ; 3 } ; silver ; 4 } ; nation } ; china } }
select the rows whose nation record fuzzily matches to japan . take the bronze record of this row . select the rows whose nation record fuzzily matches to indonesia . take the bronze record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; nation ; japan } ; bronze } ; hop { filter_eq { all_rows ; nation ; indonesia } ; bronze } }
for the 2008 status records of all rows , most of them fuzzily match to re - election .
Output: most_eq { all_rows ; 2008 status ; re - election }
select the row whose first elected record of all rows is 2nd maximum . the incumbent record of this row is louis capozzoli .
Output: eq { hop { nth_argmax { all_rows ; first elected ; 2 } ; incumbent } ; louis capozzoli }
select the rows whose result record fuzzily matches to draw . there is only one such row in the table . the date record of this unqiue row is 8 september 2007 .
Output: and { only { filter_eq { all_rows ; result ; draw } } ; eq { hop { filter_eq { all_rows ; result ; draw } ; date } ; 8 september 2007 } }
select the rows whose total record is greater than or equal to 10 . the number of such rows is 3 .
Output: eq { count { filter_greater_eq { all_rows ; total ; 10 } } ; 3 }
the average of the laps record of all rows is 19 .
Output: round_eq { avg { all_rows ; laps } ; 19 }
select the row whose points record of all rows is maximum . the player record of this row is dave allerdice .
Output: eq { hop { argmax { all_rows ; points } ; player } ; dave allerdice }
for the station records of all rows , most of them fuzzily match to kstc - tv .
Output: most_eq { all_rows ; station ; kstc - tv }
select the rows whose matches record is equal to 10 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; matches ; 10 } } ; 3 }
the sum of the votes record of all rows is 2176341 .
Output: round_eq { sum { all_rows ; votes } ; 2176341 }
select the rows whose tracks record fuzzily matches to that 's how the whole thing started . take the length record of this row . select the rows whose tracks record fuzzily matches to do what you do . take the length record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; tracks ; that 's how the whole thing started } ; length } ; hop { filter_eq { all_rows ; tracks ; do what you do } ; length } }
select the rows whose club / team record fuzzily matches to brisbane broncos . take the established record of this row . select the rows whose club / team record fuzzily matches to brisbane bandits . take the established record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; club / team ; brisbane broncos } ; established } ; hop { filter_eq { all_rows ; club / team ; brisbane bandits } ; established } }
the 2nd maximum frequency record of all rows is 933 mhz .
Output: eq { nth_max { all_rows ; frequency ; 2 } ; 933 mhz }
select the row whose winning score record of all rows is 2nd maximum . the tournament record of this row is corning classic .
Output: eq { hop { nth_argmax { all_rows ; winning score ; 2 } ; tournament } ; corning classic }
for the shot pct records of all rows , most of them are less than 80 % .
Output: most_less { all_rows ; shot pct ; 80 % }
for the venue records of all rows , most of them fuzzily match to india .
Output: most_eq { all_rows ; venue ; india }
select the row whose date of vacancy record of all rows is 1st minimum . the outgoing manager record of this row is jürgen röber .
Output: eq { hop { nth_argmin { all_rows ; date of vacancy ; 1 } ; outgoing manager } ; jürgen röber }
select the rows whose date record fuzzily matches to 1 april 2006 . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; date ; 1 april 2006 } } ; 6 }
select the rows whose location attendance record fuzzily matches to fedexforum . there is only one such row in the table . the date record of this unqiue row is march 7 .
Output: and { only { filter_eq { all_rows ; location attendance ; fedexforum } } ; eq { hop { filter_eq { all_rows ; location attendance ; fedexforum } ; date } ; march 7 } }
select the rows whose player record fuzzily matches to john senst . take the pick record of this row . select the rows whose player record fuzzily matches to paul brown . take the pick record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; john senst } ; pick } ; hop { filter_eq { all_rows ; player ; paul brown } ; pick } }
select the rows whose name record fuzzily matches to chopra . take the started record of this row . select the rows whose name record fuzzily matches to routledge . take the started record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; name ; chopra } ; started } ; hop { filter_eq { all_rows ; name ; routledge } ; started } }
for the established records of all rows , most of them are greater than or equal to 2000 .
Output: most_greater_eq { all_rows ; established ; 2000 }
select the row whose year left record of all rows is minimum . the school record of this row is south central ( union mills ) .
Output: eq { hop { argmin { all_rows ; year left } ; school } ; south central ( union mills ) }
the sum of the quantity record of all rows is 427 .
Output: round_eq { sum { all_rows ; quantity } ; 427 }
the average of the first elected record of all rows is 1996 .
Output: round_eq { avg { all_rows ; first elected } ; 1996 }
select the rows whose result record fuzzily matches to won . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; result ; won } } ; 3 }
for the championship records of all rows , most of them fuzzily match to french open .
Output: most_eq { all_rows ; championship ; french open }
select the rows whose high points record fuzzily matches to powe . the sum of the high points record of these rows is 39 .
Output: round_eq { sum { filter_eq { all_rows ; high points ; powe } ; high points } ; 39 }
select the rows whose round record is equal to 7 . the average of the overall record of these rows is 245 .
Output: round_eq { avg { filter_eq { all_rows ; round ; 7 } ; overall } ; 245 }
select the rows whose opponent record fuzzily matches to bull shaw . take the time record of this row . select the rows whose opponent record fuzzily matches to robert burnell . take the time record of this row . the first record is greater than the second record . the time record of the first row is 20:00 . the time record of the second row is 1:23 .
Output: and { greater { hop { filter_eq { all_rows ; opponent ; bull shaw } ; time } ; hop { filter_eq { all_rows ; opponent ; robert burnell } ; time } } ; and { eq { hop { filter_eq { all_rows ; opponent ; bull shaw } ; time } ; 20:00 } ; eq { hop { filter_eq { all_rows ; opponent ; robert burnell } ; time } ; 1:23 } } }
for the crowd records of all rows , all of them are greater than 10000 .
Output: all_greater { all_rows ; crowd ; 10000 }
for the northumberland senior cup records of all rows , most of them fuzzily match to newcastle united reserves .
Output: most_eq { all_rows ; northumberland senior cup ; newcastle united reserves }
select the rows whose high points record fuzzily matches to damon stoudamire . among these rows , select the rows whose team record fuzzily matches to miami . there is only one such row in the table . the date record of this unqiue row is april 5 .
Output: and { only { filter_eq { filter_eq { all_rows ; high points ; damon stoudamire } ; team ; miami } } ; eq { hop { filter_eq { filter_eq { all_rows ; high points ; damon stoudamire } ; team ; miami } ; date } ; april 5 } }
the average of the react record of all rows is .19025 .
Output: round_eq { avg { all_rows ; react } ; .19025 }
select the rows whose date ( s ) conducted record fuzzily matches to 27 october 2010 . take the green record of this row . select the rows whose date ( s ) conducted record fuzzily matches to 24 november 2010 . take the green record of this row . the second record is 1 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; date ( s ) conducted ; 27 october 2010 } ; green } ; hop { filter_eq { all_rows ; date ( s ) conducted ; 24 november 2010 } ; green } } ; -1 }
select the rows whose title record fuzzily matches to gandhi . take the gross record of this row . select the rows whose title record fuzzily matches to the dark crystal . take the gross record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; title ; gandhi } ; gross } ; hop { filter_eq { all_rows ; title ; the dark crystal } ; gross } }
select the rows whose engine record fuzzily matches to v6 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; engine ; v6 } } ; 4 }
select the row whose passengers record of all rows is maximum . the city record of this row is atlanta , ga .
Output: eq { hop { argmax { all_rows ; passengers } ; city } ; atlanta , ga }
select the rows whose president record fuzzily matches to chris maclean . there is only one such row in the table . the year record of this unqiue row is 2013 .
Output: and { only { filter_eq { all_rows ; president ; chris maclean } } ; eq { hop { filter_eq { all_rows ; president ; chris maclean } ; year } ; 2013 } }
the average of the goals record of all rows is 15 .
Output: round_eq { avg { all_rows ; goals } ; 15 }
select the rows whose season record is greater than 2005 . among these rows , select the rows whose teams record is equal to 16 . the number of such rows is 4 .
Output: eq { count { filter_eq { filter_greater { all_rows ; season ; 2005 } ; teams ; 16 } } ; 4 }
for the result records of all rows , most of them fuzzily match to 2 .
Output: most_eq { all_rows ; result ; 2 }
select the rows whose team ( s ) record fuzzily matches to dingman brothers racing . there is only one such row in the table . the year record of this unqiue row is 1987 .
Output: and { only { filter_eq { all_rows ; team ( s ) ; dingman brothers racing } } ; eq { hop { filter_eq { all_rows ; team ( s ) ; dingman brothers racing } ; year } ; 1987 } }
select the row whose attendance record of all rows is maximum . the opponent record of this row is new york giants .
Output: eq { hop { argmax { all_rows ; attendance } ; opponent } ; new york giants }
the average of the crowd record of all rows is 17944 .
Output: round_eq { avg { all_rows ; crowd } ; 17944 }
for the analogue power records of all rows , most of them are greater than 1000 kw .
Output: most_greater { all_rows ; analogue power ; 1000 kw }
select the rows whose release date record fuzzily matches to 2008 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; release date ; 2008 } } ; 2 }
select the rows whose lost ( pp ) record is equal to 1 . there is only one such row in the table . the team ( equipo ) record of this unqiue row is tauro fc .
Output: and { only { filter_eq { all_rows ; lost ( pp ) ; 1 } } ; eq { hop { filter_eq { all_rows ; lost ( pp ) ; 1 } ; team ( equipo ) } ; tauro fc } }
the 2nd maximum revenue in usd record of all rows is 469.2 billion . the company record of the row with 2nd maximum revenue in usd record is walmart .
Output: and { eq { nth_max { all_rows ; revenue in usd ; 2 } ; 469.2 billion } ; eq { hop { nth_argmax { all_rows ; revenue in usd ; 2 } ; company } ; walmart } }
select the rows whose rank record is less than or equal to 7 . for the goals records of these rows , most of them are greater than 10 .
Output: most_greater { filter_less_eq { all_rows ; rank ; 7 } ; goals ; 10 }
select the rows whose province record fuzzily matches to santiago de cuba . take the density record of this row . select the rows whose province record fuzzily matches to pinar del río . take the density record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; province ; santiago de cuba } ; density } ; hop { filter_eq { all_rows ; province ; pinar del río } ; density } }
select the rows whose name record fuzzily matches to coby fleener . take the round record of this row . select the rows whose name record fuzzily matches to josh chapman . take the round record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; name ; coby fleener } ; round } ; hop { filter_eq { all_rows ; name ; josh chapman } ; round } }
for the score records of all rows , all of them fuzzily match to 1 .
Output: all_eq { all_rows ; score ; 1 }
for the result records of all rows , most of them are greater than 0 .
Output: most_greater { all_rows ; result ; 0 }
select the row whose ends won record of all rows is maximum . the skip ( club ) record of this row is chris gardner ( renfrew ) .
Output: eq { hop { argmax { all_rows ; ends won } ; skip ( club ) } ; chris gardner ( renfrew ) }
select the rows whose res record fuzzily matches to win . among these rows , select the rows whose event record fuzzily matches to extreme challenge . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; res ; win } ; event ; extreme challenge } } ; 2 }
for the rank records of all rows , most of them are greater than 10 .
Output: most_greater { all_rows ; rank ; 10 }
select the rows whose high assists record does not match to devin harris . there is only one such row in the table . the date record of this unqiue row is january 8 .
Output: and { only { filter_not_eq { all_rows ; high assists ; devin harris } } ; eq { hop { filter_not_eq { all_rows ; high assists ; devin harris } ; date } ; january 8 } }
select the rows whose date record fuzzily matches to 11 october . take the result record of this row . select the rows whose date record fuzzily matches to 19 october . take the result record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; 11 october } ; result } ; hop { filter_eq { all_rows ; date ; 19 october } ; result } }
select the rows whose winner record fuzzily matches to learco guerra . the 2nd minimum date record of these rows is 18 may .
Output: eq { nth_min { filter_eq { all_rows ; winner ; learco guerra } ; date ; 2 } ; 18 may }
select the rows whose method record fuzzily matches to draw . there is only one such row in the table . the res record of this unqiue row is draw .
Output: and { only { filter_eq { all_rows ; method ; draw } } ; eq { hop { filter_eq { all_rows ; method ; draw } ; res } ; draw } }
select the rows whose winner record fuzzily matches to rangers . for the score records of these rows , most of them are greater than 0 .
Output: most_greater { filter_eq { all_rows ; winner ; rangers } ; score ; 0 }
select the row whose attendance record of all rows is maximum . the date record of this row is march 14 .
Output: eq { hop { argmax { all_rows ; attendance } ; date } ; march 14 }
the sum of the quantity made record of all rows is 22 .
Output: round_eq { sum { all_rows ; quantity made } ; 22 }
select the row whose opening week nett gross record of all rows is maximum . the movie record of this row is chennai express .
Output: eq { hop { argmax { all_rows ; opening week nett gross } ; movie } ; chennai express }
select the rows whose total elected 2001 record is equal to 0 . there is only one such row in the table . the party record of this unqiue row is independents .
Output: and { only { filter_eq { all_rows ; total elected 2001 ; 0 } } ; eq { hop { filter_eq { all_rows ; total elected 2001 ; 0 } ; party } ; independents } }
for the tournament location records of all rows , most of them fuzzily match to beaver meadow golf course .
Output: most_eq { all_rows ; tournament location ; beaver meadow golf course }
select the row whose final round record of all rows is minimum . the city / state record of this row is texas .
Output: eq { hop { argmin { all_rows ; final round } ; city / state } ; texas }
the average of the weeks at 1 record of all rows is 4.8 .
Output: round_eq { avg { all_rows ; weeks at 1 } ; 4.8 }
select the rows whose department record is arbitrary . the number of such rows is 7 .
Output: eq { count { filter_all { all_rows ; department } } ; 7 }
for the high assists records of all rows , most of them fuzzily match to imari sawyer .
Output: most_eq { all_rows ; high assists ; imari sawyer }
select the rows whose order record fuzzily matches to cardinal - bishop . there is only one such row in the table . the elector record of this unqiue row is francesco moricotti prignani .
Output: and { only { filter_eq { all_rows ; order ; cardinal - bishop } } ; eq { hop { filter_eq { all_rows ; order ; cardinal - bishop } ; elector } ; francesco moricotti prignani } }
select the row whose date record of all rows is maximum . the number & name record of this row is no 07005 .
Output: eq { hop { argmax { all_rows ; date } ; number & name } ; no 07005 }
select the rows whose wins record is equal to 1 . there is only one such row in the table . the player record of this unqiue row is harold henning .
Output: and { only { filter_eq { all_rows ; wins ; 1 } } ; eq { hop { filter_eq { all_rows ; wins ; 1 } ; player } ; harold henning } }
select the rows whose institution record fuzzily matches to university of dayton . take the founded record of this row . select the rows whose institution record fuzzily matches to university of memphis . take the founded record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; institution ; university of dayton } ; founded } ; hop { filter_eq { all_rows ; institution ; university of memphis } ; founded } }
the average of the wins record of all rows is 0 .
Output: round_eq { avg { all_rows ; wins } ; 0 }
select the rows whose year record fuzzily matches to 2011 . take the tournaments played record of this row . select the rows whose year record fuzzily matches to 2007 . take the tournaments played record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; year ; 2011 } ; tournaments played } ; hop { filter_eq { all_rows ; year ; 2007 } ; tournaments played } }
select the row whose goal difference record of all rows is 3rd maximum . the club record of this row is real oviedo .
Output: eq { hop { nth_argmax { all_rows ; goal difference ; 3 } ; club } ; real oviedo }
for the time records of all rows , most of them fuzzily match to n / a .
Output: most_eq { all_rows ; time ; n / a }
select the rows whose score record is equal to 64 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; score ; 64 } } ; 2 }
select the row whose qual 2 record of all rows is 2nd minimum . the name record of this row is paul tracy .
Output: eq { hop { nth_argmin { all_rows ; qual 2 ; 2 } ; name } ; paul tracy }
select the rows whose result record fuzzily matches to re - elected . for the party records of these rows , all of them fuzzily match to democratic .
Output: all_eq { filter_eq { all_rows ; result ; re - elected } ; party ; democratic }
select the rows whose player record fuzzily matches to james . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; player ; james } } ; 2 }
select the rows whose manager record fuzzily matches to bob didier . there is only one such row in the table . the year record of this unqiue row is 1977 .
Output: and { only { filter_eq { all_rows ; manager ; bob didier } } ; eq { hop { filter_eq { all_rows ; manager ; bob didier } ; year } ; 1977 } }
the sum of the aggregate record of all rows is 20-39 .
Output: round_eq { sum { all_rows ; aggregate } ; 20-39 }
for the year joined records of all rows , most of them are equal to 1971 .
Output: most_eq { all_rows ; year joined ; 1971 }
for the position records of all rows , most of them are less than or equal to equal to or less than 15th .
Output: most_less_eq { all_rows ; position ; equal to or less than 15th }
select the row whose date record of all rows is 2nd maximum . the tournament record of this row is cincinnati , united states .
Output: eq { hop { nth_argmax { all_rows ; date ; 2 } ; tournament } ; cincinnati , united states }
select the rows whose round record fuzzily matches to 7th . there is only one such row in the table . the player record of this unqiue row is jim norton .
Output: and { only { filter_eq { all_rows ; round ; 7th } } ; eq { hop { filter_eq { all_rows ; round ; 7th } ; player } ; jim norton } }
the sum of the won record of all rows is 128 .
Output: round_eq { sum { all_rows ; won } ; 128 }
select the rows whose written by record fuzzily matches to stan berkowitz . there is only one such row in the table . the title record of this unqiue row is simple .
Output: and { only { filter_eq { all_rows ; written by ; stan berkowitz } } ; eq { hop { filter_eq { all_rows ; written by ; stan berkowitz } ; title } ; simple } }
select the row whose points record of all rows is maximum . the performer record of this row is kiev connolly & the missing passengers .
Output: eq { hop { argmax { all_rows ; points } ; performer } ; kiev connolly & the missing passengers }
select the row whose quantity record of all rows is maximum . the make record of this row is gm new look .
Output: eq { hop { argmax { all_rows ; quantity } ; make } ; gm new look }
select the rows whose name record fuzzily matches to over the deel . take the age record of this row . select the rows whose name record fuzzily matches to captain dibble . take the age record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; name ; over the deel } ; age } ; hop { filter_eq { all_rows ; name ; captain dibble } ; age } }
select the rows whose date record fuzzily matches to sept 15 . take the attendance record of this row . select the rows whose date record fuzzily matches to nov 3 . take the attendance record of this row . the first record is greater than the second record . the opponent record of the first row is green bay packers . the opponent record of the second row is chicago bears .
Output: and { greater { hop { filter_eq { all_rows ; date ; sept 15 } ; attendance } ; hop { filter_eq { all_rows ; date ; nov 3 } ; attendance } } ; and { eq { hop { filter_eq { all_rows ; date ; sept 15 } ; opponent } ; green bay packers } ; eq { hop { filter_eq { all_rows ; date ; nov 3 } ; opponent } ; chicago bears } } }
select the row whose expected year of completion record of all rows is 1st maximum . the country record of this row is india .
Output: eq { hop { nth_argmax { all_rows ; expected year of completion ; 1 } ; country } ; india }
for the website records of all rows , most of them fuzzily match to english .
Output: most_eq { all_rows ; website ; english }
select the rows whose nation record fuzzily matches to romania . take the bronze record of this row . select the rows whose nation record fuzzily matches to soviet union . take the bronze record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; nation ; romania } ; bronze } ; hop { filter_eq { all_rows ; nation ; soviet union } ; bronze } }
select the rows whose crowd record is equal to 15000 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; crowd ; 15000 } } ; 3 }
select the rows whose results record fuzzily matches to retired . there is only one such row in the table . the incumbent record of this unqiue row is ernest istook .
Output: and { only { filter_eq { all_rows ; results ; retired } } ; eq { hop { filter_eq { all_rows ; results ; retired } ; incumbent } ; ernest istook } }
select the rows whose club record fuzzily matches to rcd español . take the wins record of this row . select the rows whose club record fuzzily matches to pontevedra cf . take the wins record of this row . the first record is 1 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; club ; rcd español } ; wins } ; hop { filter_eq { all_rows ; club ; pontevedra cf } ; wins } } ; 1 }
select the rows whose type record fuzzily matches to retirement . there is only one such row in the table . the name record of this unqiue row is machlas .
Output: and { only { filter_eq { all_rows ; type ; retirement } } ; eq { hop { filter_eq { all_rows ; type ; retirement } ; name } ; machlas } }
select the rows whose director record fuzzily matches to chuck jones . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; director ; chuck jones } } ; 7 }
select the rows whose winner record fuzzily matches to edd kimber . take the premiere record of this row . select the rows whose winner record fuzzily matches to joanne wheatley . take the premiere record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; winner ; edd kimber } ; premiere } ; hop { filter_eq { all_rows ; winner ; joanne wheatley } ; premiere } }
for the environment records of all rows , most of them fuzzily match to open air .
Output: most_eq { all_rows ; environment ; open air }
select the rows whose school colors record fuzzily matches to orange & black . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; school colors ; orange & black } } ; 2 }
select the rows whose writer ( s ) record fuzzily matches to joseph kuhr . among these rows , select the rows whose us original airdate record fuzzily matches to march . the number of such rows is 1 .
Output: eq { count { filter_eq { filter_eq { all_rows ; writer ( s ) ; joseph kuhr } ; us original airdate ; march } } ; 1 }
select the rows whose opponent record fuzzily matches to indianapolis colts . the sum of the attendance record of these rows is 100807 .
Output: round_eq { sum { filter_eq { all_rows ; opponent ; indianapolis colts } ; attendance } ; 100807 }
select the rows whose us viewers ( million ) record is greater than 1.7 . there is only one such row in the table . the no record of this unqiue row is 7 .
Output: and { only { filter_greater { all_rows ; us viewers ( million ) ; 1.7 } } ; eq { hop { filter_greater { all_rows ; us viewers ( million ) ; 1.7 } ; no } ; 7 } }
select the rows whose leading scorer record fuzzily matches to roy : . the average of the leading scorer record of these rows is 25.75 .
Output: round_eq { avg { filter_eq { all_rows ; leading scorer ; roy : } ; leading scorer } ; 25.75 }
the maximum hydropathy index record of all rows is 4.5 .
Output: eq { max { all_rows ; hydropathy index } ; 4.5 }
select the rows whose laps record is less than 5 . there is only one such row in the table . the year record of this unqiue row is 1953 .
Output: and { only { filter_less { all_rows ; laps ; 5 } } ; eq { hop { filter_less { all_rows ; laps ; 5 } ; year } ; 1953 } }
select the rows whose rider record fuzzily matches to steve plater . take the speed record of this row . select the rows whose rider record fuzzily matches to denver robb . take the speed record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; rider ; steve plater } ; speed } ; hop { filter_eq { all_rows ; rider ; denver robb } ; speed } }
for the days records of all rows , most of them are equal to 99 .
Output: most_eq { all_rows ; days ; 99 }
select the rows whose result record fuzzily matches to w . select the row whose week record of these rows is 3rd minimum . the opponent record of this row is pittsburgh steelers .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; result ; w } ; week ; 3 } ; opponent } ; pittsburgh steelers }
select the row whose average ratings record of all rows is maximum . the romaji title record of this row is my ☆ boss my ☆ hero .
Output: eq { hop { argmax { all_rows ; average ratings } ; romaji title } ; my ☆ boss my ☆ hero }
select the rows whose tournament record fuzzily matches to open . the sum of the cuts made record of these rows is 17 .
Output: round_eq { sum { filter_eq { all_rows ; tournament ; open } ; cuts made } ; 17 }
select the rows whose date record fuzzily matches to august . the sum of the attendance record of these rows is 83969 .
Output: round_eq { sum { filter_eq { all_rows ; date ; august } ; attendance } ; 83969 }
select the row whose played record of all rows is maximum . the manager record of this row is juan carlos chávez .
Output: eq { hop { argmax { all_rows ; played } ; manager } ; juan carlos chávez }
select the row whose silver record of all rows is maximum . the nation record of this row is cuba .
Output: eq { hop { argmax { all_rows ; silver } ; nation } ; cuba }
for the wins records of all rows , all of them are equal to 0 .
Output: all_eq { all_rows ; wins ; 0 }
select the rows whose opponent record fuzzily matches to new york giants . the 1st minimum date record of these rows is september 17 , 1967 .
Output: eq { nth_min { filter_eq { all_rows ; opponent ; new york giants } ; date ; 1 } ; september 17 , 1967 }
select the rows whose previous conference record fuzzily matches to northern indiana . among these rows , select the rows whose mascot record fuzzily matches to roughriders . there is only one such row in the table . the school record of this unqiue row is east chicago roosevelt .
Output: and { only { filter_eq { filter_eq { all_rows ; previous conference ; northern indiana } ; mascot ; roughriders } } ; eq { hop { filter_eq { filter_eq { all_rows ; previous conference ; northern indiana } ; mascot ; roughriders } ; school } ; east chicago roosevelt } }
select the row whose laps record of all rows is maximum . the year record of this row is 1989 .
Output: eq { hop { argmax { all_rows ; laps } ; year } ; 1989 }
select the row whose extra points 1 point record of all rows is maximum . the player record of this row is bruce shorts .
Output: eq { hop { argmax { all_rows ; extra points 1 point } ; player } ; bruce shorts }
select the row whose % of votes khuzestan record of all rows is 2nd maximum . the candidates record of this row is akbar hashemi rafsanjani .
Output: eq { hop { nth_argmax { all_rows ; % of votes khuzestan ; 2 } ; candidates } ; akbar hashemi rafsanjani }
the average of the elevation ( m ) record of all rows is 1952 .
Output: round_eq { avg { all_rows ; elevation ( m ) } ; 1952 }
select the rows whose constructor record fuzzily matches to maserati . among these rows , select the rows whose laps record is less than 92 . there is only one such row in the table . the driver record of this unqiue row is luigi piotti .
Output: and { only { filter_less { filter_eq { all_rows ; constructor ; maserati } ; laps ; 92 } } ; eq { hop { filter_less { filter_eq { all_rows ; constructor ; maserati } ; laps ; 92 } ; driver } ; luigi piotti } }
select the rows whose venue record fuzzily matches to roker park . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; venue ; roker park } } ; 2 }
select the rows whose away team score record is less than 10 . among these rows , select the rows whose crowd record is greater than or equal to 23000 . the number of such rows is 3 .
Output: eq { count { filter_greater_eq { filter_less { all_rows ; away team score ; 10 } ; crowd ; 23000 } } ; 3 }
the sum of the points record of all rows is 434 .
Output: round_eq { sum { all_rows ; points } ; 434 }
the sum of the attendance record of all rows is 476,381 .
Output: round_eq { sum { all_rows ; attendance } ; 476,381 }
select the row whose laps led record of all rows is maximum . the driver record of this row is dario franchitti .
Output: eq { hop { argmax { all_rows ; laps led } ; driver } ; dario franchitti }
for the linda mccartney records of all rows , most of them fuzzily match to keyboards .
Output: most_eq { all_rows ; linda mccartney ; keyboards }
select the rows whose score record fuzzily matches to w . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; score ; w } } ; 4 }
select the rows whose type record fuzzily matches to plain stage . the number of such rows is 10 .
Output: eq { count { filter_eq { all_rows ; type ; plain stage } } ; 10 }
select the rows whose wins record is equal to 0 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; wins ; 0 } } ; 2 }
select the row whose gdp per capita ( us ) record of all rows is maximum . the member countries record of this row is luxembourg .
Output: eq { hop { argmax { all_rows ; gdp per capita ( us ) } ; member countries } ; luxembourg }
select the row whose total record of all rows is 1st maximum . the player record of this row is hal sutton .
Output: eq { hop { nth_argmax { all_rows ; total ; 1 } ; player } ; hal sutton }
select the row whose date record of all rows is minimum . the location record of this row is hallandale beach , florida .
Output: eq { hop { argmin { all_rows ; date } ; location } ; hallandale beach , florida }
select the rows whose born record fuzzily matches to 30 november 1874 . take the left house record of this row . select the rows whose born record fuzzily matches to 16 january 1905 . take the left house record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; born ; 30 november 1874 } ; left house } ; hop { filter_eq { all_rows ; born ; 16 january 1905 } ; left house } }
for the start datum records of all rows , most of them fuzzily match to wgs84 .
Output: most_eq { all_rows ; start datum ; wgs84 }
the average of the points record of all rows is 2 .
Output: round_eq { avg { all_rows ; points } ; 2 }
select the rows whose note record fuzzily matches to reportedly still active as of 2009 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; note ; reportedly still active as of 2009 } } ; 4 }
select the rows whose extra record fuzzily matches to short race . among these rows , select the rows whose result record is less than 10th . there is only one such row in the table . the year record of this unqiue row is 2002 . the venue record of this unqiue row is dublin , ireland .
Output: and { only { filter_less { filter_eq { all_rows ; extra ; short race } ; result ; 10th } } ; and { eq { hop { filter_less { filter_eq { all_rows ; extra ; short race } ; result ; 10th } ; year } ; 2002 } ; eq { hop { filter_less { filter_eq { all_rows ; extra ; short race } ; result ; 10th } ; venue } ; dublin , ireland } } }
select the row whose attendance record of all rows is 2nd maximum . the opponent record of this row is denver broncos .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; opponent } ; denver broncos }
select the row whose money requested record of all rows is maximum . the entrepreneur ( s ) record of this row is emmie matthews & ed stevens .
Output: eq { hop { argmax { all_rows ; money requested } ; entrepreneur ( s ) } ; emmie matthews & ed stevens }
the average of the score record of all rows is 1.4 .
Output: round_eq { avg { all_rows ; score } ; 1.4 }
the average of the year left record of all rows is 1977 .
Output: round_eq { avg { all_rows ; year left } ; 1977 }
select the row whose elevation ( m ) record of all rows is 2nd maximum . the peak record of this row is snøhetta .
Output: eq { hop { nth_argmax { all_rows ; elevation ( m ) ; 2 } ; peak } ; snøhetta }
select the rows whose nationality record fuzzily matches to argentina . there is only one such row in the table . the name record of this unqiue row is lionel messi .
Output: and { only { filter_eq { all_rows ; nationality ; argentina } } ; eq { hop { filter_eq { all_rows ; nationality ; argentina } ; name } ; lionel messi } }
select the rows whose team record fuzzily matches to weytech . there is only one such row in the table . the driver record of this unqiue row is donny crevels .
Output: and { only { filter_eq { all_rows ; team ; weytech } } ; eq { hop { filter_eq { all_rows ; team ; weytech } ; driver } ; donny crevels } }
select the row whose vote percentage record of all rows is maximum . the player record of this row is hamit altıntop .
Output: eq { hop { argmax { all_rows ; vote percentage } ; player } ; hamit altıntop }
select the rows whose venue record fuzzily matches to western oval . the maximum crowd record of these rows is 7000 .
Output: eq { max { filter_eq { all_rows ; venue ; western oval } ; crowd } ; 7000 }
select the rows whose tournament record fuzzily matches to netherlands . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; tournament ; netherlands } } ; 2 }
for the date records of all rows , all of them fuzzily match to 17 june 1939 .
Output: all_eq { all_rows ; date ; 17 june 1939 }
select the rows whose country record does not match to united states . there is only one such row in the table . the player record of this unqiue row is david graham .
Output: and { only { filter_not_eq { all_rows ; country ; united states } } ; eq { hop { filter_not_eq { all_rows ; country ; united states } ; player } ; david graham } }
select the rows whose brand record fuzzily matches to gmc . take the year founded record of this row . select the rows whose brand record fuzzily matches to chevrolet . take the year founded record of this row . the second record is 10 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; brand ; gmc } ; year founded } ; hop { filter_eq { all_rows ; brand ; chevrolet } ; year founded } } ; -10 }
select the rows whose event record fuzzily matches to medley . there is only one such row in the table .
Output: only { filter_eq { all_rows ; event ; medley } }
select the rows whose result record fuzzily matches to re - elected . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; result ; re - elected } } ; 4 }
select the rows whose segment d record fuzzily matches to wigs . there is only one such row in the table . the episode record of this unqiue row is 71 .
Output: and { only { filter_eq { all_rows ; segment d ; wigs } } ; eq { hop { filter_eq { all_rows ; segment d ; wigs } ; episode } ; 71 } }
the average of the points record of all rows is 48.8 .
Output: round_eq { avg { all_rows ; points } ; 48.8 }
select the rows whose date record fuzzily matches to january . among these rows , select the rows whose crowd record is less than 4000 . the number of such rows is 3 .
Output: eq { count { filter_less { filter_eq { all_rows ; date ; january } ; crowd ; 4000 } } ; 3 }
select the rows whose position record fuzzily matches to m . among these rows , select the rows whose pick record is greater than 27 . the number of such rows is 3 .
Output: eq { count { filter_greater { filter_eq { all_rows ; position ; m } ; pick ; 27 } } ; 3 }
select the row whose score record of all rows is 4th minimum . the player record of this row is geoff ogilvy .
Output: eq { hop { nth_argmin { all_rows ; score ; 4 } ; player } ; geoff ogilvy }
select the rows whose transfer window record fuzzily matches to winter . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; transfer window ; winter } } ; 3 }
select the rows whose winning driver record fuzzily matches to lee wallard . take the date record of this row . select the rows whose winning driver record fuzzily matches to alberto ascari . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; winning driver ; lee wallard } ; date } ; hop { filter_eq { all_rows ; winning driver ; alberto ascari } ; date } }
select the row whose points record of all rows is 2nd maximum . the year record of this row is 1991 .
Output: eq { hop { nth_argmax { all_rows ; points ; 2 } ; year } ; 1991 }