niryuu/sni-each-converted huggingface dataset:
select the rows whose team 1 record fuzzily matches to kastoria . take the agg score record of this row . select the rows whose team 1 record fuzzily matches to ilisiakos . take the agg score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; team 1 ; kastoria } ; agg score } ; hop { filter_eq { all_rows ; team 1 ; ilisiakos } ; agg score } }
select the rows whose asts record is equal to 0 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; asts ; 0 } } ; 2 }
select the rows whose incumbent record fuzzily matches to sam graves . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to russ carnahan . take the first elected record of this row . the second record is 4 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; incumbent ; sam graves } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; russ carnahan } ; first elected } } ; -4 }
select the rows whose lost record is equal to 0 . there is only one such row in the table . the team record of this unqiue row is são paulo .
Output: and { only { filter_eq { all_rows ; lost ; 0 } } ; eq { hop { filter_eq { all_rows ; lost ; 0 } ; team } ; são paulo } }
select the rows whose captain ( s ) record fuzzily matches to greg bird nate myles . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; captain ( s ) ; greg bird nate myles } } ; 2 }
the average of the floors record of all rows is 35 .
Output: round_eq { avg { all_rows ; floors } ; 35 }
for the format records of all rows , most of them fuzzily match to cd .
Output: most_eq { all_rows ; format ; cd }
the sum of the 3rd place record of all rows is 3 .
Output: round_eq { sum { all_rows ; 3rd place } ; 3 }
the average of the points record of all rows is 62 .
Output: round_eq { avg { all_rows ; points } ; 62 }
select the rows whose opponent in the final record fuzzily matches to vasilis mazarakis . there is only one such row in the table . the date record of this unqiue row is 20 march 2007 .
Output: and { only { filter_eq { all_rows ; opponent in the final ; vasilis mazarakis } } ; eq { hop { filter_eq { all_rows ; opponent in the final ; vasilis mazarakis } ; date } ; 20 march 2007 } }
for the engine records of all rows , most of them fuzzily match to ferrari v12 .
Output: most_eq { all_rows ; engine ; ferrari v12 }
select the rows whose pos record fuzzily matches to dnf . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; pos ; dnf } } ; 7 }
select the rows whose year record fuzzily matches to 1987 . take the points record of this row . select the rows whose year record fuzzily matches to 1995 . take the points record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; year ; 1987 } ; points } ; hop { filter_eq { all_rows ; year ; 1995 } ; points } }
select the rows whose director record fuzzily matches to silvio caiozzi . the minimum year ( ceremony ) record of these rows is 1990 : ( 63rd ) .
Output: eq { min { filter_eq { all_rows ; director ; silvio caiozzi } ; year ( ceremony ) } ; 1990 : ( 63rd ) }
select the row whose goals record of all rows is 3rd maximum . the name record of this row is dean windass .
Output: eq { hop { nth_argmax { all_rows ; goals ; 3 } ; name } ; dean windass }
for the attendance records of all rows , all of them are greater than 25000 .
Output: all_greater { all_rows ; attendance ; 25000 }
select the rows whose opposing team record fuzzily matches to natal . there is only one such row in the table . the date record of this unqiue row is may 17 , 1972 .
Output: and { only { filter_eq { all_rows ; opposing team ; natal } } ; eq { hop { filter_eq { all_rows ; opposing team ; natal } ; date } ; may 17 , 1972 } }
select the row whose crowd record of all rows is 1st maximum . the venue record of this row is mcg .
Output: eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; mcg }
select the rows whose incumbent record fuzzily matches to john lewis . take the elected record of this row . select the rows whose incumbent record fuzzily matches to nathan deal . take the elected record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; incumbent ; john lewis } ; elected } ; hop { filter_eq { all_rows ; incumbent ; nathan deal } ; elected } }
select the rows whose operator record fuzzily matches to london overground . among these rows , select the rows whose platform record is equal to 1 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; operator ; london overground } ; platform ; 1 } } ; 2 }
select the rows whose withdrawn record is equal to 1951 . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; withdrawn ; 1951 } } ; 5 }
select the rows whose opponent record fuzzily matches to cleveland browns . take the attendance record of this row . select the rows whose opponent record fuzzily matches to dallas cowboys . take the attendance record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opponent ; cleveland browns } ; attendance } ; hop { filter_eq { all_rows ; opponent ; dallas cowboys } ; attendance } }
select the rows whose surface record fuzzily matches to clay . the minimum date record of these rows is january 8 , 2001 .
Output: eq { min { filter_eq { all_rows ; surface ; clay } ; date } ; january 8 , 2001 }
select the rows whose growth rate record is less than 0 . there is only one such row in the table . the district record of this unqiue row is kolkata .
Output: and { only { filter_less { all_rows ; growth rate ; 0 } } ; eq { hop { filter_less { all_rows ; growth rate ; 0 } ; district } ; kolkata } }
select the row whose attendance record of all rows is 1st maximum . the home team record of this row is sunderland .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; home team } ; sunderland }
for the venue records of all rows , most of them fuzzily match to johannesburg .
Output: most_eq { all_rows ; venue ; johannesburg }
the sum of the earnings record of all rows is 206874 .
Output: round_eq { sum { all_rows ; earnings } ; 206874 }
the average of the aspect record of all rows is 4:3 .
Output: round_eq { avg { all_rows ; aspect } ; 4:3 }
select the rows whose pole position record fuzzily matches to alain prost . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; pole position ; alain prost } } ; 2 }
the average of the total record of all rows is 289.5 .
Output: round_eq { avg { all_rows ; total } ; 289.5 }
select the rows whose rank record is less than or equal to 3 . the average of the time record of these rows is 7:30.63 .
Output: round_eq { avg { filter_less_eq { all_rows ; rank ; 3 } ; time } ; 7:30.63 }
the maximum capacity record of all rows is 41040 . the team record of the row with superlative capacity record is dinamo minsk .
Output: and { eq { max { all_rows ; capacity } ; 41040 } ; eq { hop { argmax { all_rows ; capacity } ; team } ; dinamo minsk } }
select the rows whose name record is arbitrary . the number of such rows is 8 .
Output: eq { count { filter_all { all_rows ; name } } ; 8 }
select the rows whose place record fuzzily matches to reno , nv . there is only one such row in the table . the athlete record of this unqiue row is gwen torrence .
Output: and { only { filter_eq { all_rows ; place ; reno , nv } } ; eq { hop { filter_eq { all_rows ; place ; reno , nv } ; athlete } ; gwen torrence } }
select the row whose race 2 ( pts ) record of all rows is maximum . the country record of this row is france .
Output: eq { hop { argmax { all_rows ; race 2 ( pts ) } ; country } ; france }
select the rows whose votes record is greater than 80 . there is only one such row in the table . the artist record of this unqiue row is sahlene .
Output: and { only { filter_greater { all_rows ; votes ; 80 } } ; eq { hop { filter_greater { all_rows ; votes ; 80 } ; artist } ; sahlene } }
select the rows whose high points record is greater than ( 35 ) . there is only one such row in the table .
Output: only { filter_greater { all_rows ; high points ; ( 35 ) } }
select the rows whose name record fuzzily matches to bong hawkins . take the season record of this row . select the rows whose name record fuzzily matches to bakari hendrix . take the season record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; name ; bong hawkins } ; season } ; hop { filter_eq { all_rows ; name ; bakari hendrix } ; season } }
the average of the composition record of all rows is 99 .
Output: round_eq { avg { all_rows ; composition } ; 99 }
select the rows whose laws against homosexuality record fuzzily matches to male only . there is only one such row in the table . the country record of this unqiue row is uzbekistan .
Output: and { only { filter_eq { all_rows ; laws against homosexuality ; male only } } ; eq { hop { filter_eq { all_rows ; laws against homosexuality ; male only } ; country } ; uzbekistan } }
for the score records of all rows , most of them are greater than 350 .
Output: most_greater { all_rows ; score ; 350 }
select the rows whose nation record fuzzily matches to south korea . take the bronze record of this row . select the rows whose nation record fuzzily matches to estonia . take the bronze record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; nation ; south korea } ; bronze } ; hop { filter_eq { all_rows ; nation ; estonia } ; bronze } }
select the row whose area km 2 record of all rows is maximum . the official name record of this row is salisbury .
Output: eq { hop { argmax { all_rows ; area km 2 } ; official name } ; salisbury }
select the rows whose nation record fuzzily matches to spain . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; nation ; spain } } ; 3 }
select the row whose points record of all rows is maximum . the team record of this row is river plate .
Output: eq { hop { argmax { all_rows ; points } ; team } ; river plate }
for the chassis records of all rows , most of them fuzzily match to dallara .
Output: most_eq { all_rows ; chassis ; dallara }
the sum of the viewers ( in millions ) record of all rows is 260.8 / 254.8 .
Output: round_eq { sum { all_rows ; viewers ( in millions ) } ; 260.8 / 254.8 }
select the row whose year record of all rows is minimum . the nominee ( s ) record of this row is jenny bicks .
Output: eq { hop { argmin { all_rows ; year } ; nominee ( s ) } ; jenny bicks }
select the rows whose winner record fuzzily matches to ryan sutter . take the premiered record of this row . select the rows whose winner record fuzzily matches to jesse csincsak . take the premiered record of this row . the first record is less than the second record . the premiered record of the first row is january 8 , 2003 . the premiered record of the second row is may 19 , 2008 .
Output: and { less { hop { filter_eq { all_rows ; winner ; ryan sutter } ; premiered } ; hop { filter_eq { all_rows ; winner ; jesse csincsak } ; premiered } } ; and { eq { hop { filter_eq { all_rows ; winner ; ryan sutter } ; premiered } ; january 8 , 2003 } ; eq { hop { filter_eq { all_rows ; winner ; jesse csincsak } ; premiered } ; may 19 , 2008 } } }
select the row whose crowd record of all rows is maximum . the venue record of this row is princes park .
Output: eq { hop { argmax { all_rows ; crowd } ; venue } ; princes park }
select the rows whose rider record fuzzily matches to suzuki . there is only one such row in the table .
Output: only { filter_eq { all_rows ; rider ; suzuki } }
select the row whose first elected record of all rows is 2nd minimum . the candidates record of this row is charles edward bennett ( d ) unopposed .
Output: eq { hop { nth_argmin { all_rows ; first elected ; 2 } ; candidates } ; charles edward bennett ( d ) unopposed }
the average of the votes record of all rows is 13004 .
Output: round_eq { avg { all_rows ; votes } ; 13004 }
select the rows whose partnering record fuzzily matches to neuza silva . there is only one such row in the table . the edition record of this unqiue row is 2010 fed cup europe / africa group i .
Output: and { only { filter_eq { all_rows ; partnering ; neuza silva } } ; eq { hop { filter_eq { all_rows ; partnering ; neuza silva } ; edition } ; 2010 fed cup europe / africa group i } }
select the rows whose pos record fuzzily matches to qb . the average of the contract record of these rows is 2 .
Output: round_eq { avg { filter_eq { all_rows ; pos ; qb } ; contract } ; 2 }
for the party records of all rows , most of them fuzzily match to jacksonian .
Output: most_eq { all_rows ; party ; jacksonian }
select the row whose date record of all rows is 3rd minimum . the team record of this row is san antonio .
Output: eq { hop { nth_argmin { all_rows ; date ; 3 } ; team } ; san antonio }
select the row whose multi lane record of all rows is maximum . the category wise record of this row is national highways .
Output: eq { hop { argmax { all_rows ; multi lane } ; category wise } ; national highways }
the sum of the gold record of all rows is 3 .
Output: round_eq { sum { all_rows ; gold } ; 3 }
select the rows whose season record fuzzily matches to 2009 . select the row whose points record of these rows is maximum . the club record of this row is barcelona .
Output: eq { hop { argmax { filter_eq { all_rows ; season ; 2009 } ; points } ; club } ; barcelona }
select the row whose total points record of all rows is 2nd maximum . the player record of this row is kenny sanders .
Output: eq { hop { nth_argmax { all_rows ; total points ; 2 } ; player } ; kenny sanders }
the maximum crowd record of all rows is 26500 . the date record of the row with superlative crowd record is 4 august 1951 .
Output: and { eq { max { all_rows ; crowd } ; 26500 } ; eq { hop { argmax { all_rows ; crowd } ; date } ; 4 august 1951 } }
select the rows whose venue record fuzzily matches to candlestick park . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; venue ; candlestick park } } ; 3 }
select the rows whose directed by record fuzzily matches to andy cadiff . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; directed by ; andy cadiff } } ; 3 }
select the rows whose average record is equal to 32 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; average ; 32 } } ; 3 }
for the standard records of all rows , most of them fuzzily match to umts hspa .
Output: most_eq { all_rows ; standard ; umts hspa }
for the signal power records of all rows , all of them fuzzily match to 10 kw .
Output: all_eq { all_rows ; signal power ; 10 kw }
select the row whose kilometer record of all rows is maximum . the number record of this row is 31 .
Output: eq { hop { argmax { all_rows ; kilometer } ; number } ; 31 }
select the rows whose type record fuzzily matches to gravity . among these rows , select the rows whose drainage basin ( km square ) record is equal to 0.0 . there is only one such row in the table . the location record of this unqiue row is morcín .
Output: and { only { filter_eq { filter_eq { all_rows ; type ; gravity } ; drainage basin ( km square ) ; 0.0 } } ; eq { hop { filter_eq { filter_eq { all_rows ; type ; gravity } ; drainage basin ( km square ) ; 0.0 } ; location } ; morcín } }
select the rows whose model number record fuzzily matches to atom z550 . take the tdp record of this row . select the rows whose model number record fuzzily matches to atom z510 . take the tdp record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; model number ; atom z550 } ; tdp } ; hop { filter_eq { all_rows ; model number ; atom z510 } ; tdp } }
select the row whose winner record of all rows is maximum . the episode record of this row is 2x10 .
Output: eq { hop { argmax { all_rows ; winner } ; episode } ; 2x10 }
select the rows whose score record fuzzily matches to 138 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; score ; 138 } } ; 3 }
select the rows whose scientific name record fuzzily matches to chamaeleo calyptratus . take the length ( female ) record of this row . select the rows whose scientific name record fuzzily matches to rhampholeon spectrum . take the length ( female ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; scientific name ; chamaeleo calyptratus } ; length ( female ) } ; hop { filter_eq { all_rows ; scientific name ; rhampholeon spectrum } ; length ( female ) } }
the average of the attendance record of all rows is 33151 .
Output: round_eq { avg { all_rows ; attendance } ; 33151 }
select the rows whose year record fuzzily matches to 1983 . take the points record of this row . select the rows whose year record fuzzily matches to 1984 . take the points record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; year ; 1983 } ; points } ; hop { filter_eq { all_rows ; year ; 1984 } ; points } }
for the score records of all rows , most of them are less than or equal to 3 .
Output: most_less_eq { all_rows ; score ; 3 }
select the row whose rnd record of all rows is 1st minimum . the date record of this row is march 14 .
Output: eq { hop { nth_argmin { all_rows ; rnd ; 1 } ; date } ; march 14 }
select the row whose margin record of all rows is maximum . the year record of this row is 1980 .
Output: eq { hop { argmax { all_rows ; margin } ; year } ; 1980 }
select the rows whose team record fuzzily matches to south china . take the loss record of this row . select the rows whose team record fuzzily matches to xiangxue pharmaceutical . take the loss record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; team ; south china } ; loss } ; hop { filter_eq { all_rows ; team ; xiangxue pharmaceutical } ; loss } }
select the rows whose average record is equal to 8.0 . select the row whose total record of these rows is minimum . the player record of this row is finbarr delaney .
Output: eq { hop { argmin { filter_eq { all_rows ; average ; 8.0 } ; total } ; player } ; finbarr delaney }
select the rows whose outcome record fuzzily matches to winner . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; outcome ; winner } } ; 7 }
select the row whose capacity record of all rows is maximum . the location record of this row is minsk .
Output: eq { hop { argmax { all_rows ; capacity } ; location } ; minsk }
select the rows whose english title record fuzzily matches to sergey lazo . take the length record of this row . select the rows whose english title record fuzzily matches to sour grape . take the length record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; english title ; sergey lazo } ; length } ; hop { filter_eq { all_rows ; english title ; sour grape } ; length } }
for the family friendly records of all rows , most of them fuzzily match to yes .
Output: most_eq { all_rows ; family friendly ; yes }
select the row whose attendance record of all rows is 2nd maximum . the date record of this row is 9 april 2000 .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; date } ; 9 april 2000 }
select the rows whose horse name record fuzzily matches to c p west . take the lengths behind record of this row . select the rows whose horse name record fuzzily matches to hard spun . take the lengths behind record of this row . the first record is 1 1/2 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; horse name ; c p west } ; lengths behind } ; hop { filter_eq { all_rows ; horse name ; hard spun } ; lengths behind } } ; 1 1/2 }
the average of the time record of all rows is 1:00.25 .
Output: round_eq { avg { all_rows ; time } ; 1:00.25 }
for the method records of all rows , most of them fuzzily match to tko .
Output: most_eq { all_rows ; method ; tko }
select the rows whose gender record fuzzily matches to m . the maximum votes record of these rows is 11542 .
Output: eq { max { filter_eq { all_rows ; gender ; m } ; votes } ; 11542 }
the average of the horwood record of all rows is 8 .
Output: round_eq { avg { all_rows ; horwood } ; 8 }
for the crowd records of all rows , most of them are greater than or equal to 5000 .
Output: most_greater_eq { all_rows ; crowd ; 5000 }
select the rows whose country record fuzzily matches to mexico . there is only one such row in the table . the player record of this unqiue row is lorena ochoa .
Output: and { only { filter_eq { all_rows ; country ; mexico } } ; eq { hop { filter_eq { all_rows ; country ; mexico } ; player } ; lorena ochoa } }
for the surface records of all rows , most of them fuzzily match to clay .
Output: most_eq { all_rows ; surface ; clay }
the sum of the 1st prize record of all rows is 241700 .
Output: round_eq { sum { all_rows ; 1st prize } ; 241700 }
select the row whose result record of all rows is maximum . the opponent record of this row is pittsburgh steelers .
Output: eq { hop { argmax { all_rows ; result } ; opponent } ; pittsburgh steelers }
select the rows whose transfer fee record fuzzily matches to free . among these rows , select the rows whose moving from record fuzzily matches to everton . the number of such rows is 1 .
Output: eq { count { filter_eq { filter_eq { all_rows ; transfer fee ; free } ; moving from ; everton } } ; 1 }
select the row whose car record of all rows is minimum . the driver record of this row is mike skinner .
Output: eq { hop { argmin { all_rows ; car } ; driver } ; mike skinner }
the sum of the laps record of all rows is 522 .
Output: round_eq { sum { all_rows ; laps } ; 522 }
select the row whose crowd record of all rows is 1st maximum . the venue record of this row is victoria park .
Output: eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; victoria park }
select the rows whose fat32 record fuzzily matches to yes . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; fat32 ; yes } } ; 4 }
for the nationality records of all rows , most of them fuzzily match to french .
Output: most_eq { all_rows ; nationality ; french }
select the rows whose college record fuzzily matches to detroit mercy . there is only one such row in the table . the player record of this unqiue row is joe kopicki .
Output: and { only { filter_eq { all_rows ; college ; detroit mercy } } ; eq { hop { filter_eq { all_rows ; college ; detroit mercy } ; player } ; joe kopicki } }
for the court surface records of all rows , most of them fuzzily match to hard .
Output: most_eq { all_rows ; court surface ; hard }
select the rows whose tournament record fuzzily matches to bath . take the date record of this row . select the rows whose tournament record fuzzily matches to hull . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; tournament ; bath } ; date } ; hop { filter_eq { all_rows ; tournament ; hull } ; date } }
select the rows whose round record is equal to 3 . there is only one such row in the table . the opponent record of this unqiue row is ian freeman .
Output: and { only { filter_eq { all_rows ; round ; 3 } } ; eq { hop { filter_eq { all_rows ; round ; 3 } ; opponent } ; ian freeman } }
select the row whose crowd record of all rows is maximum . the home team record of this row is st kilda . the away team record of this row is melbourne .
Output: and { eq { hop { argmax { all_rows ; crowd } ; home team } ; st kilda } ; eq { hop { argmax { all_rows ; crowd } ; away team } ; melbourne } }
select the row whose winning score record of all rows is maximum . the year record of this row is 2001 .
Output: eq { hop { argmax { all_rows ; winning score } ; year } ; 2001 }
select the row whose goals against record of all rows is 2nd maximum . the club record of this row is baracaldo ah .
Output: eq { hop { nth_argmax { all_rows ; goals against ; 2 } ; club } ; baracaldo ah }
select the rows whose arena ( capacity ) record fuzzily matches to 5000 . among these rows , select the rows whose previous season record is less than or equal to 5 . the number of such rows is 2 .
Output: eq { count { filter_less_eq { filter_eq { all_rows ; arena ( capacity ) ; 5000 } ; previous season ; 5 } } ; 2 }
select the rows whose season record is less than or equal to 1958 . the average of the attendance record of these rows is 10158.5 .
Output: round_eq { avg { filter_less_eq { all_rows ; season ; 1958 } ; attendance } ; 10158.5 }
select the row whose tonnage record of all rows is maximum . the name record of this row is northern eagle .
Output: eq { hop { argmax { all_rows ; tonnage } ; name } ; northern eagle }
for the prize records of all rows , most of them are greater than 500,000 .
Output: most_greater { all_rows ; prize ; 500,000 }
the sum of the total record of all rows is 2548 .
Output: round_eq { sum { all_rows ; total } ; 2548 }
select the rows whose fate record fuzzily matches to sunk . among these rows , select the rows whose nationality record fuzzily matches to great britain . the number of such rows is 9 .
Output: eq { count { filter_eq { filter_eq { all_rows ; fate ; sunk } ; nationality ; great britain } } ; 9 }
select the rows whose country record fuzzily matches to united states . the average of the to par record of these rows is 9.67 .
Output: round_eq { avg { filter_eq { all_rows ; country ; united states } ; to par } ; 9.67 }
the 3rd maximum no of episodes record of all rows is 135 .
Output: eq { nth_max { all_rows ; no of episodes ; 3 } ; 135 }
the average of the length record of all rows is 55 .
Output: round_eq { avg { all_rows ; length } ; 55 }
select the rows whose episode record fuzzily matches to episode 5 . take the viewing figure record of this row . select the rows whose episode record fuzzily matches to episode 7 . take the viewing figure record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; episode ; episode 5 } ; viewing figure } ; hop { filter_eq { all_rows ; episode ; episode 7 } ; viewing figure } }
select the rows whose position record fuzzily matches to defensive end . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; position ; defensive end } } ; 2 }
the sum of the number in service record of all rows is 310 .
Output: round_eq { sum { all_rows ; number in service } ; 310 }
select the rows whose supercopa sudamericana 1996 record fuzzily matches to did not qualify . among these rows , select the rows whose copa conmebol 1996 record fuzzily matches to semifinals . there is only one such row in the table . the team record of this unqiue row is vasco . the copa libertadores 1996 record of this unqiue row is did not qualify .
Output: and { only { filter_eq { filter_eq { all_rows ; supercopa sudamericana 1996 ; did not qualify } ; copa conmebol 1996 ; semifinals } } ; and { eq { hop { filter_eq { filter_eq { all_rows ; supercopa sudamericana 1996 ; did not qualify } ; copa conmebol 1996 ; semifinals } ; team } ; vasco } ; eq { hop { filter_eq { filter_eq { all_rows ; supercopa sudamericana 1996 ; did not qualify } ; copa conmebol 1996 ; semifinals } ; copa libertadores 1996 } ; did not qualify } } }
select the rows whose station record fuzzily matches to kami - mio . take the distance ( km ) record of this row . select the rows whose station record fuzzily matches to funao . take the distance ( km ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; station ; kami - mio } ; distance ( km ) } ; hop { filter_eq { all_rows ; station ; funao } ; distance ( km ) } }
select the rows whose building record fuzzily matches to duke tower ( office ) . take the height record of this row . select the rows whose building record fuzzily matches to summer gardens ( residential ) . take the height record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; building ; duke tower ( office ) } ; height } ; hop { filter_eq { all_rows ; building ; summer gardens ( residential ) } ; height } }
select the rows whose reason for termination record fuzzily matches to death . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; reason for termination ; death } } ; 7 }
select the rows whose tournament record fuzzily matches to bc open . take the margin of victory record of this row . select the rows whose tournament record fuzzily matches to glen campbell - los angeles open . take the margin of victory record of this row . the first record is 3 larger than the second record . the margin of victory record of the first row is 5 strokes . the margin of victory record of the second row is 2 strokes .
Output: and { eq { diff { hop { filter_eq { all_rows ; tournament ; bc open } ; margin of victory } ; hop { filter_eq { all_rows ; tournament ; glen campbell - los angeles open } ; margin of victory } } ; 3 } ; and { eq { hop { filter_eq { all_rows ; tournament ; bc open } ; margin of victory } ; 5 strokes } ; eq { hop { filter_eq { all_rows ; tournament ; glen campbell - los angeles open } ; margin of victory } ; 2 strokes } } }
select the rows whose lyricist record fuzzily matches to sameer . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; lyricist ; sameer } } ; 3 }
select the rows whose written by record fuzzily matches to danielle evenson . there is only one such row in the table . the title record of this unqiue row is long days journey into stan .
Output: and { only { filter_eq { all_rows ; written by ; danielle evenson } } ; eq { hop { filter_eq { all_rows ; written by ; danielle evenson } ; title } ; long days journey into stan } }
select the row whose total viewers ( in millions ) record of all rows is maximum . the title record of this row is our fathers .
Output: eq { hop { argmax { all_rows ; total viewers ( in millions ) } ; title } ; our fathers }
for the high points records of all rows , most of them fuzzily match to lebron james .
Output: most_eq { all_rows ; high points ; lebron james }
the average of the orangemen points record of all rows is 20.3 .
Output: round_eq { avg { all_rows ; orangemen points } ; 20.3 }
select the rows whose position record fuzzily matches to running back . there is only one such row in the table . the player record of this unqiue row is doug goodwin .
Output: and { only { filter_eq { all_rows ; position ; running back } } ; eq { hop { filter_eq { all_rows ; position ; running back } ; player } ; doug goodwin } }
select the rows whose club record fuzzily matches to cd sabadell cf . take the wins record of this row . select the rows whose club record fuzzily matches to deportivo alavés . take the wins record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; club ; cd sabadell cf } ; wins } ; hop { filter_eq { all_rows ; club ; deportivo alavés } ; wins } }
select the rows whose candidates record fuzzily matches to david bard . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; candidates ; david bard } } ; 2 }
select the rows whose gold record is greater than 0 . for the total records of these rows , most of them are less than 10 .
Output: most_less { filter_greater { all_rows ; gold ; 0 } ; total ; 10 }
select the rows whose week of record fuzzily matches to 2 october . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; week of ; 2 october } } ; 4 }
for the date records of all rows , all of them fuzzily match to november .
Output: all_eq { all_rows ; date ; november }
the average of the roll record of all rows is 338.66 .
Output: round_eq { avg { all_rows ; roll } ; 338.66 }
select the rows whose nation record fuzzily matches to china ( chn ) . take the bronze record of this row . select the rows whose nation record fuzzily matches to thailand ( tha ) . take the bronze record of this row . the first record is 1 larger than the second record .
Output: eq { diff { hop { filter_eq { all_rows ; nation ; china ( chn ) } ; bronze } ; hop { filter_eq { all_rows ; nation ; thailand ( tha ) } ; bronze } } ; 1 }
select the rows whose builder record fuzzily matches to denny , dunbarton . select the row whose laid down record of these rows is 1st minimum . the name record of this row is sutlej .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; builder ; denny , dunbarton } ; laid down ; 1 } ; name } ; sutlej }
select the rows whose surface record fuzzily matches to clay . for the outcome records of these rows , most of them fuzzily match to runner - up .
Output: most_eq { filter_eq { all_rows ; surface ; clay } ; outcome ; runner - up }
select the rows whose goalkeeper record fuzzily matches to juan calatayud . take the goals record of this row . select the rows whose goalkeeper record fuzzily matches to eduardo navarro . take the goals record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; goalkeeper ; juan calatayud } ; goals } ; hop { filter_eq { all_rows ; goalkeeper ; eduardo navarro } ; goals } }
the average of the age record of all rows is 23.7 .
Output: round_eq { avg { all_rows ; age } ; 23.7 }
select the rows whose satellite record fuzzily matches to echo 2 . take the mass ( kg ) record of this row . select the rows whose satellite record fuzzily matches to mylar balloon . take the mass ( kg ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; satellite ; echo 2 } ; mass ( kg ) } ; hop { filter_eq { all_rows ; satellite ; mylar balloon } ; mass ( kg ) } }
for the location records of all rows , most of them fuzzily match to yankee stadium .
Output: most_eq { all_rows ; location ; yankee stadium }
the sum of the runs record of all rows is 2296 .
Output: round_eq { sum { all_rows ; runs } ; 2296 }
select the rows whose race record fuzzily matches to flight stakes . take the date record of this row . select the rows whose race record fuzzily matches to cox plate . take the date record of this row . the second record is 21 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; race ; flight stakes } ; date } ; hop { filter_eq { all_rows ; race ; cox plate } ; date } } ; -21 }
select the rows whose leading scorer record fuzzily matches to chris mills . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; leading scorer ; chris mills } } ; 5 }
select the rows whose language record fuzzily matches to yiddish . take the number record of this row . select the rows whose language record fuzzily matches to russian . take the number record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; language ; yiddish } ; number } ; hop { filter_eq { all_rows ; language ; russian } ; number } }
select the rows whose nation record fuzzily matches to puerto rico . take the bronze record of this row . select the rows whose nation record fuzzily matches to barbados . take the bronze record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; nation ; puerto rico } ; bronze } ; hop { filter_eq { all_rows ; nation ; barbados } ; bronze } }
for the years records of all rows , most of them fuzzily match to 2001 - 2004 .
Output: most_eq { all_rows ; years ; 2001 - 2004 }
the sum of the goals record of all rows is 1158 .
Output: round_eq { sum { all_rows ; goals } ; 1158 }
select the rows whose winner record fuzzily matches to layne beachley ( aus ) . there is only one such row in the table .
Output: only { filter_eq { all_rows ; winner ; layne beachley ( aus ) } }
select the rows whose office record fuzzily matches to former governor . select the row whose popular vote record of these rows is minimum . the candidate record of this row is buddy roemer .
Output: eq { hop { argmin { filter_eq { all_rows ; office ; former governor } ; popular vote } ; candidate } ; buddy roemer }
for the date records of all rows , most of them are greater than 1980 .
Output: most_greater { all_rows ; date ; 1980 }
for the government records of all rows , most of them fuzzily match to cumann na ngaedheal government .
Output: most_eq { all_rows ; government ; cumann na ngaedheal government }
for the date records of all rows , all of them fuzzily match to 198 .
Output: all_eq { all_rows ; date ; 198 }
select the rows whose date record fuzzily matches to 1988 . take the score record of this row . select the rows whose date record fuzzily matches to 1986 . take the score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; date ; 1988 } ; score } ; hop { filter_eq { all_rows ; date ; 1986 } ; score } }
select the row whose top - 25 record of all rows is maximum . the tournament record of this row is the open championship .
Output: eq { hop { argmax { all_rows ; top - 25 } ; tournament } ; the open championship }
select the row whose pick record of all rows is 4th maximum . the player record of this row is alvin mckinley .
Output: eq { hop { nth_argmax { all_rows ; pick ; 4 } ; player } ; alvin mckinley }
the average of the candidates record of all rows is 60 .
Output: round_eq { avg { all_rows ; candidates } ; 60 }
select the rows whose emperor 's cup record fuzzily matches to semi - final . there is only one such row in the table . the season record of this unqiue row is 2000 .
Output: and { only { filter_eq { all_rows ; emperor 's cup ; semi - final } } ; eq { hop { filter_eq { all_rows ; emperor 's cup ; semi - final } ; season } ; 2000 } }
select the rows whose game site record fuzzily matches to olympic stadium . there is only one such row in the table . the date record of this unqiue row is sunday , april 4 .
Output: and { only { filter_eq { all_rows ; game site ; olympic stadium } } ; eq { hop { filter_eq { all_rows ; game site ; olympic stadium } ; date } ; sunday , april 4 } }
select the rows whose blank ends record is less than 10 . there is only one such row in the table . the skip record of this unqiue row is johnny frederiksen .
Output: and { only { filter_less { all_rows ; blank ends ; 10 } } ; eq { hop { filter_less { all_rows ; blank ends ; 10 } ; skip } ; johnny frederiksen } }
select the row whose enrollment record of all rows is maximum . the institution record of this row is east carolina university .
Output: eq { hop { argmax { all_rows ; enrollment } ; institution } ; east carolina university }
select the rows whose country record fuzzily matches to australia . there is only one such row in the table . the player record of this unqiue row is mathew goggin .
Output: and { only { filter_eq { all_rows ; country ; australia } } ; eq { hop { filter_eq { all_rows ; country ; australia } ; player } ; mathew goggin } }
select the rows whose result record fuzzily matches to final . the sum of the jury votes record of these rows is 29 .
Output: round_eq { sum { filter_eq { all_rows ; result ; final } ; jury votes } ; 29 }
select the rows whose outcome record fuzzily matches to runner - up . among these rows , select the rows whose surface record fuzzily matches to clay . the number of such rows is 5 .
Output: eq { count { filter_eq { filter_eq { all_rows ; outcome ; runner - up } ; surface ; clay } } ; 5 }
the average of the podiums record of all rows is 48 .
Output: round_eq { avg { all_rows ; podiums } ; 48 }
for the draws records of all rows , most of them are greater than or equal to 5 .
Output: most_greater_eq { all_rows ; draws ; 5 }
select the rows whose country record fuzzily matches to italy . there is only one such row in the table . the pilot record of this unqiue row is thomas gostner .
Output: and { only { filter_eq { all_rows ; country ; italy } } ; eq { hop { filter_eq { all_rows ; country ; italy } ; pilot } ; thomas gostner } }
select the row whose date record of all rows is minimum . the venue record of this row is corio oval .
Output: eq { hop { argmin { all_rows ; date } ; venue } ; corio oval }
select the row whose bronze record of all rows is maximum . the nation record of this row is france ( fra ) .
Output: eq { hop { argmax { all_rows ; bronze } ; nation } ; france ( fra ) }
for the number of aircraft records of all rows , most of them are greater than 40 .
Output: most_greater { all_rows ; number of aircraft ; 40 }
select the rows whose date record fuzzily matches to october . the sum of the attendance record of these rows is 142040 .
Output: round_eq { sum { filter_eq { all_rows ; date ; october } ; attendance } ; 142040 }
select the rows whose displacement cc record is greater than 2000 . the number of such rows is 2 .
Output: eq { count { filter_greater { all_rows ; displacement cc ; 2000 } } ; 2 }
select the rows whose player record is arbitrary . the number of such rows is 7 .
Output: eq { count { filter_all { all_rows ; player } } ; 7 }
select the rows whose equatorial bulge record is less than 100 . select the row whose equatorial diameter record of these rows is 1st maximum . the body record of this row is earth .
Output: eq { hop { nth_argmax { filter_less { all_rows ; equatorial bulge ; 100 } ; equatorial diameter ; 1 } ; body } ; earth }
select the rows whose earpads record fuzzily matches to comfort pads . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; earpads ; comfort pads } } ; 4 }
select the rows whose written by record fuzzily matches to james wood . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; written by ; james wood } } ; 5 }
select the rows whose home team score record is greater than 20 . there is only one such row in the table .
Output: only { filter_greater { all_rows ; home team score ; 20 } }
for the league cup records of all rows , most of them are equal to 0 .
Output: most_eq { all_rows ; league cup ; 0 }
select the row whose attendance record of all rows is 1st maximum . the opponent record of this row is 7 usc .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; opponent } ; 7 usc }
select the rows whose surface record fuzzily matches to hard . for the outcome records of these rows , most of them fuzzily match to winner .
Output: most_eq { filter_eq { all_rows ; surface ; hard } ; outcome ; winner }
select the rows whose aspect record fuzzily matches to 16:9 . there is only one such row in the table . the channel record of this unqiue row is 31.1 .
Output: and { only { filter_eq { all_rows ; aspect ; 16:9 } } ; eq { hop { filter_eq { all_rows ; aspect ; 16:9 } ; channel } ; 31.1 } }
select the row whose us viewers ( millions ) record of all rows is maximum . the original air date record of this row is november 7 , 2010 .
Output: eq { hop { argmax { all_rows ; us viewers ( millions ) } ; original air date } ; november 7 , 2010 }
the sum of the gold record of all rows is 36 .
Output: round_eq { sum { all_rows ; gold } ; 36 }
select the rows whose rank record is less than or equal to 10 . the number of such rows is 8 .
Output: eq { count { filter_less_eq { all_rows ; rank ; 10 } } ; 8 }
select the rows whose area record fuzzily matches to gore . the number of such rows is 7 .
Output: eq { count { filter_eq { all_rows ; area ; gore } } ; 7 }
select the rows whose 1st leg record fuzzily matches to 0-0 . there is only one such row in the table . the team 1 record of this unqiue row is são paulo . the team 2 record of this unqiue row is nacional .
Output: and { only { filter_eq { all_rows ; 1st leg ; 0-0 } } ; and { eq { hop { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 1 } ; são paulo } ; eq { hop { filter_eq { all_rows ; 1st leg ; 0-0 } ; team 2 } ; nacional } } }
select the rows whose women 's singles record fuzzily matches to juliane schenk . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; women 's singles ; juliane schenk } } ; 3 }
the average of the sequence similarity record of all rows is 70 .
Output: round_eq { avg { all_rows ; sequence similarity } ; 70 }
select the rows whose opponent record fuzzily matches to minnesota vikings . among these rows , select the rows whose result record fuzzily matches to l . the number of such rows is 1 .
Output: eq { count { filter_eq { filter_eq { all_rows ; opponent ; minnesota vikings } ; result ; l } } ; 1 }
select the rows whose result record fuzzily matches to won . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; result ; won } } ; 2 }
select the row whose attendance record of all rows is maximum . the date record of this row is sunday , may 29 .
Output: eq { hop { argmax { all_rows ; attendance } ; date } ; sunday , may 29 }
select the rows whose year record fuzzily matches to sophomore . the average of the weight record of these rows is 192.5 .
Output: round_eq { avg { filter_eq { all_rows ; year ; sophomore } ; weight } ; 192.5 }
select the rows whose country record fuzzily matches to australia . there is only one such row in the table . the player record of this unqiue row is geoff ogilvy .
Output: and { only { filter_eq { all_rows ; country ; australia } } ; eq { hop { filter_eq { all_rows ; country ; australia } ; player } ; geoff ogilvy } }
select the rows whose athlete record fuzzily matches to thuso mpuang . take the time record of this row . select the rows whose athlete record fuzzily matches to wallace spearmon . take the time record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; athlete ; thuso mpuang } ; time } ; hop { filter_eq { all_rows ; athlete ; wallace spearmon } ; time } }
select the rows whose make record fuzzily matches to dodge . there is only one such row in the table .
Output: only { filter_eq { all_rows ; make ; dodge } }
select the rows whose wins record is not equal to 0 . there is only one such row in the table . the tournament record of this unqiue row is pga championship .
Output: and { only { filter_not_eq { all_rows ; wins ; 0 } } ; eq { hop { filter_not_eq { all_rows ; wins ; 0 } ; tournament } ; pga championship } }
for the extra records of all rows , most of them fuzzily match to 200 m .
Output: most_eq { all_rows ; extra ; 200 m }
for the chassis records of all rows , most of them fuzzily match to kurtis kraft 500b .
Output: most_eq { all_rows ; chassis ; kurtis kraft 500b }
select the row whose to par record of all rows is 2nd maximum . the player record of this row is mark hensby .
Output: eq { hop { nth_argmax { all_rows ; to par ; 2 } ; player } ; mark hensby }
select the rows whose role record fuzzily matches to guy holden . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; role ; guy holden } } ; 3 }
select the row whose event record of all rows is 2nd maximum . the athlete record of this row is mostafa abdollahi .
Output: eq { hop { nth_argmax { all_rows ; event ; 2 } ; athlete } ; mostafa abdollahi }
select the rows whose team 1 record fuzzily matches to elche . take the 2nd leg record of this row . select the rows whose team 1 record fuzzily matches to numancia . take the 2nd leg record of this row . the first record fuzzily matches to the second record . the 2nd leg record of the first row is 0 - 2 . the 2nd leg record of the second row is 0 - 2 .
Output: and { eq { hop { filter_eq { all_rows ; team 1 ; elche } ; 2nd leg } ; hop { filter_eq { all_rows ; team 1 ; numancia } ; 2nd leg } } ; and { eq { hop { filter_eq { all_rows ; team 1 ; elche } ; 2nd leg } ; 0 - 2 } ; eq { hop { filter_eq { all_rows ; team 1 ; numancia } ; 2nd leg } ; 0 - 2 } } }
select the rows whose name record fuzzily matches to andreas kofler . take the points record of this row . select the rows whose name record fuzzily matches to tom hilde . take the points record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; name ; andreas kofler } ; points } ; hop { filter_eq { all_rows ; name ; tom hilde } ; points } }
select the row whose birthdate record of all rows is minimum . the name record of this row is phil verchota .
Output: eq { hop { argmin { all_rows ; birthdate } ; name } ; phil verchota }