niryuu/sni-each-converted huggingface dataset:
select the rows whose season record fuzzily matches to 2010 . take the podium record of this row . select the rows whose season record fuzzily matches to 2011 . take the podium record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; season ; 2010 } ; podium } ; hop { filter_eq { all_rows ; season ; 2011 } ; podium } }
select the row whose caps record of all rows is maximum . the name record of this row is ronaldo .
Output: eq { hop { argmax { all_rows ; caps } ; name } ; ronaldo }
for the team records of all rows , all of them fuzzily match to honda .
Output: all_eq { all_rows ; team ; honda }
select the rows whose indigenous mining production 2006 record is equal to 0 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; indigenous mining production 2006 ; 0 } } ; 4 }
select the rows whose team record fuzzily matches to detroit . among these rows , select the rows whose high rebounds record fuzzily matches to shaquille o'neal . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; team ; detroit } ; high rebounds ; shaquille o'neal } } ; 3 }
for the date records of all rows , all of them fuzzily match to april .
Output: all_eq { all_rows ; date ; april }
select the row whose original air date record of all rows is 2nd minimum . the celebrity record of this row is margot kidder .
Output: eq { hop { nth_argmin { all_rows ; original air date ; 2 } ; celebrity } ; margot kidder }
select the rows whose home team record fuzzily matches to melbourne . among these rows , select the rows whose crowd record is greater than 10000 . there is only one such row in the table . the venue record of this unqiue row is mcg .
Output: and { only { filter_greater { filter_eq { all_rows ; home team ; melbourne } ; crowd ; 10000 } } ; eq { hop { filter_greater { filter_eq { all_rows ; home team ; melbourne } ; crowd ; 10000 } ; venue } ; mcg } }
select the rows whose player record fuzzily matches to jon koncak . take the years in orlando record of this row . select the rows whose player record fuzzily matches to tim kempton . take the years in orlando record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; jon koncak } ; years in orlando } ; hop { filter_eq { all_rows ; player ; tim kempton } ; years in orlando } }
for the format ( s ) records of all rows , most of them fuzzily match to cd .
Output: most_eq { all_rows ; format ( s ) ; cd }
select the rows whose date record fuzzily matches to july 2010 . there is only one such row in the table .
Output: only { filter_eq { all_rows ; date ; july 2010 } }
select the rows whose result record fuzzily matches to drew . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; result ; drew } } ; 2 }
select the rows whose date record fuzzily matches to 2013 . there is only one such row in the table . the tournament record of this unqiue row is oklahoma city , usa f9 .
Output: and { only { filter_eq { all_rows ; date ; 2013 } } ; eq { hop { filter_eq { all_rows ; date ; 2013 } ; tournament } ; oklahoma city , usa f9 } }
select the row whose date record of all rows is 2nd minimum . the venue record of this row is port of spain , trinidad and tobago .
Output: eq { hop { nth_argmin { all_rows ; date ; 2 } ; venue } ; port of spain , trinidad and tobago }
select the rows whose nation record fuzzily matches to canada . there is only one such row in the table . the name record of this unqiue row is barbara underhill / paul martini .
Output: and { only { filter_eq { all_rows ; nation ; canada } } ; eq { hop { filter_eq { all_rows ; nation ; canada } ; name } ; barbara underhill / paul martini } }
select the rows whose song record fuzzily matches to ek ladki ko dekha . take the year record of this row . select the rows whose song record fuzzily matches to yeh kaali kaali aankhen . take the year record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; song ; ek ladki ko dekha } ; year } ; hop { filter_eq { all_rows ; song ; yeh kaali kaali aankhen } ; year } }
select the rows whose water park record fuzzily matches to water country usa . take the rank record of this row . select the rows whose water park record fuzzily matches to siam water park . take the rank record of this row . the first record is 2 larger than the second record . the rank record of the first row is 20 . the rank record of the second row is 18 .
Output: and { eq { diff { hop { filter_eq { all_rows ; water park ; water country usa } ; rank } ; hop { filter_eq { all_rows ; water park ; siam water park } ; rank } } ; 2 } ; and { eq { hop { filter_eq { all_rows ; water park ; water country usa } ; rank } ; 20 } ; eq { hop { filter_eq { all_rows ; water park ; siam water park } ; rank } ; 18 } } }
select the rows whose incumbent record fuzzily matches to jimmy duncan jr . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to zach wamp . 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 ; jimmy duncan jr } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; zach wamp } ; first elected } }
select the row whose rebs record of all rows is maximum . the player record of this row is alvan adams .
Output: eq { hop { argmax { all_rows ; rebs } ; player } ; alvan adams }
select the rows whose time record is less than 0:10 . there is only one such row in the table . the winner record of this unqiue row is scott smith . the loser record of this unqiue row is kyle noke .
Output: and { only { filter_less { all_rows ; time ; 0:10 } } ; and { eq { hop { filter_less { all_rows ; time ; 0:10 } ; winner } ; scott smith } ; eq { hop { filter_less { all_rows ; time ; 0:10 } ; loser } ; kyle noke } } }
select the rows whose age record is equal to 8 . among these rows , select the rows whose sp record fuzzily matches to 20 / 1 . the number of such rows is 1 .
Output: eq { count { filter_eq { filter_eq { all_rows ; age ; 8 } ; sp ; 20 / 1 } } ; 1 }
select the rows whose res record fuzzily matches to draw . there is only one such row in the table . the opponent record of this unqiue row is ruben villareal .
Output: and { only { filter_eq { all_rows ; res ; draw } } ; eq { hop { filter_eq { all_rows ; res ; draw } ; opponent } ; ruben villareal } }
select the rows whose player record fuzzily matches to bob jones . take the team record of this row . select the rows whose player record fuzzily matches to larry hutton . take the team record of this row . the first record does not match to the second record . the team record of the first row is minnesota twins . the team record of the second row is los angeles dodgers .
Output: and { not_eq { hop { filter_eq { all_rows ; player ; bob jones } ; team } ; hop { filter_eq { all_rows ; player ; larry hutton } ; team } } ; and { eq { hop { filter_eq { all_rows ; player ; bob jones } ; team } ; minnesota twins } ; eq { hop { filter_eq { all_rows ; player ; larry hutton } ; team } ; los angeles dodgers } } }
select the rows whose game site record fuzzily matches to forbes field . the minimum date record of these rows is sunday september 20 .
Output: eq { min { filter_eq { all_rows ; game site ; forbes field } ; date } ; sunday september 20 }
select the rows whose channel record is less than 10 . there is only one such row in the table . the callsign record of this unqiue row is wnbc .
Output: and { only { filter_less { all_rows ; channel ; 10 } } ; eq { hop { filter_less { all_rows ; channel ; 10 } ; callsign } ; wnbc } }
the average of the result record of all rows is 25.5 .
Output: round_eq { avg { all_rows ; result } ; 25.5 }
select the row whose runs record of all rows is 2nd maximum . the batsmen record of this row is mark pettini jason gallian .
Output: eq { hop { nth_argmax { all_rows ; runs ; 2 } ; batsmen } ; mark pettini jason gallian }
select the rows whose season record fuzzily matches to 2001 . take the number of clubs record of this row . select the rows whose season record fuzzily matches to 1996 . take the number of clubs record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; season ; 2001 } ; number of clubs } ; hop { filter_eq { all_rows ; season ; 1996 } ; number of clubs } }
the 2nd maximum points record of all rows is 79 . the artist record of the row with 2nd maximum points record is staffan broms . the song record of the row with 2nd maximum points record is dags igen att vara kära .
Output: and { eq { nth_max { all_rows ; points ; 2 } ; 79 } ; and { eq { hop { nth_argmax { all_rows ; points ; 2 } ; artist } ; staffan broms } ; eq { hop { nth_argmax { all_rows ; points ; 2 } ; song } ; dags igen att vara kära } } }
select the row whose attendance record of all rows is 1st maximum . the opponent record of this row is buffalo bills .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; opponent } ; buffalo bills }
select the rows whose competition record fuzzily matches to 1979 president 's cup . the sum of the score record of these rows is 4 .
Output: round_eq { sum { filter_eq { all_rows ; competition ; 1979 president 's cup } ; score } ; 4 }
for the snatch records of all rows , most of them are greater than or equal to 140 .
Output: most_greater_eq { all_rows ; snatch ; 140 }
the sum of the money record of all rows is 146800 .
Output: round_eq { sum { all_rows ; money } ; 146800 }
select the row whose points per game record of all rows is minimum . the tournament record of this row is 2011 eurobasket .
Output: eq { hop { argmin { all_rows ; points per game } ; tournament } ; 2011 eurobasket }
select the rows whose result record fuzzily matches to won . the number of such rows is 6 .
Output: eq { count { filter_eq { all_rows ; result ; won } } ; 6 }
select the rows whose date record fuzzily matches to january 2013 . the sum of the result record of these rows is 2 .
Output: round_eq { sum { filter_eq { all_rows ; date ; january 2013 } ; result } ; 2 }
the average of the attendance record of all rows is 52800 .
Output: round_eq { avg { all_rows ; attendance } ; 52800 }
select the rows whose written by record fuzzily matches to glenn leopold . select the row whose no in series record of these rows is 1st minimum . the original air date record of this row is september 23 , 1995 .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; written by ; glenn leopold } ; no in series ; 1 } ; original air date } ; september 23 , 1995 }
for the episodes records of all rows , all of them are equal to 22 .
Output: all_eq { all_rows ; episodes ; 22 }
select the rows whose directed by record fuzzily matches to steve scaini . there is only one such row in the table . the record of this unqiue row is 11 .
Output: and { only { filter_eq { all_rows ; directed by ; steve scaini } } ; eq { hop { filter_eq { all_rows ; directed by ; steve scaini } ; } ; 11 } }
select the rows whose result record fuzzily matches to t . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; result ; t } } ; 2 }
select the rows whose state record fuzzily matches to jammu & kashmir . among these rows , select the rows whose total capacity ( mw ) record is equal to 44 . there is only one such row in the table . the power plant record of this unqiue row is chutak .
Output: and { only { filter_eq { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) ; 44 } } ; eq { hop { filter_eq { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) ; 44 } ; power plant } ; chutak } }
select the rows whose gold record is greater than or equal to 10 . among these rows , select the rows whose silver record is greater than or equal to 20 . the number of such rows is 3 .
Output: eq { count { filter_greater_eq { filter_greater_eq { all_rows ; gold ; 10 } ; silver ; 20 } } ; 3 }
select the rows whose lost record is equal to 14 . there is only one such row in the table . the club record of this unqiue row is burry port rfc .
Output: and { only { filter_eq { all_rows ; lost ; 14 } } ; eq { hop { filter_eq { all_rows ; lost ; 14 } ; club } ; burry port rfc } }
for the high points records of all rows , most of them fuzzily match to paul westphal .
Output: most_eq { all_rows ; high points ; paul westphal }
select the rows whose opponent record fuzzily matches to new york islanders . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; opponent ; new york islanders } } ; 3 }
select the rows whose member countries record fuzzily matches to sweden . take the population record of this row . select the rows whose member countries record fuzzily matches to austria . take the population record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; member countries ; sweden } ; population } ; hop { filter_eq { all_rows ; member countries ; austria } ; population } }
select the rows whose position record fuzzily matches to cornerback . the average of the overall record of these rows is 41 .
Output: round_eq { avg { filter_eq { all_rows ; position ; cornerback } ; overall } ; 41 }
select the row whose attendance record of all rows is maximum . the date record of this row is 16 march 2008 .
Output: eq { hop { argmax { all_rows ; attendance } ; date } ; 16 march 2008 }
the average of the gold record of all rows is 2.67 .
Output: round_eq { avg { all_rows ; gold } ; 2.67 }
select the rows whose player record fuzzily matches to david giménez . take the total g record of this row . select the rows whose player record fuzzily matches to casado . take the total g record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; player ; david giménez } ; total g } ; hop { filter_eq { all_rows ; player ; casado } ; total g } }
select the row whose year built record of all rows is 3rd maximum . the church name record of this row is batalden bedehuskapell .
Output: eq { hop { nth_argmax { all_rows ; year built ; 3 } ; church name } ; batalden bedehuskapell }
select the rows whose opponent record fuzzily matches to kansas city chiefs . there is only one such row in the table . the week record of this unqiue row is 4 .
Output: and { only { filter_eq { all_rows ; opponent ; kansas city chiefs } } ; eq { hop { filter_eq { all_rows ; opponent ; kansas city chiefs } ; week } ; 4 } }
select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose money record is greater than 52000 . the number of such rows is 3 .
Output: eq { count { filter_greater { filter_eq { all_rows ; country ; united states } ; money ; 52000 } } ; 3 }
select the rows whose result record is greater than 40 . there is only one such row in the table .
Output: only { filter_greater { all_rows ; result ; 40 } }
select the rows whose call sign record fuzzily matches to wjds . take the frequency mhz record of this row . select the rows whose call sign record fuzzily matches to w298as . take the frequency mhz record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; call sign ; wjds } ; frequency mhz } ; hop { filter_eq { all_rows ; call sign ; w298as } ; frequency mhz } }
select the rows whose owner record fuzzily matches to wnetorg . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; owner ; wnetorg } } ; 2 }
select the row whose rating record of all rows is maximum . the episode record of this row is a modest proposal .
Output: eq { hop { argmax { all_rows ; rating } ; episode } ; a modest proposal }
select the rows whose dance styles record fuzzily matches to rumba . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; dance styles ; rumba } } ; 5 }
select the row whose points record of all rows is maximum . the opponent record of this row is ottawa senators .
Output: eq { hop { argmax { all_rows ; points } ; opponent } ; ottawa senators }
the 16th minimum date record of all rows is december 20 , 1981 .
Output: eq { nth_min { all_rows ; date ; 16 } ; december 20 , 1981 }
select the rows whose year record fuzzily matches to 2001 . take the races record of this row . select the rows whose year record fuzzily matches to 2003 . take the races record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; year ; 2001 } ; races } ; hop { filter_eq { all_rows ; year ; 2003 } ; races } }
for the pole position records of all rows , most of them fuzzily match to michael andretti .
Output: most_eq { all_rows ; pole position ; michael andretti }
select the row whose location attendance record of all rows is 2nd maximum . the date record of this row is january 12 .
Output: eq { hop { nth_argmax { all_rows ; location attendance ; 2 } ; date } ; january 12 }
the average of the pts record of all rows is 0 .
Output: round_eq { avg { all_rows ; pts } ; 0 }
select the rows whose wins record is greater than 0 . there is only one such row in the table . the tournament record of this unqiue row is masters tournament .
Output: and { only { filter_greater { all_rows ; wins ; 0 } } ; eq { hop { filter_greater { all_rows ; wins ; 0 } ; tournament } ; masters tournament } }
select the row whose maximum seating capacity record of all rows is 2nd minimum . the arena / venue record of this row is quadricentennial pavilion .
Output: eq { hop { nth_argmin { all_rows ; maximum seating capacity ; 2 } ; arena / venue } ; quadricentennial pavilion }
select the rows whose school record is arbitrary . the number of such rows is 9 .
Output: eq { count { filter_all { all_rows ; school } } ; 9 }
select the rows whose home team record fuzzily matches to manchester city . take the score record of this row . select the rows whose home team record fuzzily matches to arsenal . take the score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; home team ; manchester city } ; score } ; hop { filter_eq { all_rows ; home team ; arsenal } ; score } }
select the row whose away team score record of all rows is maximum . the away team record of this row is hawthorn .
Output: eq { hop { argmax { all_rows ; away team score } ; away team } ; hawthorn }
select the rows whose team ( equipo ) record fuzzily matches to sporting san miguelito . take the won ( pg ) record of this row . select the rows whose team ( equipo ) record fuzzily matches to alianza . take the won ( pg ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; team ( equipo ) ; sporting san miguelito } ; won ( pg ) } ; hop { filter_eq { all_rows ; team ( equipo ) ; alianza } ; won ( pg ) } }
select the rows whose school record fuzzily matches to munster . take the enrollment record of this row . select the rows whose school record fuzzily matches to andrean . take the enrollment record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; school ; munster } ; enrollment } ; hop { filter_eq { all_rows ; school ; andrean } ; enrollment } }
select the row whose attendance record of all rows is 1st maximum . the date record of this row is december 21 , 2007 .
Output: eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; date } ; december 21 , 2007 }
select the rows whose year record fuzzily matches to 1969 . take the start record of this row . select the rows whose year record fuzzily matches to 1970 . take the start record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; year ; 1969 } ; start } ; hop { filter_eq { all_rows ; year ; 1970 } ; start } }
select the rows whose company record fuzzily matches to bp . take the profits ( billion ) record of this row . select the rows whose company record fuzzily matches to hsbc . take the profits ( billion ) record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; company ; bp } ; profits ( billion ) } ; hop { filter_eq { all_rows ; company ; hsbc } ; profits ( billion ) } }
for the pictorials records of all rows , most of them fuzzily match to girls .
Output: most_eq { all_rows ; pictorials ; girls }
for the gained university status records of all rows , most of them are greater than 1920 .
Output: most_greater { all_rows ; gained university status ; 1920 }
select the rows whose country record fuzzily matches to new zealand . there is only one such row in the table . the player record of this unqiue row is bob charles .
Output: and { only { filter_eq { all_rows ; country ; new zealand } } ; eq { hop { filter_eq { all_rows ; country ; new zealand } ; player } ; bob charles } }
select the rows whose fri 5 june record fuzzily matches to no time . the number of such rows is 8 .
Output: eq { count { filter_eq { all_rows ; fri 5 june ; no time } } ; 8 }
select the rows whose role record fuzzily matches to 2nd male lead . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; role ; 2nd male lead } } ; 2 }
for the high assists records of all rows , most of them fuzzily match to lebron james .
Output: most_eq { all_rows ; high assists ; lebron james }
for the engine records of all rows , most of them fuzzily match to offenhauser l4 .
Output: most_eq { all_rows ; engine ; offenhauser l4 }
select the row whose week record of all rows is 1st minimum . the opponent record of this row is chicago cardinals .
Output: eq { hop { nth_argmin { all_rows ; week ; 1 } ; opponent } ; chicago cardinals }
select the rows whose tournament record fuzzily matches to mci heritage golf classic . there is only one such row in the table . the date record of this unqiue row is 17 apr 1988 .
Output: and { only { filter_eq { all_rows ; tournament ; mci heritage golf classic } } ; eq { hop { filter_eq { all_rows ; tournament ; mci heritage golf classic } ; date } ; 17 apr 1988 } }
select the row whose total assets ( score ) record of all rows is maximum . the bank record of this row is bank of industry and mine .
Output: eq { hop { argmax { all_rows ; total assets ( score ) } ; bank } ; bank of industry and mine }
select the rows whose first runner up record fuzzily matches to paragon denver co . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; first runner up ; paragon denver co } } ; 3 }
select the rows whose date of successors taking office record fuzzily matches to 1895 . among these rows , select the rows whose reason for vacancy record fuzzily matches to resigned . there is only one such row in the table . the vacator record of this unqiue row is edwin j jordan ( r ) .
Output: and { only { filter_eq { filter_eq { all_rows ; date of successors taking office ; 1895 } ; reason for vacancy ; resigned } } ; eq { hop { filter_eq { filter_eq { all_rows ; date of successors taking office ; 1895 } ; reason for vacancy ; resigned } ; vacator } ; edwin j jordan ( r ) } }
select the rows whose directed by record fuzzily matches to alison maclean . there is only one such row in the table . the title record of this unqiue row is the river .
Output: and { only { filter_eq { all_rows ; directed by ; alison maclean } } ; eq { hop { filter_eq { all_rows ; directed by ; alison maclean } ; title } ; the river } }
select the row whose attendance record of all rows is maximum . the week record of this row is 11 .
Output: eq { hop { argmax { all_rows ; attendance } ; week } ; 11 }
select the row whose goals record of all rows is maximum . the club record of this row is real madrid .
Output: eq { hop { argmax { all_rows ; goals } ; club } ; real madrid }
the maximum crowd record of all rows is 27000 . the venue record of the row with superlative crowd record is punt road oval .
Output: and { eq { max { all_rows ; crowd } ; 27000 } ; eq { hop { argmax { all_rows ; crowd } ; venue } ; punt road oval } }
select the rows whose height ft ( m ) record is equal to 504 . there is only one such row in the table . the name record of this unqiue row is one indiana square .
Output: and { only { filter_eq { all_rows ; height ft ( m ) ; 504 } } ; eq { hop { filter_eq { all_rows ; height ft ( m ) ; 504 } ; name } ; one indiana square } }
select the row whose points record of all rows is 3rd maximum . the opponent record of this row is hartford whalers .
Output: eq { hop { nth_argmax { all_rows ; points ; 3 } ; opponent } ; hartford whalers }
select the rows whose guest host record fuzzily matches to jessie wallace . take the air date record of this row . select the rows whose guest host record fuzzily matches to trisha goddard . take the air date record of this row . the first record is less than the second record . the air date record of the first row is 17 february 2006 . the air date record of the second row is 24 february 2006 .
Output: and { less { hop { filter_eq { all_rows ; guest host ; jessie wallace } ; air date } ; hop { filter_eq { all_rows ; guest host ; trisha goddard } ; air date } } ; and { eq { hop { filter_eq { all_rows ; guest host ; jessie wallace } ; air date } ; 17 february 2006 } ; eq { hop { filter_eq { all_rows ; guest host ; trisha goddard } ; air date } ; 24 february 2006 } } }
select the rows whose general classification record fuzzily matches to nick nuyens . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; general classification ; nick nuyens } } ; 5 }
select the rows whose lost record is greater than 1 . among these rows , select the rows whose points record is equal to 14 . there is only one such row in the table . the team record of this unqiue row is skra warszawa .
Output: and { only { filter_eq { filter_greater { all_rows ; lost ; 1 } ; points ; 14 } } ; eq { hop { filter_eq { filter_greater { all_rows ; lost ; 1 } ; points ; 14 } ; team } ; skra warszawa } }
select the row whose bronze record of all rows is maximum . the nation record of this row is spain .
Output: eq { hop { argmax { all_rows ; bronze } ; nation } ; spain }
select the row whose first operated record of all rows is maximum . the name record of this row is jubilee line .
Output: eq { hop { argmax { all_rows ; first operated } ; name } ; jubilee line }
select the rows whose school / club team record fuzzily matches to tennessee . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; school / club team ; tennessee } } ; 2 }
the 2nd minimum year record of all rows is 1979 . the issued title record of the row with 2nd minimum year record is 1958 miles .
Output: and { eq { nth_min { all_rows ; year ; 2 } ; 1979 } ; eq { hop { nth_argmin { all_rows ; year ; 2 } ; issued title } ; 1958 miles } }
select the rows whose championship record fuzzily matches to wimbledon . select the row whose year record of these rows is minimum . the opponent in the final record of this row is wilhelm bungert .
Output: eq { hop { argmin { filter_eq { all_rows ; championship ; wimbledon } ; year } ; opponent in the final } ; wilhelm bungert }
select the rows whose country record fuzzily matches to burma . there is only one such row in the table . the city record of this unqiue row is yangon .
Output: and { only { filter_eq { all_rows ; country ; burma } } ; eq { hop { filter_eq { all_rows ; country ; burma } ; city } ; yangon } }
select the row whose assistant professors record of all rows is minimum . the lecturers record of this row is 7 .
Output: eq { hop { argmin { all_rows ; assistant professors } ; lecturers } ; 7 }
select the row whose attendance record of all rows is maximum . the week record of this row is 16 .
Output: eq { hop { argmax { all_rows ; attendance } ; week } ; 16 }
select the rows whose winner record fuzzily matches to elio fox . take the prize money record of this row . select the rows whose winner record fuzzily matches to barry shulman . take the prize money record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; winner ; elio fox } ; prize money } ; hop { filter_eq { all_rows ; winner ; barry shulman } ; prize money } }
the average of the crowd record of all rows is 23516 .
Output: round_eq { avg { all_rows ; crowd } ; 23516 }
select the rows whose opponent record fuzzily matches to baltimore colts . the minimum attendance record of these rows is 57808 .
Output: eq { min { filter_eq { all_rows ; opponent ; baltimore colts } ; attendance } ; 57808 }
select the row whose home team score record of all rows is maximum . the home team record of this row is richmond .
Output: eq { hop { argmax { all_rows ; home team score } ; home team } ; richmond }
select the rows whose losing bonus record is equal to 3 . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; losing bonus ; 3 } } ; 5 }
the maximum wins record of all rows is 6 . the year record of the row with superlative wins record is 2011 .
Output: and { eq { max { all_rows ; wins } ; 6 } ; eq { hop { argmax { all_rows ; wins } ; year } ; 2011 } }
the average of the to par record of all rows is 13.4 .
Output: round_eq { avg { all_rows ; to par } ; 13.4 }
select the rows whose country record fuzzily matches to scotland . there is only one such row in the table . the player record of this unqiue row is sandy lyle .
Output: and { only { filter_eq { all_rows ; country ; scotland } } ; eq { hop { filter_eq { all_rows ; country ; scotland } ; player } ; sandy lyle } }
select the row whose grand total record of all rows is maximum . the nato member record of this row is uk .
Output: eq { hop { argmax { all_rows ; grand total } ; nato member } ; uk }
select the rows whose viewers ( households in millions ) record is less than 18 . the number of such rows is 3 .
Output: eq { count { filter_less { all_rows ; viewers ( households in millions ) ; 18 } } ; 3 }
for the current streak records of all rows , most of them fuzzily match to w .
Output: most_eq { all_rows ; current streak ; w }
for the wheels records of all rows , most of them fuzzily match to 4 - 4 - 2t .
Output: most_eq { all_rows ; wheels ; 4 - 4 - 2t }
select the rows whose type record fuzzily matches to first . among these rows , select the rows whose intake record is equal to 30 . the number of such rows is 4 .
Output: eq { count { filter_eq { filter_eq { all_rows ; type ; first } ; intake ; 30 } } ; 4 }
for the owgr points records of all rows , most of them are equal to 6 .
Output: most_eq { all_rows ; owgr points ; 6 }
select the rows whose location attendance record fuzzily matches to quicken loans arena . there is only one such row in the table . the date record of this unqiue row is november 3 .
Output: and { only { filter_eq { all_rows ; location attendance ; quicken loans arena } } ; eq { hop { filter_eq { all_rows ; location attendance ; quicken loans arena } ; date } ; november 3 } }
select the rows whose opponent record fuzzily matches to tampa bay buccaneers . take the date record of this row . select the rows whose opponent record fuzzily matches to green bay packers . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; opponent ; tampa bay buccaneers } ; date } ; hop { filter_eq { all_rows ; opponent ; green bay packers } ; date } }
for the joined records of all rows , most of them are equal to 2010 .
Output: most_eq { all_rows ; joined ; 2010 }
select the row whose unemployment rate record of all rows is maximum . the county record of this row is schoharie .
Output: eq { hop { argmax { all_rows ; unemployment rate } ; county } ; schoharie }
select the row whose crowd record of all rows is 2nd maximum . the home team record of this row is footscray . the date record of this row is 20 june 1942 .
Output: and { eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; home team } ; footscray } ; eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; date } ; 20 june 1942 } }
select the rows whose score record fuzzily matches to l . there is only one such row in the table . the team record of this unqiue row is georgetown ( 19 ) .
Output: and { only { filter_eq { all_rows ; score ; l } } ; eq { hop { filter_eq { all_rows ; score ; l } ; team } ; georgetown ( 19 ) } }
the average of the to par record of all rows is 0.92 .
Output: round_eq { avg { all_rows ; to par } ; 0.92 }
select the row whose date record of all rows is minimum . the location attendance record of this row is american airlines center 19688 .
Output: eq { hop { argmin { all_rows ; date } ; location attendance } ; american airlines center 19688 }
select the rows whose of seats won record is less than 10 . there is only one such row in the table . the election record of this unqiue row is 1993 .
Output: and { only { filter_less { all_rows ; of seats won ; 10 } } ; eq { hop { filter_less { all_rows ; of seats won ; 10 } ; election } ; 1993 } }
select the rows whose royal house record fuzzily matches to twelfth . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; royal house ; twelfth } } ; 3 }
select the rows whose opponent record fuzzily matches to thomas muster . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; opponent ; thomas muster } } ; 2 }
select the rows whose wins record is equal to 0 . there is only one such row in the table . the tournament record of this unqiue row is masters tournament .
Output: and { only { filter_eq { all_rows ; wins ; 0 } } ; eq { hop { filter_eq { all_rows ; wins ; 0 } ; tournament } ; masters tournament } }
select the row whose crowd record of all rows is 2nd maximum . the home team record of this row is essendon .
Output: eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; home team } ; essendon }
the sum of the viewers record of all rows is 111.2 .
Output: round_eq { sum { all_rows ; viewers } ; 111.2 }
select the rows whose branch record fuzzily matches to us marine corps . there is only one such row in the table . the name record of this unqiue row is general james l jones .
Output: and { only { filter_eq { all_rows ; branch ; us marine corps } } ; eq { hop { filter_eq { all_rows ; branch ; us marine corps } ; name } ; general james l jones } }
select the rows whose date record fuzzily matches to 2008 . the maximum date record of these rows is 4 september 2008 .
Output: eq { max { filter_eq { all_rows ; date ; 2008 } ; date } ; 4 september 2008 }
select the rows whose party record fuzzily matches to democratic . for the first elected records of these rows , most of them are less than 1970 .
Output: most_less { filter_eq { all_rows ; party ; democratic } ; first elected ; 1970 }
the maximum length record of all rows is 136 ' . the name record of the row with superlative length record is fay no 4 .
Output: and { eq { max { all_rows ; length } ; 136 ' } ; eq { hop { argmax { all_rows ; length } ; name } ; fay no 4 } }
select the rows whose writer record fuzzily matches to ed roe . there is only one such row in the table . the production code record of this unqiue row is 402 .
Output: and { only { filter_eq { all_rows ; writer ; ed roe } } ; eq { hop { filter_eq { all_rows ; writer ; ed roe } ; production code } ; 402 } }
select the row whose points record of all rows is maximum . the song record of this row is living my life without you .
Output: eq { hop { argmax { all_rows ; points } ; song } ; living my life without you }
select the rows whose game site record fuzzily matches to rca dome . select the row whose date record of these rows is 4th minimum . the attendance record of this row is 56860 .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; game site ; rca dome } ; date ; 4 } ; attendance } ; 56860 }
select the row whose league record of all rows is 2nd maximum . the player record of this row is tom de sutter .
Output: eq { hop { nth_argmax { all_rows ; league ; 2 } ; player } ; tom de sutter }
select the rows whose tv network ( s ) record fuzzily matches to fox8 . there is only one such row in the table . the country record of this unqiue row is australia .
Output: and { only { filter_eq { all_rows ; tv network ( s ) ; fox8 } } ; eq { hop { filter_eq { all_rows ; tv network ( s ) ; fox8 } ; country } ; australia } }
select the rows whose opponent in the final record fuzzily matches to graeme dott . there is only one such row in the table . the championship record of this unqiue row is world series - berlin event .
Output: and { only { filter_eq { all_rows ; opponent in the final ; graeme dott } } ; eq { hop { filter_eq { all_rows ; opponent in the final ; graeme dott } ; championship } ; world series - berlin event } }
for the launch year records of all rows , most of them are equal to 2004 .
Output: most_eq { all_rows ; launch year ; 2004 }
select the rows whose winning constructor record fuzzily matches to bugatti . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; winning constructor ; bugatti } } ; 4 }
select the rows whose indian scorers record fuzzily matches to own goal . there is only one such row in the table . the date record of this unqiue row is 23 june 2011 .
Output: and { only { filter_eq { all_rows ; indian scorers ; own goal } } ; eq { hop { filter_eq { all_rows ; indian scorers ; own goal } ; date } ; 23 june 2011 } }
for the friendly records of all rows , most of them fuzzily match to world cup 2006 qualifier .
Output: most_eq { all_rows ; friendly ; world cup 2006 qualifier }
select the rows whose away team record fuzzily matches to richmond . take the away team score record of this row . select the rows whose away team record fuzzily matches to swans . take the away team score record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; away team ; richmond } ; away team score } ; hop { filter_eq { all_rows ; away team ; swans } ; away team score } }
select the rows whose event record fuzzily matches to ufc . among these rows , select the rows whose res record fuzzily matches to win . the number of such rows is 8 .
Output: eq { count { filter_eq { filter_eq { all_rows ; event ; ufc } ; res ; win } } ; 8 }
select the rows whose group position record fuzzily matches to 1st . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; group position ; 1st } } ; 3 }
select the rows whose party record fuzzily matches to democratic . select the row whose first elected record of these rows is minimum . the incumbent record of this row is charles edward bennett .
Output: eq { hop { argmin { filter_eq { all_rows ; party ; democratic } ; first elected } ; incumbent } ; charles edward bennett }
select the rows whose june 10 - 11 record fuzzily matches to june 10 . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; june 10 - 11 ; june 10 } } ; 2 }
the average of the score record of all rows is 19.05 .
Output: round_eq { avg { all_rows ; score } ; 19.05 }
select the rows whose english title record fuzzily matches to the saviour of the soul . take the hk viewers record of this row . select the rows whose english title record fuzzily matches to men in pain . take the hk viewers record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; english title ; the saviour of the soul } ; hk viewers } ; hop { filter_eq { all_rows ; english title ; men in pain } ; hk viewers } }
select the row whose rank record of all rows is minimum . the nation record of this row is soviet union .
Output: eq { hop { argmin { all_rows ; rank } ; nation } ; soviet union }
select the rows whose winner record fuzzily matches to chuck arnason . take the goals record of this row . select the rows whose winner record fuzzily matches to reggie leach . take the goals record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; winner ; chuck arnason } ; goals } ; hop { filter_eq { all_rows ; winner ; reggie leach } ; goals } }
the average of the attendance record of all rows is 36092.5 .
Output: round_eq { avg { all_rows ; attendance } ; 36092.5 }
select the rows whose no built record is greater than 20 . the number of such rows is 6 .
Output: eq { count { filter_greater { all_rows ; no built ; 20 } } ; 6 }
select the rows whose district record fuzzily matches to new york . there is only one such row in the table . the vacator record of this unqiue row is jonathan havens ( dr ) .
Output: and { only { filter_eq { all_rows ; district ; new york } } ; eq { hop { filter_eq { all_rows ; district ; new york } ; vacator } ; jonathan havens ( dr ) } }
select the row whose population density ( per km square ) record of all rows is maximum . the country with flag record of this row is puerto rico ( usa ) .
Output: eq { hop { argmax { all_rows ; population density ( per km square ) } ; country with flag } ; puerto rico ( usa ) }
select the rows whose date record fuzzily matches to 2003 . the average of the score record of these rows is 3 .
Output: round_eq { avg { filter_eq { all_rows ; date ; 2003 } ; score } ; 3 }
select the rows whose club record fuzzily matches to montauban . take the lost record of this row . select the rows whose club record fuzzily matches to biarritz . take the lost record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; club ; montauban } ; lost } ; hop { filter_eq { all_rows ; club ; biarritz } ; lost } }
for the report records of all rows , all of them fuzzily match to bbc .
Output: all_eq { all_rows ; report ; bbc }
select the row whose result record of all rows is maximum . the venue record of this row is adelaide oval .
Output: eq { hop { argmax { all_rows ; result } ; venue } ; adelaide oval }
select the row whose year record of all rows is maximum . the chassis record of this row is ferrari 500 / 625 .
Output: eq { hop { argmax { all_rows ; year } ; chassis } ; ferrari 500 / 625 }
select the rows whose location / attendance record fuzzily matches to the omni . among these rows , select the rows whose score record fuzzily matches to l . the number of such rows is 6 .
Output: eq { count { filter_eq { filter_eq { all_rows ; location / attendance ; the omni } ; score ; l } } ; 6 }
select the rows whose competition record fuzzily matches to world championships . among these rows , select the rows whose position record fuzzily matches to 31st . there is only one such row in the table . the year record of this unqiue row is 2007 .
Output: and { only { filter_eq { filter_eq { all_rows ; competition ; world championships } ; position ; 31st } } ; eq { hop { filter_eq { filter_eq { all_rows ; competition ; world championships } ; position ; 31st } ; year } ; 2007 } }
select the rows whose outgoing manager record fuzzily matches to raşit çetiner . take the date of vacancy record of this row . select the rows whose outgoing manager record fuzzily matches to engin ipekoğlu . take the date of vacancy record of this row . the second record is 8 days larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; outgoing manager ; raşit çetiner } ; date of vacancy } ; hop { filter_eq { all_rows ; outgoing manager ; engin ipekoğlu } ; date of vacancy } } ; -8 days }
select the rows whose date record fuzzily matches to october . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; date ; october } } ; 2 }
select the rows whose result record fuzzily matches to w . select the row whose week record of these rows is 1st minimum . the opponent record of this row is new york jets . the game site record of this row is qualcomm stadium .
Output: and { eq { hop { nth_argmin { filter_eq { all_rows ; result ; w } ; week ; 1 } ; opponent } ; new york jets } ; eq { hop { nth_argmin { filter_eq { all_rows ; result ; w } ; week ; 1 } ; game site } ; qualcomm stadium } }
the average of the purse record of all rows is 163250 .
Output: round_eq { avg { all_rows ; purse } ; 163250 }
select the row whose length record of all rows is 2nd minimum . the line record of this row is sofia - dragoman .
Output: eq { hop { nth_argmin { all_rows ; length ; 2 } ; line } ; sofia - dragoman }
select the rows whose margin of victory record fuzzily matches to 3 strokes . there is only one such row in the table . the tournament record of this unqiue row is anheuser - busch golf classic .
Output: and { only { filter_eq { all_rows ; margin of victory ; 3 strokes } } ; eq { hop { filter_eq { all_rows ; margin of victory ; 3 strokes } ; tournament } ; anheuser - busch golf classic } }
select the rows whose name record fuzzily matches to justin knox . take the height record of this row . select the rows whose name record fuzzily matches to jamychal green . take the height record of this row . the first record fuzzily matches to the second record .
Output: eq { hop { filter_eq { all_rows ; name ; justin knox } ; height } ; hop { filter_eq { all_rows ; name ; jamychal green } ; height } }
select the rows whose citrate record fuzzily matches to negative . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; citrate ; negative } } ; 3 }
the average of the points against record of all rows is 292 .
Output: round_eq { avg { all_rows ; points against } ; 292 }
select the rows whose vocalist record fuzzily matches to kanako . there is only one such row in the table .
Output: only { filter_eq { all_rows ; vocalist ; kanako } }
select the rows whose type record fuzzily matches to public . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; type ; public } } ; 3 }
select the rows whose current streak record fuzzily matches to l . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; current streak ; l } } ; 2 }
select the rows whose name record fuzzily matches to aran . take the tournaments record of this row . select the rows whose name record fuzzily matches to itai . take the tournaments record of this row . the second record is 1 larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; name ; aran } ; tournaments } ; hop { filter_eq { all_rows ; name ; itai } ; tournaments } } ; -1 }
the average of the frequency mhz record of all rows is 99.16 .
Output: round_eq { avg { all_rows ; frequency mhz } ; 99.16 }
select the rows whose pos record does not match to dnf . the sum of the laps record of these rows is 1359 .
Output: round_eq { sum { filter_not_eq { all_rows ; pos ; dnf } ; laps } ; 1359 }
the sum of the crowd record of all rows is 103000 .
Output: round_eq { sum { all_rows ; crowd } ; 103000 }
for the date records of all rows , all of them fuzzily match to 2 august 1980 .
Output: all_eq { all_rows ; date ; 2 august 1980 }
select the rows whose crowd record is less than 10,000 . there is only one such row in the table . the venue record of this unqiue row is corio oval .
Output: and { only { filter_less { all_rows ; crowd ; 10,000 } } ; eq { hop { filter_less { all_rows ; crowd ; 10,000 } ; venue } ; corio oval } }
select the rows whose play - by - play record fuzzily matches to al michaels . for the play - by - play records of these rows , all of them fuzzily match to al michaels .
Output: all_eq { filter_eq { all_rows ; play - by - play ; al michaels } ; play - by - play ; al michaels }
for the starts records of all rows , most of them are greater than 33 .
Output: most_greater { all_rows ; starts ; 33 }
the 3rd minimum rank record of all rows is 3 . the rowers record of the row with 3rd minimum rank record is morten nielsen , thomas larsen . the time record of the row with 3rd minimum rank record is 6:38.33 .
Output: and { eq { nth_min { all_rows ; rank ; 3 } ; 3 } ; and { eq { hop { nth_argmin { all_rows ; rank ; 3 } ; rowers } ; morten nielsen , thomas larsen } ; eq { hop { nth_argmin { all_rows ; rank ; 3 } ; time } ; 6:38.33 } } }
select the row whose crowd record of all rows is 2nd maximum . the home team record of this row is north melbourne .
Output: eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; home team } ; north melbourne }
select the rows whose score record fuzzily matches to 81 - 104 . take the date record of this row . select the rows whose score record fuzzily matches to 77 - 96 . take the date record of this row . the second record is 3 days larger than the first record .
Output: eq { diff { hop { filter_eq { all_rows ; score ; 81 - 104 } ; date } ; hop { filter_eq { all_rows ; score ; 77 - 96 } ; date } } ; -3 days }