niryuu/sni-each-converted huggingface dataset:
eq { count { filter_eq { all_rows ; result ; re - elected } } ; 4 } = true
Output: select the rows whose result record fuzzily matches to re  elected . the number of such rows is 4 .
round_eq { sum { filter_eq { all_rows ; date ; october } ; attendance } ; 142040 } = true
Output: select the rows whose date record fuzzily matches to october . the sum of the attendance record of these rows is 142040 .
eq { hop { nth_argmax { all_rows ; votes ; 2 } ; election } ; 2002 } = true
Output: select the row whose votes record of all rows is 2nd maximum . the election record of this row is 2002 .
eq { hop { nth_argmax { all_rows ; power notation ; 2 } ; short scale } ; one billion a thousand million } = true
Output: select the row whose power notation record of all rows is 2nd maximum . the short scale record of this row is one billion a thousand million .
and { only { filter_eq { all_rows ; reason for change ; contested election } } ; eq { hop { filter_eq { all_rows ; reason for change ; contested election } ; vacator } ; josã m gallegos ( d ) } } = true
Output: select the rows whose reason for change record fuzzily matches to contested election . there is only one such row in the table . the vacator record of this unqiue row is josã m gallegos  d  .
round_eq { avg { all_rows ; races } ; 7.6 } = true
Output: the average of the races record of all rows is 7.6 .
eq { max { all_rows ; laps } ; 325 } = true
Output: the maximum laps record of all rows is 325 .
round_eq { avg { all_rows ; blank ends } ; 7.64 } = true
Output: the average of the blank ends record of all rows is 7.64 .
round_eq { avg { filter_eq { all_rows ; builder ; beyer peacock & co } ; builder 's no } ; 3442 } = true
Output: select the rows whose builder record fuzzily matches to beyer peacock  co . the average of the builder s no record of these rows is 3442 .
eq { hop { nth_argmin { all_rows ; year ; 4 } ; against } ; georgia } = true
Output: select the row whose year record of all rows is 4th minimum . the against record of this row is georgia .
greater { hop { filter_eq { all_rows ; date ; 22 october } ; score } ; hop { filter_eq { all_rows ; date ; 24 october } ; score } } = true
Output: select the rows whose date record fuzzily matches to 22 october . take the score record of this row . select the rows whose date record fuzzily matches to 24 october . take the score record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; points } ; 47.55 } = true
Output: the average of the points record of all rows is 47.55 .
less { hop { filter_eq { all_rows ; venue ; glenferrie oval } ; crowd } ; hop { filter_eq { all_rows ; venue ; mcg } ; crowd } } = true
Output: select the rows whose venue record fuzzily matches to glenferrie oval . take the crowd record of this row . select the rows whose venue record fuzzily matches to mcg . take the crowd record of this row . the first record is less than the second record .
eq { hop { argmax { all_rows ; apparent magnitude } ; ngc number } ; 5003 } = true
Output: select the row whose apparent magnitude record of all rows is maximum . the ngc number record of this row is 5003 .
eq { count { filter_eq { all_rows ; time ; 25.61 } } ; 2 } = true
Output: select the rows whose time record is equal to 25.61 . the number of such rows is 2 .
eq { hop { argmax { all_rows ; wins } ; club } ; warrnambool } = true
Output: select the row whose wins record of all rows is maximum . the club record of this row is warrnambool .
and { only { filter_eq { all_rows ; position ; 6th } } ; eq { hop { filter_eq { all_rows ; position ; 6th } ; year } ; 2005 } } = true
Output: select the rows whose position record fuzzily matches to 6th . there is only one such row in the table . the year record of this unqiue row is 2005 .
eq { count { filter_greater { filter_less { all_rows ; land area ( km square ) ; 10.0 } ; population density ( per km square ) ; 150.0 } } ; 2 } = true
Output: select the rows whose land area  km square  record is less than 10.0 . among these rows , select the rows whose population density  per km square  record is greater than 150.0 . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; release date ; 2008 } } ; 2 } = true
Output: select the rows whose release date record fuzzily matches to 2008 . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; directed by ; jeff bleckner } } ; 3 } = true
Output: select the rows whose directed by record fuzzily matches to jeff bleckner . the number of such rows is 3 .
eq { count { filter_eq { filter_eq { all_rows ; date ; 2004 } ; surface ; hard } } ; 2 } = true
Output: select the rows whose date record fuzzily matches to 2004 . among these rows , select the rows whose surface record fuzzily matches to hard . the number of such rows is 2 .
most_eq { filter_eq { all_rows ; position ; center } ; nationality ; sweden } = true
Output: select the rows whose position record fuzzily matches to center . for the nationality records of these rows , most of them fuzzily match to sweden .
less { hop { filter_eq { all_rows ; winning driver ; lee wallard } ; date } ; hop { filter_eq { all_rows ; winning driver ; alberto ascari } ; date } } = true
Output: select the rows whose winning driver record fuzzily matches to lee wallard . take the date record of this row . select the rows whose winning driver record fuzzily matches to alberto ascari . take the date record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; bleeding time ; unaffected } } ; 6 } = true
Output: select the rows whose bleeding time record fuzzily matches to unaffected . the number of such rows is 6 .
eq { count { filter_eq { all_rows ; 2nd position ; lija } } ; 2 } = true
Output: select the rows whose 2nd position record fuzzily matches to lija . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; cores ; 2 } } ; 10 } = true
Output: select the rows whose cores record is equal to 2 . the number of such rows is 10 .
only { filter_eq { all_rows ; vocalist ; kanako } } = true
Output: select the rows whose vocalist record fuzzily matches to kanako . there is only one such row in the table .
and { only { filter_eq { filter_eq { all_rows ; batting style ; left hand bat } ; first class team ; windward islands } } ; eq { hop { filter_eq { filter_eq { all_rows ; batting style ; left hand bat } ; first class team ; windward islands } ; player } ; nixon mclean } } = true
Output: select the rows whose batting style record fuzzily matches to left hand bat . among these rows , select the rows whose first class team record fuzzily matches to windward islands . there is only one such row in the table . the player record of this unqiue row is nixon mclean .
eq { count { filter_greater { all_rows ; % change ; 10 % } } ; 3 } = true
Output: select the rows whose  change record is greater than 10  . the number of such rows is 3 .
round_eq { avg { all_rows ; score } ; 139 } = true
Output: the average of the score record of all rows is 139 .
eq { max { all_rows ; distance } ; 2500 m } = true
Output: the maximum distance record of all rows is 2500 m .
round_eq { sum { all_rows ; severe tropical cyclones } ; 59 } = true
Output: the sum of the severe tropical cyclones record of all rows is 59 .
most_eq { all_rows ; publisher ; nintendo } = true
Output: for the publisher records of all rows , most of them fuzzily match to nintendo .
most_eq { all_rows ; owner ; canadian broadcasting corporation } = true
Output: for the owner records of all rows , most of them fuzzily match to canadian broadcasting corporation .
most_eq { all_rows ; score ; 1 } = true
Output: for the score records of all rows , most of them are equal to 1 .
eq { count { filter_eq { all_rows ; format ; country } } ; 2 } = true
Output: select the rows whose format record fuzzily matches to country . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; attendance ; 2 } ; visitor } ; kings } = true
Output: select the row whose attendance record of all rows is 2nd maximum . the visitor record of this row is kings .
and { eq { hop { argmax { all_rows ; us viewers ( in millions ) } ; title } ; the new normal } ; eq { hop { argmax { all_rows ; us viewers ( in millions ) } ; directed by } ; michael lange } } = true
Output: select the row whose us viewers  in millions  record of all rows is maximum . the title record of this row is the new normal . the directed by record of this row is michael lange .
and { greater { hop { filter_eq { all_rows ; project ; özhan canaydın project } ; capacity } ; hop { filter_eq { all_rows ; project ; faruk süren project } ; capacity } } ; and { eq { hop { filter_eq { all_rows ; project ; özhan canaydın project } ; capacity } ; 52652 } ; eq { hop { filter_eq { all_rows ; project ; faruk süren project } ; capacity } ; 40482 } } } = true
Output: select the rows whose project record fuzzily matches to özhan canaydın project . take the capacity record of this row . select the rows whose project record fuzzily matches to faruk süren project . take the capacity record of this row . the first record is greater than the second record . the capacity record of the first row is 52652 . the capacity record of the second row is 40482 .
eq { count { filter_eq { all_rows ; maximum seating capacity ; unknown } } ; 4 } = true
Output: select the rows whose maximum seating capacity record fuzzily matches to unknown . the number of such rows is 4 .
greater { hop { filter_eq { all_rows ; club ; malmö ff } ; allsvenskan titles } ; hop { filter_eq { all_rows ; club ; ifk göteborg } ; allsvenskan titles } } = true
Output: select the rows whose club record fuzzily matches to malmö ff . take the allsvenskan titles record of this row . select the rows whose club record fuzzily matches to ifk göteborg . take the allsvenskan titles record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; us viewers ( millions ) } ; no in season } ; 1 } = true
Output: select the row whose us viewers  millions  record of all rows is maximum . the no in season record of this row is 1 .
most_less_eq { all_rows ; co2 ; 200 g/km } = true
Output: for the co2 records of all rows , most of them are less than or equal to 200 gkm .
eq { diff { hop { filter_eq { all_rows ; title ; capybara } ; originalairdate } ; hop { filter_eq { all_rows ; title ; the sleep of babies } ; originalairdate } } ; -7 days } = true
Output: select the rows whose title record fuzzily matches to capybara . take the originalairdate record of this row . select the rows whose title record fuzzily matches to the sleep of babies . take the originalairdate record of this row . the second record is 7 days larger than the first record .
eq { count { filter_eq { filter_less { all_rows ; time ; 2:01 } ; nationality ; australia } } ; 2 } = true
Output: select the rows whose time record is less than 2:01 . among these rows , select the rows whose nationality record fuzzily matches to australia . the number of such rows is 2 .
less { hop { filter_eq { all_rows ; driver ; graham hill } ; time / retired } ; hop { filter_eq { all_rows ; driver ; jim clark } ; time / retired } } = true
Output: select the rows whose driver record fuzzily matches to graham hill . take the time  retired record of this row . select the rows whose driver record fuzzily matches to jim clark . take the time  retired record of this row . the first record is less than the second record .
eq { count { filter_eq { all_rows ; 9:00 ; the office } } ; 2 } = true
Output: select the rows whose 9:00 record fuzzily matches to the office . the number of such rows is 2 .
and { only { filter_eq { all_rows ; class ; a } } ; eq { hop { filter_eq { all_rows ; class ; a } ; city of license } ; kingston } } = true
Output: select the rows whose class record fuzzily matches to a . there is only one such row in the table . the city of license record of this unqiue row is kingston .
greater { hop { filter_eq { all_rows ; state ; oklahoma } ; interview } ; hop { filter_eq { all_rows ; state ; new jersey } ; interview } } = true
Output: select the rows whose state record fuzzily matches to oklahoma . take the interview record of this row . select the rows whose state record fuzzily matches to new jersey . take the interview record of this row . the first record is greater than the second record .
eq { count { filter_eq { all_rows ; stadium ; eden gardens } } ; 2 } = true
Output: select the rows whose stadium record fuzzily matches to eden gardens . the number of such rows is 2 .
round_eq { sum { all_rows ; laps } ; 1,268 } = true
Output: the sum of the laps record of all rows is 1,268 .
round_eq { avg { all_rows ; candidates } ; 51.0 } = true
Output: the average of the candidates record of all rows is 51.0 .
eq { hop { argmax { all_rows ; matches } ; name } ; glenn ferguson } = true
Output: select the row whose matches record of all rows is maximum . the name record of this row is glenn ferguson .
eq { hop { nth_argmax { all_rows ; date ; 3 } ; tournament } ; westende } = true
Output: select the row whose date record of all rows is 3rd maximum . the tournament record of this row is westende .
eq { hop { argmax { all_rows ; overs } ; player } ; j srinath } = true
Output: select the row whose overs record of all rows is maximum . the player record of this row is j srinath .
most_eq { all_rows ; location ; yankee stadium } = true
Output: for the location records of all rows , most of them fuzzily match to yankee stadium .
eq { max { filter_eq { all_rows ; position ; guard } ; height in ft } ; 6 - 8 } = true
Output: select the rows whose position record fuzzily matches to guard . the maximum height in ft record of these rows is 6  8 .
most_less { all_rows ; time ; 4:00 } = true
Output: for the time records of all rows , most of them are less than 4:00 .
eq { hop { nth_argmin { all_rows ; incorporated ; 2 } ; company } ; airline allied services ltd } = true
Output: select the row whose incorporated record of all rows is 2nd minimum . the company record of this row is airline allied services ltd .
less { hop { filter_eq { all_rows ; school ; doane college } ; founded } ; hop { filter_eq { all_rows ; school ; college of saint mary } ; founded } } = true
Output: select the rows whose school record fuzzily matches to doane college . take the founded record of this row . select the rows whose school record fuzzily matches to college of saint mary . take the founded record of this row . the first record is less than the second record .
greater { hop { filter_eq { all_rows ; ship ; lützow } ; total } ; hop { filter_eq { all_rows ; ship ; seydlitz } ; total } } = true
Output: select the rows whose ship record fuzzily matches to lützow . take the total record of this row . select the rows whose ship record fuzzily matches to seydlitz . take the total record of this row . the first record is greater than the second record .
round_eq { sum { all_rows ; rainfall by depth ( mm / year ) } ; 19537 } = true
Output: the sum of the rainfall by depth  mm  year  record of all rows is 19537 .
and { only { filter_eq { all_rows ; method ; draw } } ; eq { hop { filter_eq { all_rows ; method ; draw } ; opponent } ; yoko takahashi } } = true
Output: select the rows whose method record fuzzily matches to draw . there is only one such row in the table . the opponent record of this unqiue row is yoko takahashi .
eq { hop { argmax { all_rows ; rnag ( mhz ) } ; transmitter } ; clermont carn } = true
Output: select the row whose rnag  mhz  record of all rows is maximum . the transmitter record of this row is clermont carn .
most_less { filter_eq { all_rows ; date ; december } ; attendance ; 67000 } = true
Output: select the rows whose date record fuzzily matches to december . for the attendance records of these rows , most of them are less than 67000 .
most_eq { filter_eq { all_rows ; start ; 194 } ; day power ( w ) ; 1000 } = true
Output: select the rows whose start record fuzzily matches to 194 . for the day power  w  records of these rows , most of them are equal to 1000 .
most_less_eq { all_rows ; tournaments played ; 3 } = true
Output: for the tournaments played records of all rows , most of them are less than or equal to 3 .
greater { hop { filter_eq { all_rows ; position ; 1 } ; draws } ; hop { filter_eq { all_rows ; position ; 2 } ; draws } } = true
Output: select the rows whose position record fuzzily matches to 1 . take the draws record of this row . select the rows whose position record fuzzily matches to 2 . take the draws record of this row . the first record is greater than the second record .
and { only { filter_greater { all_rows ; transfer fee ; 0 } } ; eq { hop { filter_greater { all_rows ; transfer fee ; 0 } ; player } ; matthew mills } } = true
Output: select the rows whose transfer fee record is greater than 0 . there is only one such row in the table . the player record of this unqiue row is matthew mills .
most_greater_eq { all_rows ; best score ; 29 } = true
Output: for the best score records of all rows , most of them are greater than or equal to 29 .
most_eq { all_rows ; season outcome ; failed to make playoffs } = true
Output: for the season outcome records of all rows , most of them fuzzily match to failed to make playoffs .
eq { count { filter_eq { all_rows ; reg gp ; 2 } } ; 1 } = true
Output: select the rows whose reg gp record is equal to 2 . the number of such rows is 1 .
most_greater_eq { all_rows ; time ; 2:00.00 } = true
Output: for the time records of all rows , most of them are greater than or equal to 2:00.00 .
and { greater { hop { filter_eq { all_rows ; name ; santa maria da feira } ; area ( km square ) } ; hop { filter_eq { all_rows ; name ; vale de cambra } ; area ( km square ) } } ; and { eq { hop { filter_eq { all_rows ; name ; santa maria da feira } ; area ( km square ) } ; 215.1 } ; eq { hop { filter_eq { all_rows ; name ; vale de cambra } ; area ( km square ) } ; 146.5 } } } = true
Output: select the rows whose name record fuzzily matches to santa maria da feira . take the area  km square  record of this row . select the rows whose name record fuzzily matches to vale de cambra . take the area  km square  record of this row . the first record is greater than the second record . the area  km square  record of the first row is 215.1 . the area  km square  record of the second row is 146.5 .
most_greater { all_rows ; crowd ; 20000 } = true
Output: for the crowd records of all rows , most of them are greater than 20000 .
eq { count { filter_eq { all_rows ; nationality ; united states } } ; 5 } = true
Output: select the rows whose nationality record fuzzily matches to united states . the number of such rows is 5 .
and { only { filter_not_eq { all_rows ; status ; re - elected } } ; eq { hop { filter_not_eq { all_rows ; status ; re - elected } ; incumbent } ; rick santorum } } = true
Output: select the rows whose status record does not match to re  elected . there is only one such row in the table . the incumbent record of this unqiue row is rick santorum .
most_eq { all_rows ; chassis ; dallara } = true
Output: for the chassis records of all rows , most of them fuzzily match to dallara .
eq { hop { nth_argmax { all_rows ; year ; 2 } ; host city } ; seattle , washington } = true
Output: select the row whose year record of all rows is 2nd maximum . the host city record of this row is seattle , washington .
and { only { filter_eq { all_rows ; mlb affiliate ; kansas city } } ; eq { hop { filter_eq { all_rows ; mlb affiliate ; kansas city } ; season } ; 2013 } } = true
Output: select the rows whose mlb affiliate record fuzzily matches to kansas city . there is only one such row in the table . the season record of this unqiue row is 2013 .
eq { count { filter_eq { all_rows ; theme ; montreal canadiens } } ; 2 } = true
Output: select the rows whose theme record fuzzily matches to montreal canadiens . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; place ( result ) ; 1st runner - up } } ; 2 } = true
Output: select the rows whose place  result  record fuzzily matches to 1st runner  up . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; nor 1 ; 3 } ; driver } ; jens klingmann } = true
Output: select the row whose nor 1 record of all rows is 3rd maximum . the driver record of this row is jens klingmann .
eq { hop { nth_argmax { all_rows ; number of electorates ( 2003 ) ; 2 } ; constituency number } ; 49 } = true
Output: select the row whose number of electorates  2003  record of all rows is 2nd maximum . the constituency number record of this row is 49 .
most_eq { all_rows ; station ; kstc - tv } = true
Output: for the station records of all rows , most of them fuzzily match to kstc  tv .
round_eq { sum { all_rows ; annual payroll ( 1000 ) } ; 3,521,267,581 } = true
Output: the sum of the annual payroll  1000  record of all rows is 3,521,267,581 .
eq { count { filter_eq { all_rows ; surface ; clay } } ; 3 } = true
Output: select the rows whose surface record fuzzily matches to clay . the number of such rows is 3 .
round_eq { avg { all_rows ; cuts made } ; 3 } = true
Output: the average of the cuts made record of all rows is 3 .
and { only { filter_less { all_rows ; attendance ; 20000 } } ; eq { hop { filter_less { all_rows ; attendance ; 20000 } ; date } ; sept 19 } } = true
Output: select the rows whose attendance record is less than 20000 . there is only one such row in the table . the date record of this unqiue row is sept 19 .
and { only { filter_eq { all_rows ; score ; ot } } ; eq { hop { filter_eq { all_rows ; score ; ot } ; date } ; december 26 } } = true
Output: select the rows whose score record fuzzily matches to ot . there is only one such row in the table . the date record of this unqiue row is december 26 .
eq { hop { argmin { all_rows ; built } ; class } ; class 86 } = true
Output: select the row whose built record of all rows is minimum . the class record of this row is class 86 .
greater { hop { filter_eq { all_rows ; english name ; hexi district } ; density } ; hop { filter_eq { all_rows ; english name ; fenghuang } ; density } } = true
Output: select the rows whose english name record fuzzily matches to hexi district . take the density record of this row . select the rows whose english name record fuzzily matches to fenghuang . take the density record of this row . the first record is greater than the second record .
only { filter_eq { all_rows ; method ; decision } } = true
Output: select the rows whose method record fuzzily matches to decision . there is only one such row in the table .
and { only { filter_eq { all_rows ; surface ; carpet } } ; eq { hop { filter_eq { all_rows ; surface ; carpet } ; date } ; february 14 , 1999 } } = true
Output: select the rows whose surface record fuzzily matches to carpet . there is only one such row in the table . the date record of this unqiue row is february 14 , 1999 .
eq { hop { nth_argmin { all_rows ; commissioned ; 1 } ; name } ; cygnet } = true
Output: select the row whose commissioned record of all rows is 1st minimum . the name record of this row is cygnet .
eq { count { filter_eq { filter_eq { all_rows ; city of license ; ga } ; class ; a } } ; 4 } = true
Output: select the rows whose city of license record fuzzily matches to ga . among these rows , select the rows whose class record fuzzily matches to a . the number of such rows is 4 .
and { only { filter_not_eq { all_rows ; time ; 8:00 pm } } ; eq { hop { filter_not_eq { all_rows ; time ; 8:00 pm } ; ground } ; robertson oval , wagga wagga } } = true
Output: select the rows whose time record does not match to 8:00 pm . there is only one such row in the table . the ground record of this unqiue row is robertson oval , wagga wagga .
eq { hop { nth_argmin { filter_eq { all_rows ; location attendance ; arizona veterans memorial coliseum } ; game ; 3 } ; date } ; october 26 } = true
Output: select the rows whose location attendance record fuzzily matches to arizona veterans memorial coliseum . select the row whose game record of these rows is 3rd minimum . the date record of this row is october 26 .
most_eq { all_rows ; result ; advanced } = true
Output: for the result records of all rows , most of them fuzzily match to advanced .
most_eq { all_rows ; date ; august } = true
Output: for the date records of all rows , most of them fuzzily match to august .
round_eq { avg { all_rows ; attendance } ; 17706 } = true
Output: the average of the attendance record of all rows is 17706 .
less { hop { filter_eq { all_rows ; race winner ; carlos pace } ; date } ; hop { filter_eq { all_rows ; race winner ; niki lauda } ; date } } = true
Output: select the rows whose race winner record fuzzily matches to carlos pace . take the date record of this row . select the rows whose race winner record fuzzily matches to niki lauda . take the date record of this row . the first record is less than the second record .
round_eq { avg { all_rows ; bronze } ; 1.7 } = true
Output: the average of the bronze record of all rows is 1.7 .
less { hop { filter_eq { all_rows ; film title used in nomination ; back in trouble } ; year ( ceremony ) } ; hop { filter_eq { all_rows ; film title used in nomination ; renart the fox } ; year ( ceremony ) } } = true
Output: select the rows whose film title used in nomination record fuzzily matches to back in trouble . take the year  ceremony  record of this row . select the rows whose film title used in nomination record fuzzily matches to renart the fox . take the year  ceremony  record of this row . the first record is less than the second record .
round_eq { avg { all_rows ; population } ; 338662 } = true
Output: the average of the population record of all rows is 338662 .
eq { diff { hop { filter_eq { all_rows ; name ; keutenberg } ; kilometer } ; hop { filter_eq { all_rows ; name ; fromberg } ; kilometer } } ; 4 } = true
Output: select the rows whose name record fuzzily matches to keutenberg . take the kilometer record of this row . select the rows whose name record fuzzily matches to fromberg . take the kilometer record of this row . the first record is 4 larger than the second record .
eq { count { filter_eq { all_rows ; pole position ; ayrton senna } } ; 13 } = true
Output: select the rows whose pole position record fuzzily matches to ayrton senna . the number of such rows is 13 .
eq { count { filter_eq { all_rows ; county ; essex , nj } } ; 6 } = true
Output: select the rows whose county record fuzzily matches to essex , nj . the number of such rows is 6 .
and { only { filter_eq { all_rows ; surface ; carpet } } ; eq { hop { filter_eq { all_rows ; surface ; carpet } ; year } ; 11 october 2004 } } = true
Output: select the rows whose surface record fuzzily matches to carpet . there is only one such row in the table . the year record of this unqiue row is 11 october 2004 .
eq { count { filter_all { all_rows ; nation } } ; 7 } = true
Output: select the rows whose nation record is arbitrary . the number of such rows is 7 .
eq { hop { nth_argmax { all_rows ; 1st ship delivery date ; 2 } ; yard name } ; pendleton shipyards corp } = true
Output: select the row whose 1st ship delivery date record of all rows is 2nd maximum . the yard name record of this row is pendleton shipyards corp .
eq { hop { argmax { all_rows ; potential prize money } ; season } ; 5 } = true
Output: select the row whose potential prize money record of all rows is maximum . the season record of this row is 5 .
and { only { filter_greater { all_rows ; stolen ends ; 15 } } ; eq { hop { filter_greater { all_rows ; stolen ends ; 15 } ; skip ( club ) } ; chris gardner ( renfrew ) } } = true
Output: select the rows whose stolen ends record is greater than 15 . there is only one such row in the table . the skip  club  record of this unqiue row is chris gardner  renfrew  .
round_eq { avg { all_rows ; bronze } ; 2 } = true
Output: the average of the bronze record of all rows is 2 .
eq { hop { argmin { all_rows ; first elected } ; incumbent } ; mike doyle } = true
Output: select the row whose first elected record of all rows is minimum . the incumbent record of this row is mike doyle .
eq { count { filter_eq { all_rows ; points ; 0 } } ; 5 } = true
Output: select the rows whose points record is equal to 0 . the number of such rows is 5 .
all_eq { all_rows ; chassis ; dallara } = true
Output: for the chassis records of all rows , all of them fuzzily match to dallara .
round_eq { avg { all_rows ; season } ; 2007 } = true
Output: the average of the season record of all rows is 2007 .
eq { count { filter_eq { all_rows ; played ; 17 } } ; 6 } = true
Output: select the rows whose played record is equal to 17 . the number of such rows is 6 .
eq { diff { hop { filter_eq { all_rows ; player ; john burch } ; round } ; hop { filter_eq { all_rows ; player ; kendall trainor } ; round } } ; -1 } = true
Output: select the rows whose player record fuzzily matches to john burch . take the round record of this row . select the rows whose player record fuzzily matches to kendall trainor . take the round record of this row . the second record is 1 larger than the first record .
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 ) } } = true
Output: 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  .
all_eq { all_rows ; engine ; ford v8 } = true
Output: for the engine records of all rows , all of them fuzzily match to ford v8 .
round_eq { avg { all_rows ; pick } ; 13.14 } = true
Output: the average of the pick record of all rows is 13.14 .
eq { count { filter_eq { all_rows ; publisher ; walker } } ; 6 } = true
Output: select the rows whose publisher record fuzzily matches to walker . the number of such rows is 6 .
eq { count { filter_eq { filter_eq { all_rows ; writer ( s ) ; joseph kuhr } ; us original airdate ; march } } ; 1 } = true
Output: select the rows whose writer  s  record fuzzily matches to joseph kuhr . among these rows , select the rows whose us original airdate record fuzzily matches to march . the number of such rows is 1 .
most_eq { all_rows ; nationality ; canada } = true
Output: for the nationality records of all rows , most of them fuzzily match to canada .
round_eq { avg { all_rows ; podiums } ; 48 } = true
Output: the average of the podiums record of all rows is 48 .
most_greater { all_rows ; to par ; 0 } = true
Output: for the to par records of all rows , most of them are greater than 0 .
round_eq { avg { all_rows ; area ( km square ) } ; 47 } = true
Output: the average of the area  km square  record of all rows is 47 .
and { only { filter_eq { all_rows ; category ; outstanding featured actor in a musical } } ; eq { hop { filter_eq { all_rows ; category ; outstanding featured actor in a musical } ; nominee } ; alexander gemignani } } = true
Output: select the rows whose category record fuzzily matches to outstanding featured actor in a musical . there is only one such row in the table . the nominee record of this unqiue row is alexander gemignani .
eq { hop { nth_argmin { all_rows ; score ; 2 } ; player } ; steve jones } = true
Output: select the row whose score record of all rows is 2nd minimum . the player record of this row is steve jones .
eq { hop { nth_argmin { all_rows ; date ; 7 } ; venue } ; istanbul , turkey } = true
Output: select the row whose date record of all rows is 7th minimum . the venue record of this row is istanbul , turkey .
eq { count { filter_eq { filter_eq { all_rows ; location ; boston garden } ; date ; wed } } ; 4 } = true
Output: select the rows whose location record fuzzily matches to boston garden . among these rows , select the rows whose date record fuzzily matches to wed . the number of such rows is 4 .
most_greater { all_rows ; goals ; 9 } = true
Output: for the goals records of all rows , most of them are greater than 9 .
eq { hop { nth_argmin { filter_eq { all_rows ; location attendance ; us airways center } ; date ; 4 } ; high points } ; shaquille o'neal ( 26 ) } = true
Output: select the rows whose location attendance record fuzzily matches to us airways center . select the row whose date record of these rows is 4th minimum . the high points record of this row is shaquille oneal  26  .
eq { count { filter_eq { all_rows ; laid down ; july 1933 } } ; 5 } = true
Output: select the rows whose laid down record fuzzily matches to july 1933 . the number of such rows is 5 .
eq { count { filter_eq { all_rows ; kitmaker ; adidas } } ; 3 } = true
Output: select the rows whose kitmaker record fuzzily matches to adidas . the number of such rows is 3 .
eq { hop { nth_argmax { all_rows ; w ; 2 } ; team } ; narbonne } = true
Output: select the row whose w record of all rows is 2nd maximum . the team record of this row is narbonne .
most_greater { all_rows ; crowd ; 10000 } = true
Output: for the crowd records of all rows , most of them are greater than 10000 .
most_less { all_rows ; year joined ; 1978 } = true
Output: for the year joined records of all rows , most of them are less than 1978 .
less { hop { filter_eq { all_rows ; region ; united kingdom } ; date } ; hop { filter_eq { all_rows ; region ; japan } ; date } } = true
Output: select the rows whose region record fuzzily matches to united kingdom . take the date record of this row . select the rows whose region record fuzzily matches to japan . take the date record of this row . the first record is less than the second record .
greater { hop { filter_eq { all_rows ; player ; arnold palmer } ; score } ; hop { filter_eq { all_rows ; player ; kermit zarley } ; score } } = true
Output: select the rows whose player record fuzzily matches to arnold palmer . take the score record of this row . select the rows whose player record fuzzily matches to kermit zarley . take the score record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; number of electorates ( 2009 ) ; 2 } ; name } ; mandsour } = true
Output: select the row whose number of electorates  2009  record of all rows is 2nd maximum . the name record of this row is mandsour .
round_eq { avg { filter_eq { all_rows ; model ; 1.2 tsi } ; power rpm } ; 4750 } = true
Output: select the rows whose model record fuzzily matches to 1.2 tsi . the average of the power rpm record of these rows is 4750 .
eq { hop { nth_argmin { all_rows ; first elected ; 1 } ; incumbent } ; john roane } = true
Output: select the row whose first elected record of all rows is 1st minimum . the incumbent record of this row is john roane .
eq { hop { argmax { all_rows ; number of electorates ( 2003 ) } ; name } ; niwari } = true
Output: select the row whose number of electorates  2003  record of all rows is maximum . the name record of this row is niwari .
eq { hop { argmax { all_rows ; area km 2 } ; official name } ; elgin } = true
Output: select the row whose area km 2 record of all rows is maximum . the official name record of this row is elgin .
eq { hop { argmax { all_rows ; maximum seating capacity } ; arena / venue } ; la salle coliseum } = true
Output: select the row whose maximum seating capacity record of all rows is maximum . the arena  venue record of this row is la salle coliseum .
eq { count { filter_greater_eq { filter_eq { all_rows ; country ; united states } ; total ; 300 } } ; 3 } = true
Output: select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose total record is greater than or equal to 300 . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; gold ; wu yanan } } ; 2 } = true
Output: select the rows whose gold record fuzzily matches to wu yanan . the number of such rows is 2 .
and { only { filter_eq { all_rows ; driver ; alex ribeiro } } ; eq { hop { filter_eq { all_rows ; driver ; alex ribeiro } ; year } ; 1979 } } = true
Output: select the rows whose driver record fuzzily matches to alex ribeiro . there is only one such row in the table . the year record of this unqiue row is 1979 .
greater { hop { filter_eq { all_rows ; player ; ed furgol } ; money } ; hop { filter_eq { all_rows ; player ; lloyd mangrum } ; money } } = true
Output: select the rows whose player record fuzzily matches to ed furgol . take the money record of this row . select the rows whose player record fuzzily matches to lloyd mangrum . take the money record of this row . the first record is greater than the second record .
round_eq { sum { all_rows ; points } ; 636 } = true
Output: the sum of the points record of all rows is 636 .
eq { min { all_rows ; finish position } ; 36th } = true
Output: the minimum finish position record of all rows is 36th .
eq { count { filter_eq { all_rows ; playoffs ; did not qualify } } ; 2 } = true
Output: select the rows whose playoffs record fuzzily matches to did not qualify . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; points ; 2 } ; year } ; 1970 } = true
Output: select the row whose points record of all rows is 2nd maximum . the year record of this row is 1970 .
and { only { filter_less { all_rows ; age ; 24 } } ; eq { hop { filter_less { all_rows ; age ; 24 } ; player } ; don budge } } = true
Output: select the rows whose age record is less than 24 . there is only one such row in the table . the player record of this unqiue row is don budge .
and { only { filter_eq { all_rows ; bronze ; 34 } } ; eq { hop { filter_eq { all_rows ; bronze ; 34 } ; rank } ; 5 } } = true
Output: select the rows whose bronze record is equal to 34 . there is only one such row in the table . the rank record of this unqiue row is 5 .
eq { count { filter_all { all_rows ; } } ; 9 } = true
Output: select the rows whose record is arbitrary . the number of such rows is 9 .
eq { hop { nth_argmax { all_rows ; duration ; 2 } ; interregnum began } ; 20 october 1740 death of charles vi } = true
Output: select the row whose duration record of all rows is 2nd maximum . the interregnum began record of this row is 20 october 1740 death of charles vi .
and { only { filter_eq { all_rows ; joined ; 1941 } } ; eq { hop { filter_eq { all_rows ; joined ; 1941 } ; school } ; south bend adams } } = true
Output: select the rows whose joined record is equal to 1941 . there is only one such row in the table . the school record of this unqiue row is south bend adams .
eq { count { filter_eq { all_rows ; location ; china } } ; 6 } = true
Output: select the rows whose location record fuzzily matches to china . the number of such rows is 6 .
and { only { filter_eq { all_rows ; college ; weber state } } ; eq { hop { filter_eq { all_rows ; college ; weber state } ; player } ; lee white } } = true
Output: select the rows whose college record fuzzily matches to weber state . there is only one such row in the table . the player record of this unqiue row is lee white .
all_eq { all_rows ; wins ; 0 } = true
Output: for the wins records of all rows , all of them are equal to 0 .
and { only { filter_eq { all_rows ; result ; lost re-election republican gain } } ; eq { hop { filter_eq { all_rows ; result ; lost re-election republican gain } ; incumbent } ; tom luken } } = true
Output: select the rows whose result record fuzzily matches to lost reelection republican gain . there is only one such row in the table . the incumbent record of this unqiue row is tom luken .
eq { hop { argmax { all_rows ; number of episodes } ; english title ( chinese title ) } ; dark tales ii 聊齋 ( 貳 ) } = true
Output: select the row whose number of episodes record of all rows is maximum . the english title  chinese title  record of this row is dark tales ii 聊齋  貳  .
eq { count { filter_eq { all_rows ; p1 diameter ( mm ) ; 10.77 } } ; 2 } = true
Output: select the rows whose p1 diameter  mm  record is equal to 10.77 . the number of such rows is 2 .
eq { count { filter_eq { filter_eq { all_rows ; result ; re - elected } ; first elected ; 1902 } } ; 2 } = true
Output: select the rows whose result record fuzzily matches to re  elected . among these rows , select the rows whose first elected record is equal to 1902 . the number of such rows is 2 .
less { hop { filter_eq { all_rows ; cycle ; 2 } ; number of contestants } ; hop { filter_eq { all_rows ; cycle ; 1 } ; number of contestants } } = true
Output: select the rows whose cycle record fuzzily matches to 2 . take the number of contestants record of this row . select the rows whose cycle record fuzzily matches to 1 . take the number of contestants record of this row . the first record is less than the second record .
most_greater { all_rows ; analogue power ; 1000 kw } = true
Output: for the analogue power records of all rows , most of them are greater than 1000 kw .
and { only { filter_greater { all_rows ; towns / villages ; 350 } } ; eq { hop { filter_greater { all_rows ; towns / villages ; 350 } ; name of county } ; borsod - abaúj - zemplén } } = true
Output: select the rows whose towns  villages record is greater than 350 . there is only one such row in the table . the name of county record of this unqiue row is borsod  abaúj  zemplén .
greater { hop { filter_eq { all_rows ; club ; tredegar rfc } ; lost } ; hop { filter_eq { all_rows ; club ; newbridge rfc } ; lost } } = true
Output: select the rows whose club record fuzzily matches to tredegar rfc . take the lost record of this row . select the rows whose club record fuzzily matches to newbridge rfc . take the lost record of this row . the first record is greater than the second record .
only { filter_less { filter_less_eq { all_rows ; season ; 5th } ; viewers ( in millions ) ; 12 } } = true
Output: select the rows whose season record is less than or equal to 5th . among these rows , select the rows whose viewers  in millions  record is less than 12 . there is only one such row in the table .
most_eq { filter_eq { all_rows ; province ; badajoz } ; district ; fregenal de la sierra } = true
Output: select the rows whose province record fuzzily matches to badajoz . for the district records of these rows , most of them fuzzily match to fregenal de la sierra .
eq { hop { nth_argmax { all_rows ; round ; 2 } ; player } ; jeffrey hunter } = true
Output: select the row whose round record of all rows is 2nd maximum . the player record of this row is jeffrey hunter .
eq { hop { argmax { all_rows ; gains } ; party } ; wildrose } = true
Output: select the row whose gains record of all rows is maximum . the party record of this row is wildrose .
eq { hop { argmax { all_rows ; crowd } ; venue } ; punt road oval } = true
Output: select the row whose crowd record of all rows is maximum . the venue record of this row is punt road oval .
and { only { filter_eq { all_rows ; country ; new zealand } } ; eq { hop { filter_eq { all_rows ; country ; new zealand } ; player } ; bob charles } } = true
Output: 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 .
eq { hop { argmax { filter_eq { all_rows ; subject ; mathematics } ; mean score } ; test } ; sat subject test in mathematics level 2 } = true
Output: select the rows whose subject record fuzzily matches to mathematics . select the row whose mean score record of these rows is maximum . the test record of this row is sat subject test in mathematics level 2 .
round_eq { sum { filter_eq { all_rows ; chassis ; ford fiesta } ; points } ; 727 } = true
Output: select the rows whose chassis record fuzzily matches to ford fiesta . the sum of the points record of these rows is 727 .
greater { hop { filter_eq { all_rows ; opponent ; cleber luciano } ; time } ; hop { filter_eq { all_rows ; opponent ; john borsos } ; time } } = true
Output: select the rows whose opponent record fuzzily matches to cleber luciano . take the time record of this row . select the rows whose opponent record fuzzily matches to john borsos . take the time record of this row . the first record is greater than the second record .
eq { hop { nth_argmin { all_rows ; age ; 7 } ; name } ; konstantopoulos } = true
Output: select the row whose age record of all rows is 7th minimum . the name record of this row is konstantopoulos .
less { hop { filter_eq { all_rows ; region ; japan } ; date } ; hop { filter_eq { all_rows ; region ; australia } ; date } } = true
Output: select the rows whose region record fuzzily matches to japan . take the date record of this row . select the rows whose region record fuzzily matches to australia . take the date record of this row . the first record is less than the second record .
eq { hop { argmin { all_rows ; final placing } ; series } ; porsche carrera cup germany } = true
Output: select the row whose final placing record of all rows is minimum . the series record of this row is porsche carrera cup germany .
and { only { filter_eq { all_rows ; position in 2004 ; first league } } ; eq { hop { filter_eq { all_rows ; position in 2004 ; first league } ; team } ; lokomotiv } } = true
Output: select the rows whose position in 2004 record fuzzily matches to first league . there is only one such row in the table . the team record of this unqiue row is lokomotiv .
most_eq { all_rows ; engine ; ford cosworth dfx } = true
Output: for the engine records of all rows , most of them fuzzily match to ford cosworth dfx .
and { only { filter_eq { all_rows ; written by ; lucy watkins } } ; eq { hop { filter_eq { all_rows ; written by ; lucy watkins } ; title } ; sweet dreams } } = true
Output: select the rows whose written by record fuzzily matches to lucy watkins . there is only one such row in the table . the title record of this unqiue row is sweet dreams .
eq { min { filter_eq { all_rows ; directed by ; roger christiansen } ; original air date } ; january 9 , 2010 } = true
Output: select the rows whose directed by record fuzzily matches to roger christiansen . the minimum original air date record of these rows is january 9 , 2010 .
eq { count { filter_eq { all_rows ; women 's singles ; juliane schenk } } ; 3 } = true
Output: select the rows whose women s singles record fuzzily matches to juliane schenk . the number of such rows is 3 .
and { only { filter_eq { all_rows ; score ; did not play } } ; eq { hop { filter_eq { all_rows ; score ; did not play } ; year } ; 1924 } } = true
Output: select the rows whose score record fuzzily matches to did not play . there is only one such row in the table . the year record of this unqiue row is 1924 .
eq { count { filter_eq { all_rows ; constellation ; ursa major } } ; 3 } = true
Output: select the rows whose constellation record fuzzily matches to ursa major . the number of such rows is 3 .
only { filter_greater { all_rows ; year ; 2010 } } = true
Output: select the rows whose year record is greater than 2010 . there is only one such row in the table .
eq { hop { nth_argmin { all_rows ; time ; 5 } ; athlete } ; craig virgin } = true
Output: select the row whose time record of all rows is 5th minimum . the athlete record of this row is craig virgin .
round_eq { avg { all_rows ; original air date } ; 2009 } = true
Output: the average of the original air date record of all rows is 2009 .
less { hop { filter_eq { all_rows ; incumbent ; homer thornberry } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; john dowdy } ; first elected } } = true
Output: select the rows whose incumbent record fuzzily matches to homer thornberry . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to john dowdy . take the first elected record of this row . the first record is less than the second record .
greater { hop { filter_eq { all_rows ; language ; portuguese } ; points } ; hop { filter_eq { all_rows ; language ; spanish } ; points } } = true
Output: select the rows whose language record fuzzily matches to portuguese . take the points record of this row . select the rows whose language record fuzzily matches to spanish . take the points record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; earnings ; 2 } ; player } ; don january } = true
Output: select the row whose earnings record of all rows is 2nd maximum . the player record of this row is don january .
round_eq { avg { all_rows ; laps } ; 19 } = true
Output: the average of the laps record of all rows is 19 .
most_eq { all_rows ; partner ; app } = true
Output: for the partner records of all rows , most of them fuzzily match to app .
round_eq { avg { all_rows ; points } ; 62 } = true
Output: the average of the points record of all rows is 62 .