niryuu/sni-each-converted huggingface dataset:
select the rows whose venue record fuzzily matches to a . select the row whose date record of these rows is 2nd minimum . the attendance record of this row is 10000 .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; venue ; a } ; date ; 2 } ; attendance } ; 10000 }
select the row whose score record of all rows is maximum . the tournament record of this row is 1999 .
Output: eq { hop { argmax { all_rows ; score } ; tournament } ; 1999 }
select the row whose first elected record of all rows is 7th minimum . the party record of this row is republican .
Output: eq { hop { nth_argmin { all_rows ; first elected ; 7 } ; party } ; republican }
select the row whose points record of all rows is maximum . the driver record of this row is michael schumacher .
Output: eq { hop { argmax { all_rows ; points } ; driver } ; michael schumacher }
select the rows whose result record fuzzily matches to retired democratic hold . there is only one such row in the table . the incumbent record of this unqiue row is william y humphreys .
Output: and { only { filter_eq { all_rows ; result ; retired democratic hold } } ; eq { hop { filter_eq { all_rows ; result ; retired democratic hold } ; incumbent } ; william y humphreys } }
the sum of the in service record of all rows is 35 .
Output: round_eq { sum { all_rows ; in service } ; 35 }
select the rows whose color record fuzzily matches to green and light colors . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; color ; green and light colors } } ; 2 }
select the row whose entries record of all rows is 1st maximum . the driver record of this row is michael schumacher .
Output: eq { hop { nth_argmax { all_rows ; entries ; 1 } ; driver } ; michael schumacher }
select the rows whose location record fuzzily matches to albany . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; location ; albany } } ; 2 }
select the row whose attendance record of all rows is 3rd maximum . the date record of this row is october 31 , 1999 .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 3 } ; date } ; october 31 , 1999 }
select the rows whose visitor record fuzzily matches to penguins . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; visitor ; penguins } } ; 6 }
for the overall wc points ( rank ) records of all rows , most of them are less than 500 .
Output: most_less { all_rows ; overall wc points ( rank ) ; 500 }
select the rows whose movie record fuzzily matches to agneepath . take the lifetime india distributor share record of this row . select the rows whose movie record fuzzily matches to dabangg 2 . take the lifetime india distributor share record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; movie ; agneepath } ; lifetime india distributor share } ; hop { filter_eq { all_rows ; movie ; dabangg 2 } ; lifetime india distributor share } }
select the rows whose % ( 2040 ) record is greater than or equal to 10 . the number of such rows is 3 .
Output: eq { count { filter_greater_eq { all_rows ; % ( 2040 ) ; 10 } } ; 3 }
select the rows whose award record does not match to saturn award . there is only one such row in the table . the year record of this unqiue row is 2010 . the result record of this unqiue row is nominated .
Output: and { only { filter_not_eq { all_rows ; award ; saturn award } } ; and { eq { hop { filter_not_eq { all_rows ; award ; saturn award } ; year } ; 2010 } ; eq { hop { filter_not_eq { all_rows ; award ; saturn award } ; result } ; nominated } } }
select the row whose founded record of all rows is minimum . the school record of this row is lockland high school .
Output: eq { hop { argmin { all_rows ; founded } ; school } ; lockland high school }
select the rows whose founded record is greater than 2000 . among these rows , select the rows whose seasons in prsl record fuzzily matches to 2009 . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_greater { all_rows ; founded ; 2000 } ; seasons in prsl ; 2009 } } ; 3 }
select the rows whose nominee ( s ) record fuzzily matches to john wells . take the year record of this row . select the rows whose nominee ( s ) record fuzzily matches to carol flint . take the year record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; nominee ( s ) ; john wells } ; year } ; hop { filter_eq { all_rows ; nominee ( s ) ; carol flint } ; year } }
select the rows whose founded record is greater than or equal to 2005 . the number of such rows is 6 .
Output: eq { count { filter_greater_eq { all_rows ; founded ; 2005 } } ; 6 }
select the rows whose points record is greater than 90 . the number of such rows is 2 .
Output: eq { count { filter_greater { all_rows ; points ; 90 } } ; 2 }
select the rows whose location record fuzzily matches to metlife stadium . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; location ; metlife stadium } } ; 3 }
select the row whose height ( m ) record of all rows is 2nd maximum . the island record of this row is ailsa craig .
Output: eq { hop { nth_argmax { all_rows ; height ( m ) ; 2 } ; island } ; ailsa craig }
select the row whose year record of all rows is 4th minimum . the champion record of this row is emory .
Output: eq { hop { nth_argmin { all_rows ; year ; 4 } ; champion } ; emory }
the maximum term start record of all rows is 11 january 2005 . the minister record of the row with superlative term start record is eli ben - menachem .
Output: and { eq { max { all_rows ; term start } ; 11 january 2005 } ; eq { hop { argmax { all_rows ; term start } ; minister } ; eli ben - menachem } }
select the rows whose nationality record fuzzily matches to switzerland . there is only one such row in the table . the player record of this unqiue row is julien vauclair .
Output: and { only { filter_eq { all_rows ; nationality ; switzerland } } ; eq { hop { filter_eq { all_rows ; nationality ; switzerland } ; player } ; julien vauclair } }
select the rows whose video record fuzzily matches to 720p . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; video ; 720p } } ; 2 }
select the rows whose position record fuzzily matches to guard . there is only one such row in the table . the player record of this unqiue row is steve kerr .
Output: and { only { filter_eq { all_rows ; position ; guard } } ; eq { hop { filter_eq { all_rows ; position ; guard } ; player } ; steve kerr } }
select the rows whose built record is equal to 1921 . there is only one such row in the table . the name record of this unqiue row is bob white .
Output: and { only { filter_eq { all_rows ; built ; 1921 } } ; eq { hop { filter_eq { all_rows ; built ; 1921 } ; name } ; bob white } }
select the row whose first elected record of all rows is 2nd minimum . the incumbent record of this row is frank w boykin .
Output: eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; incumbent } ; frank w boykin }
for the team records of all rows , most of them fuzzily match to motopark academy .
Output: most_eq { all_rows ; team ; motopark academy }
for the competition records of all rows , most of them fuzzily match to european championships .
Output: most_eq { all_rows ; competition ; european championships }
select the rows whose summary record fuzzily matches to runners - up . among these rows , select the rows whose losses record is equal to 11 . there is only one such row in the table . the year record of this unqiue row is 2012 .
Output: and { only { filter_eq { filter_eq { all_rows ; summary ; runners - up } ; losses ; 11 } } ; eq { hop { filter_eq { filter_eq { all_rows ; summary ; runners - up } ; losses ; 11 } ; year } ; 2012 } }
select the rows whose player record fuzzily matches to scott simpson . take the place record of this row . select the rows whose player record fuzzily matches to larry mize . take the place record of this row . the second record is 3 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; player ; scott simpson } ; place } ; hop { filter_eq { all_rows ; player ; larry mize } ; place } } ; -3 }
select the row whose wins record of all rows is maximum . the series record of this row is italian formula three .
Output: eq { hop { argmax { all_rows ; wins } ; series } ; italian formula three }
select the rows whose writer ( s ) record fuzzily matches to steven melching . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; writer ( s ) ; steven melching } } ; 3 }
select the rows whose english name record fuzzily matches to hexi district . take the density record of this row . select the rows whose english name record fuzzily matches to fenghuang . take the density record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; english name ; hexi district } ; density } ; hop { filter_eq { all_rows ; english name ; fenghuang } ; density } }
select the rows whose island record fuzzily matches to hawaii . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; island ; hawaii } } ; 2 }
the average of the average record of all rows is 2530.6 .
Output: round_eq { avg { all_rows ; average } ; 2530.6 }
select the rows whose home team score record is less than 10.0 . the number of such rows is 3 .
Output: eq { count { filter_less { all_rows ; home team score ; 10.0 } } ; 3 }
the sum of the crowd record of all rows is 174 , 580 .
Output: round_eq { sum { all_rows ; crowd } ; 174 , 580 }
for the position records of all rows , most of them fuzzily match to rhp .
Output: most_eq { all_rows ; position ; rhp }
select the rows whose player record fuzzily matches to vic / sa . there is only one such row in the table .
Output: only { filter_eq { all_rows ; player ; vic / sa } }
for the publisher records of all rows , most of them fuzzily match to nintendo .
Output: most_eq { all_rows ; publisher ; nintendo }
select the rows whose result record is greater than 50 . there is only one such row in the table . the date record of this unqiue row is september 9 , 1979 . the opponent record of this unqiue row is cincinnati bengals .
Output: and { only { filter_greater { all_rows ; result ; 50 } } ; and { eq { hop { filter_greater { all_rows ; result ; 50 } ; date } ; september 9 , 1979 } ; eq { hop { filter_greater { all_rows ; result ; 50 } ; opponent } ; cincinnati bengals } } }
select the rows whose location attendance record fuzzily matches to arizona veterans memorial coliseum . the sum of the score record of these rows is 200 .
Output: round_eq { sum { filter_eq { all_rows ; location attendance ; arizona veterans memorial coliseum } ; score } ; 200 }
for the location records of all rows , most of them fuzzily match to south dakota .
Output: most_eq { all_rows ; location ; south dakota }
select the row whose round record of all rows is 1st maximum . the opponent record of this row is antoine jaoude .
Output: eq { hop { nth_argmax { all_rows ; round ; 1 } ; opponent } ; antoine jaoude }
select the rows whose hometown record fuzzily matches to brampton , on . there is only one such row in the table .
Output: only { filter_eq { all_rows ; hometown ; brampton , on } }
for the money records of all rows , most of them are greater than or equal to 400 .
Output: most_greater_eq { all_rows ; money ; 400 }
select the rows whose record label record fuzzily matches to for us . there is only one such row in the table . the single record of this unqiue row is moped girls .
Output: and { only { filter_eq { all_rows ; record label ; for us } } ; eq { hop { filter_eq { all_rows ; record label ; for us } ; single } ; moped girls } }
the average of the sun devils points record of all rows is 38.5 .
Output: round_eq { avg { all_rows ; sun devils points } ; 38.5 }
select the rows whose crowd record is greater than 20000 . among these rows , select the rows whose venue record fuzzily matches to victoria park . there is only one such row in the table . the home team record of this unqiue row is collingwood .
Output: and { only { filter_eq { filter_greater { all_rows ; crowd ; 20000 } ; venue ; victoria park } } ; eq { hop { filter_eq { filter_greater { all_rows ; crowd ; 20000 } ; venue ; victoria park } ; home team } ; collingwood } }
select the rows whose year record is greater than or equal to 1980 . the average of the world ranking record of these rows is 9.5 .
Output: round_eq { avg { filter_greater_eq { all_rows ; year ; 1980 } ; world ranking } ; 9.5 }
for the political party records of all rows , most of them fuzzily match to democratic party .
Output: most_eq { all_rows ; political party ; democratic party }
select the rows whose nationality record fuzzily matches to czechoslovakia . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; nationality ; czechoslovakia } } ; 3 }
select the rows whose institution record fuzzily matches to shippensburg university . take the established record of this row . select the rows whose institution record fuzzily matches to penn state harrisburg . take the established record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; institution ; shippensburg university } ; established } ; hop { filter_eq { all_rows ; institution ; penn state harrisburg } ; established } }
select the rows whose matches record is equal to 10 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; matches ; 10 } } ; 2 }
select the rows whose 2012 club record fuzzily matches to diablo water polo . there is only one such row in the table . the name record of this unqiue row is maggie steffens .
Output: and { only { filter_eq { all_rows ; 2012 club ; diablo water polo } } ; eq { hop { filter_eq { all_rows ; 2012 club ; diablo water polo } ; name } ; maggie steffens } }
select the rows whose total dismissals record is less than 10 . the sum of the total dismissals record of these rows is 8 .
Output: round_eq { sum { filter_less { all_rows ; total dismissals ; 10 } ; total dismissals } ; 8 }
for the timeslot records of all rows , all of them fuzzily match to wednesday 8:30 pm .
Output: all_eq { all_rows ; timeslot ; wednesday 8:30 pm }
for the length records of all rows , most of them are less than 5:00 .
Output: most_less { all_rows ; length ; 5:00 }
select the rows whose rank record fuzzily matches to 1 . take the total record of this row . select the rows whose rank record fuzzily matches to 2 . take the total record of this row . the first record is less than the second record . the total record of the first row is 4 . the total record of the second row is 5 .
Output: and { less { hop { filter_eq { all_rows ; rank ; 1 } ; total } ; hop { filter_eq { all_rows ; rank ; 2 } ; total } } ; and { eq { hop { filter_eq { all_rows ; rank ; 1 } ; total } ; 4 } ; eq { hop { filter_eq { all_rows ; rank ; 2 } ; total } ; 5 } } }
for the wins records of all rows , most of them are less than 7 .
Output: most_less { all_rows ; wins ; 7 }
select the row whose total ( kg ) record of all rows is 3rd maximum . the name record of this row is soraya jiménez ( mex ) .
Output: eq { hop { nth_argmax { all_rows ; total ( kg ) ; 3 } ; name } ; soraya jiménez ( mex ) }
select the row whose population m ( luz ) record of all rows is 2nd maximum . the city record of this row is london .
Output: eq { hop { nth_argmax { all_rows ; population m ( luz ) ; 2 } ; city } ; london }
for the partner records of all rows , most of them fuzzily match to nicolas mahut .
Output: most_eq { all_rows ; partner ; nicolas mahut }
select the rows whose affiliation record fuzzily matches to public . among these rows , select the rows whose enrollment record is greater than 20000 . the number of such rows is 4 .
Output: eq { count { filter_greater { filter_eq { all_rows ; affiliation ; public } ; enrollment ; 20000 } } ; 4 }
select the rows whose year record is equal to 2002 . there is only one such row in the table . the event record of this unqiue row is ozzfest 2002 .
Output: and { only { filter_eq { all_rows ; year ; 2002 } } ; eq { hop { filter_eq { all_rows ; year ; 2002 } ; event } ; ozzfest 2002 } }
the average of the rank record of all rows is 38 .
Output: round_eq { avg { all_rows ; rank } ; 38 }
the average of the candidates record of all rows is 57 % .
Output: round_eq { avg { all_rows ; candidates } ; 57 % }
select the rows whose date record fuzzily matches to december . for the result records of these rows , most of them fuzzily match to l .
Output: most_eq { filter_eq { all_rows ; date ; december } ; result ; l }
select the rows whose incumbent record fuzzily matches to bruce vento . take the opponent record of this row . select the rows whose incumbent record fuzzily matches to gerry sikorski . take the opponent record of this row . the first record is .10 % larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; incumbent ; bruce vento } ; opponent } ; hop { filter_eq { all_rows ; incumbent ; gerry sikorski } ; opponent } } ; .10 % }
select the rows whose venue record fuzzily matches to chile . there is only one such row in the table . the date record of this unqiue row is september 4 , 2001 .
Output: and { only { filter_eq { all_rows ; venue ; chile } } ; eq { hop { filter_eq { all_rows ; venue ; chile } ; date } ; september 4 , 2001 } }
for the matches records of all rows , all of them are equal to 7 .
Output: all_eq { all_rows ; matches ; 7 }
select the rows whose nat record fuzzily matches to slovakia . there is only one such row in the table . the name record of this unqiue row is filip šebo .
Output: and { only { filter_eq { all_rows ; nat ; slovakia } } ; eq { hop { filter_eq { all_rows ; nat ; slovakia } ; name } ; filip šebo } }
select the rows whose event record fuzzily matches to ufc 12 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; event ; ufc 12 } } ; 2 }
select the rows whose school / club team record fuzzily matches to kentucky . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; school / club team ; kentucky } } ; 2 }
select the rows whose winner record fuzzily matches to rangers . among these rows , select the rows whose runner - up record fuzzily matches to morton . there is only one such row in the table . the season record of this unqiue row is 1941 - 42 .
Output: and { only { filter_eq { filter_eq { all_rows ; winner ; rangers } ; runner - up ; morton } } ; eq { hop { filter_eq { filter_eq { all_rows ; winner ; rangers } ; runner - up ; morton } ; season } ; 1941 - 42 } }
select the rows whose successor record fuzzily matches to vacant . for the date successor seated records of these rows , most of them fuzzily match to not filled this term .
Output: most_eq { filter_eq { all_rows ; successor ; vacant } ; date successor seated ; not filled this term }
select the rows whose position record fuzzily matches to running back . for the round records of these rows , all of them are less than 10 .
Output: all_less { filter_eq { all_rows ; position ; running back } ; round ; 10 }
select the row whose enrollment record of all rows is 2nd maximum . the school record of this row is whitko .
Output: eq { hop { nth_argmax { all_rows ; enrollment ; 2 } ; school } ; whitko }
select the rows whose country record fuzzily matches to northern ireland . there is only one such row in the table . the player record of this unqiue row is david feherty .
Output: and { only { filter_eq { all_rows ; country ; northern ireland } } ; eq { hop { filter_eq { all_rows ; country ; northern ireland } ; player } ; david feherty } }
for the enrollment ( 2013 / 14 ) records of all rows , most of them are greater than 400 .
Output: most_greater { all_rows ; enrollment ( 2013 / 14 ) ; 400 }
select the row whose date of appointment record of all rows is 1st minimum . the team record of this row is académica de coimbra .
Output: eq { hop { nth_argmin { all_rows ; date of appointment ; 1 } ; team } ; académica de coimbra }
select the rows whose winning party 2003 record fuzzily matches to liberal democrats . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; winning party 2003 ; liberal democrats } } ; 3 }
select the rows whose location record fuzzily matches to scotiabank place . among these rows , select the rows whose score record fuzzily matches to w . the number of such rows is 4 .
Output: eq { count { filter_eq { filter_eq { all_rows ; location ; scotiabank place } ; score ; w } } ; 4 }
for the points 1 records of all rows , most of them are greater than or equal to 30 .
Output: most_greater_eq { all_rows ; points 1 ; 30 }
for the round records of all rows , most of them fuzzily match to final .
Output: most_eq { all_rows ; round ; final }
select the rows whose opponent record fuzzily matches to buffalo bills . the sum of the attendance record of these rows is 26748 .
Output: round_eq { sum { filter_eq { all_rows ; opponent ; buffalo bills } ; attendance } ; 26748 }
the average of the lost record of all rows is 13.3 .
Output: round_eq { avg { all_rows ; lost } ; 13.3 }
select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is nathaniel macon .
Output: eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; nathaniel macon }
select the rows whose position record fuzzily matches to 6th . there is only one such row in the table . the year record of this unqiue row is 2005 .
Output: and { only { filter_eq { all_rows ; position ; 6th } } ; eq { hop { filter_eq { all_rows ; position ; 6th } ; year } ; 2005 } }
select the rows whose winning driver record fuzzily matches to harry schell . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; winning driver ; harry schell } } ; 2 }
select the row whose opponents record of all rows is maximum . the opponent record of this row is penn state .
Output: eq { hop { argmax { all_rows ; opponents } ; opponent } ; penn state }
the average of the overall record of all rows is 38 .
Output: round_eq { avg { all_rows ; overall } ; 38 }
select the rows whose constellation record fuzzily matches to centaurus . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; constellation ; centaurus } } ; 4 }
select the row whose opened ( closing date if defunct ) record of all rows is 2nd minimum . the track record of this row is indianapolis speedrome .
Output: eq { hop { nth_argmin { all_rows ; opened ( closing date if defunct ) ; 2 } ; track } ; indianapolis speedrome }
select the rows whose score record fuzzily matches to l . there is only one such row in the table . the date record of this unqiue row is april 5 .
Output: and { only { filter_eq { all_rows ; score ; l } } ; eq { hop { filter_eq { all_rows ; score ; l } ; date } ; april 5 } }
for the venue records of all rows , all of them fuzzily match to skonto hall , riga .
Output: all_eq { all_rows ; venue ; skonto hall , riga }
select the rows whose player record fuzzily matches to mike heldt . take the round record of this row . select the rows whose player record fuzzily matches to joachim weinberg . take the round record of this row . the second record is 1 larger than the first record . the round record of the first row is 10 . the round record of the second row is 11 .
Output: and { eq { diff { hop { filter_eq { all_rows ; player ; mike heldt } ; round } ; hop { filter_eq { all_rows ; player ; joachim weinberg } ; round } } ; -1 } ; and { eq { hop { filter_eq { all_rows ; player ; mike heldt } ; round } ; 10 } ; eq { hop { filter_eq { all_rows ; player ; joachim weinberg } ; round } ; 11 } } }
select the rows whose opponent record fuzzily matches to guido carlo . take the round record of this row . select the rows whose opponent record fuzzily matches to sebastien gauthier . take the round record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; opponent ; guido carlo } ; round } ; hop { filter_eq { all_rows ; opponent ; sebastien gauthier } ; round } }
select the rows whose university record fuzzily matches to mcgill university . take the founded record of this row . select the rows whose university record fuzzily matches to université de sherbrooke . take the founded record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; university ; mcgill university } ; founded } ; hop { filter_eq { all_rows ; university ; université de sherbrooke } ; founded } }
for the winner records of all rows , most of them fuzzily match to blue .
Output: most_eq { all_rows ; winner ; blue }
select the row whose attendance record of all rows is 1st maximum . the date record of this row is march 19 .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; date } ; march 19 }
select the rows whose category record fuzzily matches to arts . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; category ; arts } } ; 4 }
select the rows whose tournament record fuzzily matches to sweden . there is only one such row in the table . the date record of this unqiue row is 4 july 1988 .
Output: and { only { filter_eq { all_rows ; tournament ; sweden } } ; eq { hop { filter_eq { all_rows ; tournament ; sweden } ; date } ; 4 july 1988 } }
the average of the number of contestants record of all rows is 11.4 .
Output: round_eq { avg { all_rows ; number of contestants } ; 11.4 }
select the rows whose hdtv record fuzzily matches to yes . there is only one such row in the table . the television service record of this unqiue row is satisfaction hd .
Output: and { only { filter_eq { all_rows ; hdtv ; yes } } ; eq { hop { filter_eq { all_rows ; hdtv ; yes } ; television service } ; satisfaction hd } }
select the rows whose award record fuzzily matches to laurence olivier award . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; award ; laurence olivier award } } ; 7 }
the 2nd minimum date record of all rows is jun 25 . the score record of the row with 2nd minimum date record is 2 - 3 .
Output: and { eq { nth_min { all_rows ; date ; 2 } ; jun 25 } ; eq { hop { nth_argmin { all_rows ; date ; 2 } ; score } ; 2 - 3 } }
the average of the round record of all rows is 3.8 .
Output: round_eq { avg { all_rows ; round } ; 3.8 }
select the rows whose language record fuzzily matches to marathi . there is only one such row in the table . the role record of this unqiue row is sonal .
Output: and { only { filter_eq { all_rows ; language ; marathi } } ; eq { hop { filter_eq { all_rows ; language ; marathi } ; role } ; sonal } }
for the games played records of all rows , all of them are equal to 36 .
Output: all_eq { all_rows ; games played ; 36 }
select the row whose capacity record of all rows is minimum . the venue record of this row is peyia municipal stadium .
Output: eq { hop { argmin { all_rows ; capacity } ; venue } ; peyia municipal stadium }
select the rows whose position record fuzzily matches to fb . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; position ; fb } } ; 3 }
select the rows whose province record fuzzily matches to gyeonggi . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; province ; gyeonggi } } ; 8 }
the average of the group 's equity shareholding record of all rows is 85 % .
Output: round_eq { avg { all_rows ; group 's equity shareholding } ; 85 % }
for the country of origin records of all rows , all of them fuzzily match to philippines .
Output: all_eq { all_rows ; country of origin ; philippines }
select the rows whose game site record fuzzily matches to qualcomm stadium . among these rows , select the rows whose time record fuzzily matches to 5:15 pm . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; game site ; qualcomm stadium } ; time ; 5:15 pm } } ; 3 }
select the rows whose home team score record is less than 14.0 . among these rows , select the rows whose date record fuzzily matches to 28 april 1973 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_less { all_rows ; home team score ; 14.0 } ; date ; 28 april 1973 } } ; 2 }
select the rows whose artist record fuzzily matches to ronnie carroll . take the points record of this row . select the rows whose artist record fuzzily matches to carmela corren . take the points record of this row . the first record is 12 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; artist ; ronnie carroll } ; points } ; hop { filter_eq { all_rows ; artist ; carmela corren } ; points } } ; 12 }
select the rows whose season record fuzzily matches to 2001 - 02 . take the regular season 1 record of this row . select the rows whose season record fuzzily matches to 2002 - 03 . take the regular season 1 record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; season ; 2001 - 02 } ; regular season 1 } ; hop { filter_eq { all_rows ; season ; 2002 - 03 } ; regular season 1 } }
select the rows whose country record fuzzily matches to ghana . there is only one such row in the table . the name record of this unqiue row is owusu - abeyie .
Output: and { only { filter_eq { all_rows ; country ; ghana } } ; eq { hop { filter_eq { all_rows ; country ; ghana } ; name } ; owusu - abeyie } }
select the rows whose director ( s ) record fuzzily matches to gary ross . there is only one such row in the table . the title record of this unqiue row is the hunger games .
Output: and { only { filter_eq { all_rows ; director ( s ) ; gary ross } } ; eq { hop { filter_eq { all_rows ; director ( s ) ; gary ross } ; title } ; the hunger games } }
select the row whose born record of all rows is minimum . the archbishop record of this row is jean baptiste lamy .
Output: eq { hop { argmin { all_rows ; born } ; archbishop } ; jean baptiste lamy }
for the residence records of all rows , most of them fuzzily match to winnipeg .
Output: most_eq { all_rows ; residence ; winnipeg }
select the rows whose actress record fuzzily matches to marlee matlin . take the year record of this row . select the rows whose actress record fuzzily matches to jessica tandy . take the year record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; actress ; marlee matlin } ; year } ; hop { filter_eq { all_rows ; actress ; jessica tandy } ; year } }
select the row whose original air date record of all rows is 2nd minimum . the family / families record of this row is the peterfreund family .
Output: eq { hop { nth_argmin { all_rows ; original air date ; 2 } ; family / families } ; the peterfreund family }
select the rows whose player record fuzzily matches to jo angel ( wa ) . take the matches record of this row . select the rows whose player record fuzzily matches to clarrie grimmett ( vic / sa ) . take the matches record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; jo angel ( wa ) } ; matches } ; hop { filter_eq { all_rows ; player ; clarrie grimmett ( vic / sa ) } ; matches } }
select the rows whose competition record fuzzily matches to uefa europa league . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; competition ; uefa europa league } } ; 4 }
select the rows whose height in ft record is less than 6-0 . there is only one such row in the table . the player record of this unqiue row is turner , andre andre turner .
Output: and { only { filter_less { all_rows ; height in ft ; 6-0 } } ; eq { hop { filter_less { all_rows ; height in ft ; 6-0 } ; player } ; turner , andre andre turner } }
for the district records of all rows , most of them fuzzily match to hisar .
Output: most_eq { all_rows ; district ; hisar }
select the rows whose aircraft record fuzzily matches to boeing . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; aircraft ; boeing } } ; 3 }
for the extra points records of all rows , most of them are equal to 0 .
Output: most_eq { all_rows ; extra points ; 0 }
the sum of the w record of all rows is 21 .
Output: round_eq { sum { all_rows ; w } ; 21 }
the average of the away team score record of all rows is 14.2 .
Output: round_eq { avg { all_rows ; away team score } ; 14.2 }
the average of the to par record of all rows is -7 .
Output: round_eq { avg { all_rows ; to par } ; -7 }
select the rows whose year record is equal to 2008 . among these rows , select the rows whose nominated / won record fuzzily matches to won . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; year ; 2008 } ; nominated / won ; won } } ; 3 }
select the rows whose date record fuzzily matches to november 8 , 1992 . take the date record of this row . select the rows whose date record fuzzily matches to november 22 , 1992 . take the date record of this row . the second record is 14 days larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; date ; november 8 , 1992 } ; date } ; hop { filter_eq { all_rows ; date ; november 22 , 1992 } ; date } } ; -14 days }
select the rows whose region record fuzzily matches to central . among these rows , select the rows whose pop density record is greater than 1000 . the number of such rows is 3 .
Output: eq { count { filter_greater { filter_eq { all_rows ; region ; central } ; pop density ; 1000 } } ; 3 }
for the date records of all rows , most of them fuzzily match to 1992 .
Output: most_eq { all_rows ; date ; 1992 }
select the rows whose category record fuzzily matches to outstanding featured actor in a musical . there is only one such row in the table . the nominee record of this unqiue row is alexander gemignani .
Output: and { only { filter_eq { all_rows ; category ; outstanding featured actor in a musical } } ; eq { hop { filter_eq { all_rows ; category ; outstanding featured actor in a musical } ; nominee } ; alexander gemignani } }
select the row whose crowd record of all rows is maximum . the home team record of this row is carlton . the away team record of this row is richmond .
Output: and { eq { hop { argmax { all_rows ; crowd } ; home team } ; carlton } ; eq { hop { argmax { all_rows ; crowd } ; away team } ; richmond } }
select the rows whose opponent record fuzzily matches to anorthosis famagusta . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; opponent ; anorthosis famagusta } } ; 2 }
select the row whose laps record of all rows is 2nd minimum . the driver record of this row is graham hill .
Output: eq { hop { nth_argmin { all_rows ; laps ; 2 } ; driver } ; graham hill }
select the row whose total wins record of all rows is maximum . the nationality record of this row is united states .
Output: eq { hop { argmax { all_rows ; total wins } ; nationality } ; united states }
select the row whose attendance record of all rows is 2nd maximum . the visitor record of this row is kings .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; visitor } ; kings }
select the rows whose country record fuzzily matches to united kingdom . the number of such rows is 9 .
Output: eq { count { filter_eq { all_rows ; country ; united kingdom } } ; 9 }
for the played records of all rows , most of them are equal to 14 .
Output: most_eq { all_rows ; played ; 14 }
select the rows whose owner record fuzzily matches to eternacom . there is only one such row in the table . the frequency record of this unqiue row is 103.5 fm .
Output: and { only { filter_eq { all_rows ; owner ; eternacom } } ; eq { hop { filter_eq { all_rows ; owner ; eternacom } ; frequency } ; 103.5 fm } }
select the rows whose country record fuzzily matches to italy . there is only one such row in the table . the athlete record of this unqiue row is beatrice adelizzi & giulia lapi .
Output: and { only { filter_eq { all_rows ; country ; italy } } ; eq { hop { filter_eq { all_rows ; country ; italy } ; athlete } ; beatrice adelizzi & giulia lapi } }
select the rows whose date record fuzzily matches to dec . for the result records of these rows , most of them fuzzily match to win .
Output: most_eq { filter_eq { all_rows ; date ; dec } ; result ; win }
select the rows whose engine record fuzzily matches to offy . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; engine ; offy } } ; 4 }
select the rows whose percentage record is greater than 3 % . the number of such rows is 1 .
Output: eq { count { filter_greater { all_rows ; percentage ; 3 % } } ; 1 }
the average of the total record of all rows is 2.13 .
Output: round_eq { avg { all_rows ; total } ; 2.13 }
select the rows whose location record fuzzily matches to india . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; location ; india } } ; 2 }
select the rows whose champions record fuzzily matches to guy forget . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; champions ; guy forget } } ; 2 }
select the rows whose entrant record is arbitrary . the number of such rows is 6 .
Output: eq { count { filter_all { all_rows ; entrant } } ; 6 }
select the rows whose week record fuzzily matches to top 10 . take the result record of this row . select the rows whose week record fuzzily matches to top 10 . take the result record of this row . the first record fuzzily matches to the second record . the result record of the first row is bottom 2 . the result record of the second row is bottom 2 .
Output: and { eq { hop { filter_eq { all_rows ; week ; top 10 } ; result } ; hop { filter_eq { all_rows ; week ; top 10 } ; result } } ; and { eq { hop { filter_eq { all_rows ; week ; top 10 } ; result } ; bottom 2 } ; eq { hop { filter_eq { all_rows ; week ; top 10 } ; result } ; bottom 2 } } }
select the row whose pick record of all rows is 6th minimum . the player record of this row is mike wacker .
Output: eq { hop { nth_argmin { all_rows ; pick ; 6 } ; player } ; mike wacker }
select the rows whose receiving tds record is equal to 0 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; receiving tds ; 0 } } ; 2 }
the sum of the number of stations record of all rows is 259 .
Output: round_eq { sum { all_rows ; number of stations } ; 259 }
for the high points records of all rows , most of them fuzzily match to vince carter .
Output: most_eq { all_rows ; high points ; vince carter }
select the rows whose constructor record fuzzily matches to cooper - climax . select the row whose date record of these rows is minimum . the race name record of this row is vii glover trophy .
Output: eq { hop { argmin { filter_eq { all_rows ; constructor ; cooper - climax } ; date } ; race name } ; vii glover trophy }
select the rows whose incumbent record fuzzily matches to hale boggs . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to joe waggonner . take the first elected record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; incumbent ; hale boggs } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; joe waggonner } ; first elected } }
the average of the max speed record of all rows is 29 .
Output: round_eq { avg { all_rows ; max speed } ; 29 }
select the rows whose result record fuzzily matches to retired . there is only one such row in the table . the incumbent record of this unqiue row is harold r collier .
Output: and { only { filter_eq { all_rows ; result ; retired } } ; eq { hop { filter_eq { all_rows ; result ; retired } ; incumbent } ; harold r collier } }
for the type records of all rows , most of them fuzzily match to village .
Output: most_eq { all_rows ; type ; village }
for the engine records of all rows , all of them fuzzily match to offenhauser l4 .
Output: all_eq { all_rows ; engine ; offenhauser l4 }
for the power rpm records of all rows , most of them are greater than or equal to 6000 .
Output: most_greater_eq { all_rows ; power rpm ; 6000 }
for the loans received records of all rows , most of them are greater than 0 .
Output: most_greater { all_rows ; loans received ; 0 }
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: less { hop { filter_eq { all_rows ; location ; arnhem } ; date ( from ) } ; hop { filter_eq { all_rows ; location ; nijmegen } ; date ( from ) } }
select the row whose total record of all rows is maximum . the player record of this row is hal sutton .
Output: eq { hop { argmax { all_rows ; total } ; player } ; hal sutton }
select the rows whose competition record fuzzily matches to 2014 fifa world cup qualification . there is only one such row in the table . the date record of this unqiue row is 15 october 2013 .
Output: and { only { filter_eq { all_rows ; competition ; 2014 fifa world cup qualification } } ; eq { hop { filter_eq { all_rows ; competition ; 2014 fifa world cup qualification } ; date } ; 15 october 2013 } }
select the rows whose congress record fuzzily matches to 109th congress . take the of cosponsors record of this row . select the rows whose congress record fuzzily matches to 108th congress . take the of cosponsors record of this row . the first record is 3 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; congress ; 109th congress } ; of cosponsors } ; hop { filter_eq { all_rows ; congress ; 108th congress } ; of cosponsors } } ; 3 }
the average of the episode record of all rows is 240 .
Output: round_eq { avg { all_rows ; episode } ; 240 }
for the pos records of all rows , most of them fuzzily match to cf .
Output: most_eq { all_rows ; pos ; cf }
select the row whose - record of all rows is 2nd minimum . the team record of this row is rahal letterman lanigan racing .
Output: eq { hop { nth_argmin { all_rows ; - ; 2 } ; team } ; rahal letterman lanigan racing }
select the rows whose high assists record fuzzily matches to damon stoudamire . the sum of the high assists record of these rows is 95 .
Output: round_eq { sum { filter_eq { all_rows ; high assists ; damon stoudamire } ; high assists } ; 95 }
the sum of the attendance record of all rows is 197707 .
Output: round_eq { sum { all_rows ; attendance } ; 197707 }
select the rows whose wildcats points record is equal to 33 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; wildcats points ; 33 } } ; 2 }
the 5th minimum track record of all rows is 5 . the song title record of the row with 5th minimum track record is crazy arms .
Output: and { eq { nth_min { all_rows ; track ; 5 } ; 5 } ; eq { hop { nth_argmin { all_rows ; track ; 5 } ; song title } ; crazy arms } }
for the nba draft records of all rows , most of them fuzzily match to 1987 draft .
Output: most_eq { all_rows ; nba draft ; 1987 draft }
select the rows whose athlete record fuzzily matches to federico muller . take the round of 16 record of this row . select the rows whose athlete record fuzzily matches to felipe saucedo . take the round of 16 record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; athlete ; federico muller } ; round of 16 } ; hop { filter_eq { all_rows ; athlete ; felipe saucedo } ; round of 16 } }
select the row whose county team record of all rows is 2nd minimum . the player record of this row is mick mackey .
Output: eq { hop { nth_argmin { all_rows ; county team ; 2 } ; player } ; mick mackey }
select the rows whose authority record fuzzily matches to state integrated . there is only one such row in the table . the name record of this unqiue row is st mary 's catholic school .
Output: and { only { filter_eq { all_rows ; authority ; state integrated } } ; eq { hop { filter_eq { all_rows ; authority ; state integrated } ; name } ; st mary 's catholic school } }
select the rows whose regional county municipality ( rcm ) record is arbitrary . the number of such rows is 6 .
Output: eq { count { filter_all { all_rows ; regional county municipality ( rcm ) } } ; 6 }
select the rows whose opponent record fuzzily matches to richard white . take the round record of this row . select the rows whose opponent record fuzzily matches to jeremy beck . take the round record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opponent ; richard white } ; round } ; hop { filter_eq { all_rows ; opponent ; jeremy beck } ; round } }
select the rows whose engine record fuzzily matches to 16v . the maximum displacement record of these rows is 1596 cc .
Output: eq { max { filter_eq { all_rows ; engine ; 16v } ; displacement } ; 1596 cc }
select the rows whose height record is greater than 80 . the number of such rows is 4 .
Output: eq { count { filter_greater { all_rows ; height ; 80 } } ; 4 }
select the row whose agg record of all rows is maximum . the team 1 record of this row is matera ( h15 ) . the team 2 record of this row is ( h14 ) quarto .
Output: and { eq { hop { argmax { all_rows ; agg } ; team 1 } ; matera ( h15 ) } ; eq { hop { argmax { all_rows ; agg } ; team 2 } ; ( h14 ) quarto } }
the sum of the points record of all rows is 490 .
Output: round_eq { sum { all_rows ; points } ; 490 }
for the high rebounds records of all rows , most of them fuzzily match to tim duncan .
Output: most_eq { all_rows ; high rebounds ; tim duncan }
select the rows whose internl tourism receipts 2011 ( million usd ) record fuzzily matches to n/d . there is only one such row in the table . the selected caribbean and n latin america countries record of this unqiue row is cuba . the revenues as % of exports goods and services 2011 record of this unqiue row is n / d .
Output: and { only { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } } ; and { eq { hop { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } ; selected caribbean and n latin america countries } ; cuba } ; eq { hop { filter_eq { all_rows ; internl tourism receipts 2011 ( million usd ) ; n/d } ; revenues as % of exports goods and services 2011 } ; n / d } } }
select the rows whose oberbayern record fuzzily matches to fc ingolstadt 04 . the minimum season record of these rows is 2005 .
Output: eq { min { filter_eq { all_rows ; oberbayern ; fc ingolstadt 04 } ; season } ; 2005 }
for the sprints classification records of all rows , most of them fuzzily match to rené weissinger .
Output: most_eq { all_rows ; sprints classification ; rené weissinger }
select the row whose attendance record of all rows is 4th maximum . the date record of this row is december 13 .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 4 } ; date } ; december 13 }
select the row whose year ( ceremony ) record of all rows is 2nd minimum . the director record of this row is shamil najafzadeh .
Output: eq { hop { nth_argmin { all_rows ; year ( ceremony ) ; 2 } ; director } ; shamil najafzadeh }
select the row whose earnings record of all rows is 2nd maximum . the player record of this row is tom watson .
Output: eq { hop { nth_argmax { all_rows ; earnings ; 2 } ; player } ; tom watson }
the average of the overall record of all rows is 125.1 .
Output: round_eq { avg { all_rows ; overall } ; 125.1 }
select the rows whose suburb / town record fuzzily matches to vincentia . select the row whose founded record of these rows is minimum . the school record of this row is vincentia public school .
Output: eq { hop { argmin { filter_eq { all_rows ; suburb / town ; vincentia } ; founded } ; school } ; vincentia public school }
select the rows whose election record is arbitrary . the number of such rows is 6 .
Output: eq { count { filter_all { all_rows ; election } } ; 6 }
select the row whose points record of all rows is 1st maximum . the name record of this row is jakim donaldson .
Output: eq { hop { nth_argmax { all_rows ; points ; 1 } ; name } ; jakim donaldson }
for the drawn records of all rows , most of them are greater than or equal to 1 .
Output: most_greater_eq { all_rows ; drawn ; 1 }
select the rows whose player record fuzzily matches to bob brown . take the pick record of this row . select the rows whose player record fuzzily matches to ted davis . take the pick record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; bob brown } ; pick } ; hop { filter_eq { all_rows ; player ; ted davis } ; pick } }