niryuu/sni-each-converted huggingface dataset:
select the rows whose coach record fuzzily matches to brian noble . select the row whose main article record of these rows is 4th minimum . the lost record of this row is 7 .
Output: eq { hop { nth_argmin { filter_eq { all_rows ; coach ; brian noble } ; main article ; 4 } ; lost } ; 7 }
for the box score records of all rows , most of them fuzzily match to w .
Output: most_eq { all_rows ; box score ; w }
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 .
Output: eq { count { filter_greater_eq { filter_eq { all_rows ; country ; united states } ; total ; 300 } } ; 3 }
select the rows whose method record fuzzily matches to no contest . there is only one such row in the table . the opponent record of this unqiue row is john ott .
Output: and { only { filter_eq { all_rows ; method ; no contest } } ; eq { hop { filter_eq { all_rows ; method ; no contest } ; opponent } ; john ott } }
select the rows whose law preservation ticket record fuzzily matches to ( none ) . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; law preservation ticket ; ( none ) } } ; 4 }
select the rows whose extra record fuzzily matches to long jump . there is only one such row in the table . the year record of this unqiue row is 1965 .
Output: and { only { filter_eq { all_rows ; extra ; long jump } } ; eq { hop { filter_eq { all_rows ; extra ; long jump } ; year } ; 1965 } }
select the rows whose written by record fuzzily matches to brad kern . there is only one such row in the table . the title record of this unqiue row is the honeymoon 's over .
Output: and { only { filter_eq { all_rows ; written by ; brad kern } } ; eq { hop { filter_eq { all_rows ; written by ; brad kern } ; title } ; the honeymoon 's over } }
select the rows whose line color record fuzzily matches to blue . take the stations served record of this row . select the rows whose line color record fuzzily matches to pink . take the stations served record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; line color ; blue } ; stations served } ; hop { filter_eq { all_rows ; line color ; pink } ; stations served } }
select the rows whose team record fuzzily matches to pescarolo sport . for the laps records of these rows , most of them are greater than 300 .
Output: most_greater { filter_eq { all_rows ; team ; pescarolo sport } ; laps ; 300 }
select the rows whose date record fuzzily matches to august . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; date ; august } } ; 2 }
select the rows whose opponent record fuzzily matches to tampa bay buccaneers . there is only one such row in the table . the date record of this unqiue row is september 26 , 1999 .
Output: and { only { filter_eq { all_rows ; opponent ; tampa bay buccaneers } } ; eq { hop { filter_eq { all_rows ; opponent ; tampa bay buccaneers } ; date } ; september 26 , 1999 } }
select the row whose production ( mt ) record of all rows is maximum . the commodity record of this row is wheat .
Output: eq { hop { argmax { all_rows ; production ( mt ) } ; commodity } ; wheat }
select the rows whose date record fuzzily matches to october . for the result records of these rows , all of them fuzzily match to w .
Output: all_eq { filter_eq { all_rows ; date ; october } ; result ; w }
select the rows whose score record fuzzily matches to w . among these rows , select the rows whose high points record fuzzily matches to kevin martin . the number of such rows is 3 .
Output: eq { count { filter_eq { filter_eq { all_rows ; score ; w } ; high points ; kevin martin } } ; 3 }
select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose total record is greater than 153 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_eq { all_rows ; country ; united states } ; total ; 153 } } ; 2 }
select the rows whose crowd record is less than 10000 . among these rows , select the rows whose home team score record is greater than 10 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_less { all_rows ; crowd ; 10000 } ; home team score ; 10 } } ; 2 }
for the nationality records of all rows , most of them fuzzily match to united states .
Output: most_eq { all_rows ; nationality ; united states }
select the row whose duration ( days ) record of all rows is maximum . the expedition record of this row is salyut 7 - eo - 3 .
Output: eq { hop { argmax { all_rows ; duration ( days ) } ; expedition } ; salyut 7 - eo - 3 }
the 1st maximum crowd record of all rows is 40000 . the venue record of the row with 1st maximum crowd record is victoria park .
Output: and { eq { nth_max { all_rows ; crowd ; 1 } ; 40000 } ; eq { hop { nth_argmax { all_rows ; crowd ; 1 } ; venue } ; victoria park } }
select the row whose original airdate record of all rows is 2nd maximum . the series record of this row is 12 .
Output: eq { hop { nth_argmax { all_rows ; original airdate ; 2 } ; series } ; 12 }
select the rows whose year record is arbitrary . the number of such rows is 5 .
Output: eq { count { filter_all { all_rows ; year } } ; 5 }
select the rows whose player record fuzzily matches to todd van poppel . take the year record of this row . select the rows whose player record fuzzily matches to doug million . take the year record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; todd van poppel } ; year } ; hop { filter_eq { all_rows ; player ; doug million } ; year } }
select the rows whose result record fuzzily matches to 2nd . there is only one such row in the table . the year record of this unqiue row is 1963 .
Output: and { only { filter_eq { all_rows ; result ; 2nd } } ; eq { hop { filter_eq { all_rows ; result ; 2nd } ; year } ; 1963 } }
the average of the attendance record of all rows is 22526 .
Output: round_eq { avg { all_rows ; attendance } ; 22526 }
select the row whose length record of all rows is 2nd maximum . the ship record of this row is ms moby drea .
Output: eq { hop { nth_argmax { all_rows ; length ; 2 } ; ship } ; ms moby drea }
for the position records of all rows , most of them fuzzily match to center .
Output: most_eq { all_rows ; position ; center }
the average of the wins record of all rows is 3 .
Output: round_eq { avg { all_rows ; wins } ; 3 }
select the rows whose position record fuzzily matches to defensive tackle . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; position ; defensive tackle } } ; 3 }
select the rows whose team record fuzzily matches to 2009 . take the losses record of this row . select the rows whose team record fuzzily matches to 2011 . take the losses record of this row . the first record is greater than the second record . the losses record of the first row is 8 . the losses record of the second row is 4 .
Output: and { greater { hop { filter_eq { all_rows ; team ; 2009 } ; losses } ; hop { filter_eq { all_rows ; team ; 2011 } ; losses } } ; and { eq { hop { filter_eq { all_rows ; team ; 2009 } ; losses } ; 8 } ; eq { hop { filter_eq { all_rows ; team ; 2011 } ; losses } ; 4 } } }
select the row whose runs ( balls ) record of all rows is 3rd maximum . the partnerships record of this row is herschelle gibbs / justin kemp .
Output: eq { hop { nth_argmax { all_rows ; runs ( balls ) ; 3 } ; partnerships } ; herschelle gibbs / justin kemp }
select the rows whose result record fuzzily matches to re-elected . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; result ; re-elected } } ; 4 }
select the row whose pick record of all rows is 1st minimum . the nationality record of this row is canada .
Output: eq { hop { nth_argmin { all_rows ; pick ; 1 } ; nationality } ; canada }
select the rows whose founding university record fuzzily matches to university at buffalo , suny . there is only one such row in the table . the organization record of this unqiue row is delta epsilon sigma iota .
Output: and { only { filter_eq { all_rows ; founding university ; university at buffalo , suny } } ; eq { hop { filter_eq { all_rows ; founding university ; university at buffalo , suny } ; organization } ; delta epsilon sigma iota } }
select the row whose reg gp record of all rows is maximum . the player record of this row is alexander edler .
Output: eq { hop { argmax { all_rows ; reg gp } ; player } ; alexander edler }
the maximum score record of all rows is w 132 - 93 ( ot ) .
Output: eq { max { all_rows ; score } ; w 132 - 93 ( ot ) }
select the row whose candidates record of all rows is 1st maximum . the incumbent record of this row is henry clay .
Output: eq { hop { nth_argmax { all_rows ; candidates ; 1 } ; incumbent } ; henry clay }
select the rows whose type record fuzzily matches to private . the number of such rows is 12 .
Output: eq { count { filter_eq { all_rows ; type ; private } } ; 12 }
select the rows whose nationality record fuzzily matches to fin . there is only one such row in the table . the name record of this unqiue row is janne ahonen .
Output: and { only { filter_eq { all_rows ; nationality ; fin } } ; eq { hop { filter_eq { all_rows ; nationality ; fin } ; name } ; janne ahonen } }
select the rows whose player record fuzzily matches to zandro limpot , jr . take the pick record of this row . select the rows whose player record fuzzily matches to richard bachmann . take the pick record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; zandro limpot , jr } ; pick } ; hop { filter_eq { all_rows ; player ; richard bachmann } ; pick } }
select the rows whose date record fuzzily matches to october . select the row whose week record of these rows is 3rd minimum . the opponent record of this row is san francisco 49ers . the result record of this row is w 31 - 17 .
Output: and { eq { hop { nth_argmin { filter_eq { all_rows ; date ; october } ; week ; 3 } ; opponent } ; san francisco 49ers } ; eq { hop { nth_argmin { filter_eq { all_rows ; date ; october } ; week ; 3 } ; result } ; w 31 - 17 } }
select the row whose races record of all rows is 2nd minimum . the season record of this row is 2010 .
Output: eq { hop { nth_argmin { all_rows ; races ; 2 } ; season } ; 2010 }
select the rows whose gold record is equal to 0 . among these rows , select the rows whose silver record is equal to 1 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; gold ; 0 } ; silver ; 1 } } ; 2 }
select the row whose note record of all rows is maximum . the date record of this row is april 21 , 1980 . the format record of this row is stereo lp .
Output: and { eq { hop { argmax { all_rows ; note } ; date } ; april 21 , 1980 } ; eq { hop { argmax { all_rows ; note } ; format } ; stereo lp } }
the sum of the score record of all rows is 1554 .
Output: round_eq { sum { all_rows ; score } ; 1554 }
select the row whose mass ( kg ) record of all rows is maximum . the satellite record of this row is echo 2 .
Output: eq { hop { argmax { all_rows ; mass ( kg ) } ; satellite } ; echo 2 }
select the rows whose directed by record fuzzily matches to win phelps . among these rows , select the rows whose written by record fuzzily matches to david e kelley and william m finkelstein . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; directed by ; win phelps } ; written by ; david e kelley and william m finkelstein } } ; 2 }
for the date records of all rows , most of them fuzzily match to october .
Output: most_eq { all_rows ; date ; october }
the sum of the silver record of all rows is 2 .
Output: round_eq { sum { all_rows ; silver } ; 2 }
select the row whose round record of all rows is minimum . the name record of this row is joseph addai .
Output: eq { hop { argmin { all_rows ; round } ; name } ; joseph addai }
select the rows whose position record fuzzily matches to fw . select the row whose number record of these rows is 2nd maximum . the player record of this row is fwayo tembo .
Output: eq { hop { nth_argmax { filter_eq { all_rows ; position ; fw } ; number ; 2 } ; player } ; fwayo tembo }
for the date records of all rows , all of them fuzzily match to july .
Output: all_eq { all_rows ; date ; july }
select the rows whose school / club team record fuzzily matches to brigham young university . there is only one such row in the table . the player record of this unqiue row is tevita ofahengaue .
Output: and { only { filter_eq { all_rows ; school / club team ; brigham young university } } ; eq { hop { filter_eq { all_rows ; school / club team ; brigham young university } ; player } ; tevita ofahengaue } }
select the row whose max pressure record of all rows is 4th maximum . the cartridge record of this row is .38 super .
Output: eq { hop { nth_argmax { all_rows ; max pressure ; 4 } ; cartridge } ; .38 super }
select the row whose spent per vote ( php ) record of all rows is maximum . the candidate ( party ) record of this row is jamby madrigal ( i ) .
Output: eq { hop { argmax { all_rows ; spent per vote ( php ) } ; candidate ( party ) } ; jamby madrigal ( i ) }
select the rows whose nationality record fuzzily matches to nor . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; nationality ; nor } } ; 2 }
the average of the deaths record of all rows is 13.4 .
Output: round_eq { avg { all_rows ; deaths } ; 13.4 }
select the rows whose rank record fuzzily matches to 4th . there is only one such row in the table . the year record of this unqiue row is 2003 .
Output: and { only { filter_eq { all_rows ; rank ; 4th } } ; eq { hop { filter_eq { all_rows ; rank ; 4th } ; year } ; 2003 } }
select the row whose points record of all rows is maximum . the points record of this row is 227 .
Output: eq { hop { argmax { all_rows ; points } ; points } ; 227 }
select the rows whose time / retired record fuzzily matches to oil leak . there is only one such row in the table . the driver record of this unqiue row is jack brabham .
Output: and { only { filter_eq { all_rows ; time / retired ; oil leak } } ; eq { hop { filter_eq { all_rows ; time / retired ; oil leak } ; driver } ; jack brabham } }
for the apparent magnitude records of all rows , most of them are greater than 10.0 .
Output: most_greater { all_rows ; apparent magnitude ; 10.0 }
select the row whose the championship record of all rows is maximum . the player record of this row is becchio .
Output: eq { hop { argmax { all_rows ; the championship } ; player } ; becchio }
the average of the gold record of all rows is 15 .
Output: round_eq { avg { all_rows ; gold } ; 15 }
select the row whose year ( ceremony ) record of all rows is minimum . the film title used in nomination record of this row is caucasia .
Output: eq { hop { argmin { all_rows ; year ( ceremony ) } ; film title used in nomination } ; caucasia }
the average of the round record of all rows is 2.3 .
Output: round_eq { avg { all_rows ; round } ; 2.3 }
for the season premiere records of all rows , most of them fuzzily match to september .
Output: most_eq { all_rows ; season premiere ; september }
select the rows whose institution record fuzzily matches to university of texas at austin . take the enrollment record of this row . select the rows whose institution record fuzzily matches to texas christian university . take the enrollment record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; institution ; university of texas at austin } ; enrollment } ; hop { filter_eq { all_rows ; institution ; texas christian university } ; enrollment } }
select the rows whose year record is less than or equal to 2009 . the sum of the tournaments played record of these rows is 52 .
Output: round_eq { sum { filter_less_eq { all_rows ; year ; 2009 } ; tournaments played } ; 52 }
select the rows whose season record fuzzily matches to 2003 / 04 . take the goals record of this row . select the rows whose season record fuzzily matches to 2004 / 05 . take the goals record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; season ; 2003 / 04 } ; goals } ; hop { filter_eq { all_rows ; season ; 2004 / 05 } ; goals } }
select the row whose rank record of all rows is minimum . the airline record of this row is tam .
Output: eq { hop { argmin { all_rows ; rank } ; airline } ; tam }
select the rows whose institution record is arbitrary . the number of such rows is 6 .
Output: eq { count { filter_all { all_rows ; institution } } ; 6 }
select the rows whose attendance record is greater than 20000 . there is only one such row in the table . the acc team record of this unqiue row is 10 north carolina . the big ten team record of this unqiue row is 9 michigan state .
Output: and { only { filter_greater { all_rows ; attendance ; 20000 } } ; and { eq { hop { filter_greater { all_rows ; attendance ; 20000 } ; acc team } ; 10 north carolina } ; eq { hop { filter_greater { all_rows ; attendance ; 20000 } ; big ten team } ; 9 michigan state } } }
for the total records of all rows , most of them are less than 300 .
Output: most_less { all_rows ; total ; 300 }
for the country records of all rows , most of them fuzzily match to canada .
Output: most_eq { all_rows ; country ; canada }
select the rows whose race winner record fuzzily matches to patrick depailler . there is only one such row in the table . the race record of this unqiue row is spanish grand prix .
Output: and { only { filter_eq { all_rows ; race winner ; patrick depailler } } ; eq { hop { filter_eq { all_rows ; race winner ; patrick depailler } ; race } ; spanish grand prix } }
select the rows whose country record fuzzily matches to united states . among these rows , select the rows whose score record fuzzily matches to 143 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; country ; united states } ; score ; 143 } } ; 2 }
the sum of the severe tropical cyclones record of all rows is 59 .
Output: round_eq { sum { all_rows ; severe tropical cyclones } ; 59 }
select the row whose bronze record of all rows is maximum . the nation record of this row is cuba .
Output: eq { hop { argmax { all_rows ; bronze } ; nation } ; cuba }
for the status records of all rows , most of them fuzzily match to nominated .
Output: most_eq { all_rows ; status ; nominated }
select the row whose pick record of all rows is minimum . the player record of this row is james lofton .
Output: eq { hop { argmin { all_rows ; pick } ; player } ; james lofton }
select the rows whose player record fuzzily matches to dan comiskey . take the pick record of this row . select the rows whose player record fuzzily matches to kelly lochbaum . take the pick record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; dan comiskey } ; pick } ; hop { filter_eq { all_rows ; player ; kelly lochbaum } ; pick } }
select the rows whose points record is equal to 0 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; points ; 0 } } ; 4 }
select the rows whose 2002 - 03 record is equal to 5849 . there is only one such row in the table . the commodity record of this unqiue row is cattle and calves .
Output: and { only { filter_eq { all_rows ; 2002 - 03 ; 5849 } } ; eq { hop { filter_eq { all_rows ; 2002 - 03 ; 5849 } ; commodity } ; cattle and calves } }
select the rows whose location record fuzzily matches to savannah . there is only one such row in the table . the opponent record of this unqiue row is michael buchkovich .
Output: and { only { filter_eq { all_rows ; location ; savannah } } ; eq { hop { filter_eq { all_rows ; location ; savannah } ; opponent } ; michael buchkovich } }
select the rows whose segment c record fuzzily matches to part 1 . there is only one such row in the table . the episode record of this unqiue row is 147 .
Output: and { only { filter_eq { all_rows ; segment c ; part 1 } } ; eq { hop { filter_eq { all_rows ; segment c ; part 1 } ; episode } ; 147 } }
select the rows whose points record is equal to 6 . among these rows , select the rows whose difference record is equal to -10 . there is only one such row in the table . the team record of this unqiue row is amãrica .
Output: and { only { filter_eq { filter_eq { all_rows ; points ; 6 } ; difference ; -10 } } ; eq { hop { filter_eq { filter_eq { all_rows ; points ; 6 } ; difference ; -10 } ; team } ; amãrica } }
select the rows whose location attendance record fuzzily matches to at & t center . the average of the location attendance record of these rows is 18581 .
Output: round_eq { avg { filter_eq { all_rows ; location attendance ; at & t center } ; location attendance } ; 18581 }
select the rows whose writer ( s ) record fuzzily matches to mark james . there is only one such row in the table . the track record of this unqiue row is 4 . the song title record of this unqiue row is moody blue .
Output: and { only { filter_eq { all_rows ; writer ( s ) ; mark james } } ; and { eq { hop { filter_eq { all_rows ; writer ( s ) ; mark james } ; track } ; 4 } ; eq { hop { filter_eq { all_rows ; writer ( s ) ; mark james } ; song title } ; moody blue } } }
for the year records of all rows , most of them are less than 2000 .
Output: most_less { all_rows ; year ; 2000 }
the average of the losses record of all rows is 3.43 .
Output: round_eq { avg { all_rows ; losses } ; 3.43 }
for the party records of all rows , most of them fuzzily match to democrat .
Output: most_eq { all_rows ; party ; democrat }
select the rows whose 1 usd = record is equal to 1 . there is only one such row in the table . the country record of this unqiue row is ecuador .
Output: and { only { filter_eq { all_rows ; 1 usd
select the row whose runs record of all rows is maximum . the player record of this row is adam gilchrist .
Output: eq { hop { argmax { all_rows ; runs } ; player } ; adam gilchrist }
select the rows whose team record fuzzily matches to aermacchi . take the year record of this row . select the rows whose team record fuzzily matches to harley davidson . take the year record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; team ; aermacchi } ; year } ; hop { filter_eq { all_rows ; team ; harley davidson } ; year } }
select the rows whose date record is greater than 9 march 1985 . for the score records of these rows , most of them are greater than or equal to 1 .
Output: most_greater_eq { filter_greater { all_rows ; date ; 9 march 1985 } ; score ; 1 }
for the seating records of all rows , most of them are less than 100000 .
Output: most_less { all_rows ; seating ; 100000 }
for the year completed records of all rows , most of them are less than 1970 .
Output: most_less { all_rows ; year completed ; 1970 }
select the rows whose womens singles record fuzzily matches to zhang yining . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; womens singles ; zhang yining } } ; 4 }
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 .
Output: 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 } }
for the competition records of all rows , most of them fuzzily match to division 2 .
Output: most_eq { all_rows ; competition ; division 2 }
for the frequency mhz records of all rows , most of them are greater than or equal to 90 .
Output: most_greater_eq { all_rows ; frequency mhz ; 90 }
select the rows whose us viewers ( millions ) record is greater than 3.0 . there is only one such row in the table . the title record of this unqiue row is pilot .
Output: and { only { filter_greater { all_rows ; us viewers ( millions ) ; 3.0 } } ; eq { hop { filter_greater { all_rows ; us viewers ( millions ) ; 3.0 } ; title } ; pilot } }
select the rows whose villains record fuzzily matches to none . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; villains ; none } } ; 4 }
select the row whose race record of all rows is minimum . the season record of this row is 2011 .
Output: eq { hop { argmin { all_rows ; race } ; season } ; 2011 }
the average of the overall rank record of all rows is 72 .
Output: round_eq { avg { all_rows ; overall rank } ; 72 }
select the rows whose polyunsaturated fat record is less than 10 % . there is only one such row in the table . the record of this unqiue row is suet .
Output: and { only { filter_less { all_rows ; polyunsaturated fat ; 10 % } } ; eq { hop { filter_less { all_rows ; polyunsaturated fat ; 10 % } ; } ; suet } }
select the rows whose nation record fuzzily matches to sweden . take the gold record of this row . select the rows whose nation record fuzzily matches to germany . take the gold record of this row . the first record is greater than the second record . the gold record of the first row is 2 . the gold record of the second row is 1 .
Output: and { greater { hop { filter_eq { all_rows ; nation ; sweden } ; gold } ; hop { filter_eq { all_rows ; nation ; germany } ; gold } } ; and { eq { hop { filter_eq { all_rows ; nation ; sweden } ; gold } ; 2 } ; eq { hop { filter_eq { all_rows ; nation ; germany } ; gold } ; 1 } } }
select the rows whose team record fuzzily matches to aston martin racing . for the class pos records of these rows , most of them fuzzily match to dnf .
Output: most_eq { filter_eq { all_rows ; team ; aston martin racing } ; class pos ; dnf }
the average of the pts record of all rows is 2.33 .
Output: round_eq { avg { all_rows ; pts } ; 2.33 }
for the current affiliation records of all rows , most of them fuzzily match to cbs owned - and - operated ( o & o ) .
Output: most_eq { all_rows ; current affiliation ; cbs owned - and - operated ( o & o ) }
select the rows whose municipality record does not match to total . the number of such rows is 12 .
Output: eq { count { filter_not_eq { all_rows ; municipality ; total } } ; 12 }
select the row whose rank record of all rows is 4th minimum . the rowers record of this row is zsolt hirling , tamã ¡ s varga .
Output: eq { hop { nth_argmin { all_rows ; rank ; 4 } ; rowers } ; zsolt hirling , tamã ¡ s varga }
select the rows whose home team record fuzzily matches to geelong . take the crowd record of this row . select the rows whose home team record fuzzily matches to melbourne . take the crowd record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; home team ; geelong } ; crowd } ; hop { filter_eq { all_rows ; home team ; melbourne } ; crowd } }
select the rows whose candidates record is greater than or equal to 55.0 % . the number of such rows is 2 .
Output: eq { count { filter_greater_eq { all_rows ; candidates ; 55.0 % } } ; 2 }
select the rows whose county record fuzzily matches to kilkenny . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; county ; kilkenny } } ; 4 }
select the rows whose club record fuzzily matches to real madrid . the number of such rows is 9 .
Output: eq { count { filter_eq { all_rows ; club ; real madrid } } ; 9 }
select the rows whose position record fuzzily matches to goaltender . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; position ; goaltender } } ; 3 }
select the rows whose date record fuzzily matches to december . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; date ; december } } ; 3 }
select the rows whose team record fuzzily matches to forsythe racing . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; team ; forsythe racing } } ; 3 }
for the manner of departure records of all rows , most of them fuzzily match to contract ended .
Output: most_eq { all_rows ; manner of departure ; contract ended }
select the rows whose country record fuzzily matches to kazakhstan . there is only one such row in the table . the athlete record of this unqiue row is inga dudchenko .
Output: and { only { filter_eq { all_rows ; country ; kazakhstan } } ; eq { hop { filter_eq { all_rows ; country ; kazakhstan } ; athlete } ; inga dudchenko } }
select the rows whose status record fuzzily matches to second test . there is only one such row in the table . the opposing team record of this unqiue row is argentina . the date record of this unqiue row is 4 august 1990 .
Output: and { only { filter_eq { all_rows ; status ; second test } } ; and { eq { hop { filter_eq { all_rows ; status ; second test } ; opposing team } ; argentina } ; eq { hop { filter_eq { all_rows ; status ; second test } ; date } ; 4 august 1990 } } }
select the rows whose surface record fuzzily matches to hard ( i ) . there is only one such row in the table .
Output: only { filter_eq { all_rows ; surface ; hard ( i ) } }
select the rows whose player record fuzzily matches to wayne walker . take the overall pick record of this row . select the rows whose player record fuzzily matches to john yarno . take the overall pick record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; player ; wayne walker } ; overall pick } ; hop { filter_eq { all_rows ; player ; john yarno } ; overall pick } }
select the rows whose exited record fuzzily matches to day 16 . the maximum finished record of these rows is 3rd .
Output: eq { max { filter_eq { all_rows ; exited ; day 16 } ; finished } ; 3rd }
the average of the p1 diameter ( mm ) record of all rows is 13.425 .
Output: round_eq { avg { all_rows ; p1 diameter ( mm ) } ; 13.425 }
select the rows whose game site record fuzzily matches to gillette stadium . the average of the result record of these rows is 26 .
Output: round_eq { avg { filter_eq { all_rows ; game site ; gillette stadium } ; result } ; 26 }
the average of the time record of all rows is 2:40 .
Output: round_eq { avg { all_rows ; time } ; 2:40 }
the average of the decile record of all rows is 5 .
Output: round_eq { avg { all_rows ; decile } ; 5 }
the sum of the worldwide gross record of all rows is 2632 .
Output: round_eq { sum { all_rows ; worldwide gross } ; 2632 }
select the rows whose high points record fuzzily matches to dorell wright . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; high points ; dorell wright } } ; 2 }
select the rows whose race record fuzzily matches to warwick stakes . take the distance record of this row . select the rows whose race record fuzzily matches to george main stakes . take the distance record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; race ; warwick stakes } ; distance } ; hop { filter_eq { all_rows ; race ; george main stakes } ; distance } }
the 4th minimum rank record of all rows is 4 . the team record of the row with 4th minimum rank record is estonia . the wins record of the row with 4th minimum rank record is 2 . the losses record of the row with 4th minimum rank record is 3 . the points record of the row with 4th minimum rank record is 4 .
Output: and { eq { nth_min { all_rows ; rank ; 4 } ; 4 } ; and { eq { hop { nth_argmin { all_rows ; rank ; 4 } ; team } ; estonia } ; and { eq { hop { nth_argmin { all_rows ; rank ; 4 } ; wins } ; 2 } ; and { eq { hop { nth_argmin { all_rows ; rank ; 4 } ; losses } ; 3 } ; eq { hop { nth_argmin { all_rows ; rank ; 4 } ; points } ; 4 } } } } }
select the rows whose release date record fuzzily matches to 2004 . there is only one such row in the table . the version record of this unqiue row is 4.10 .
Output: and { only { filter_eq { all_rows ; release date ; 2004 } } ; eq { hop { filter_eq { all_rows ; release date ; 2004 } ; version } ; 4.10 } }
select the row whose matches record of all rows is 2nd maximum . the name record of this row is glenn mcgrath .
Output: eq { hop { nth_argmax { all_rows ; matches ; 2 } ; name } ; glenn mcgrath }
for the results records of all rows , most of them fuzzily match to re - elected .
Output: most_eq { all_rows ; results ; re - elected }
select the rows whose date record fuzzily matches to september . among these rows , select the rows whose attendance record is greater than 70000 . there is only one such row in the table .
Output: only { filter_greater { filter_eq { all_rows ; date ; september } ; attendance ; 70000 } }
select the rows whose fate record fuzzily matches to damaged . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; fate ; damaged } } ; 2 }
select the rows whose gold record is equal to 4 . there is only one such row in the table . the nation record of this unqiue row is south korea .
Output: and { only { filter_eq { all_rows ; gold ; 4 } } ; eq { hop { filter_eq { all_rows ; gold ; 4 } ; nation } ; south korea } }
for the tournaments played records of all rows , most of them are less than or equal to 3 .
Output: most_less_eq { all_rows ; tournaments played ; 3 }
select the rows whose opponent record fuzzily matches to copenhagen . the number of such rows is 2 .
Output: eq { count { filter_eq { all_rows ; opponent ; copenhagen } } ; 2 }
select the rows whose genre record fuzzily matches to fighting . there is only one such row in the table . the game record of this unqiue row is super smash bros melee .
Output: and { only { filter_eq { all_rows ; genre ; fighting } } ; eq { hop { filter_eq { all_rows ; genre ; fighting } ; game } ; super smash bros melee } }
select the rows whose service years record fuzzily matches to 1989 . there is only one such row in the table . the ship name record of this unqiue row is kri halim perdanakususma .
Output: and { only { filter_eq { all_rows ; service years ; 1989 } } ; eq { hop { filter_eq { all_rows ; service years ; 1989 } ; ship name } ; kri halim perdanakususma } }
select the rows whose date record fuzzily matches to 10 june 2009 . take the competition record of this row . select the rows whose date record fuzzily matches to 5 september 2009 . take the competition record of this row . the first record fuzzily matches to the second record . the competition record of the first row is 2010 fifa world cup qualification . the competition record of the second row is 2010 fifa world cup qualification .
Output: and { eq { hop { filter_eq { all_rows ; date ; 10 june 2009 } ; competition } ; hop { filter_eq { all_rows ; date ; 5 september 2009 } ; competition } } ; and { eq { hop { filter_eq { all_rows ; date ; 10 june 2009 } ; competition } ; 2010 fifa world cup qualification } ; eq { hop { filter_eq { all_rows ; date ; 5 september 2009 } ; competition } ; 2010 fifa world cup qualification } } }
select the rows whose game record fuzzily matches to 3 . take the time record of this row . select the rows whose game record fuzzily matches to 4 . take the time record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; game ; 3 } ; time } ; hop { filter_eq { all_rows ; game ; 4 } ; time } }
select the rows whose result record fuzzily matches to w . the minimum date record of these rows is september 13 .
Output: eq { min { filter_eq { all_rows ; result ; w } ; date } ; september 13 }
select the rows whose score record fuzzily matches to 2 . among these rows , select the rows whose opponent record fuzzily matches to minnesota north stars . there is only one such row in the table . the february record of this unqiue row is 1 .
Output: and { only { filter_eq { filter_eq { all_rows ; score ; 2 } ; opponent ; minnesota north stars } } ; eq { hop { filter_eq { filter_eq { all_rows ; score ; 2 } ; opponent ; minnesota north stars } ; february } ; 1 } }
the average of the vuelta record of all rows is 1.67 .
Output: round_eq { avg { all_rows ; vuelta } ; 1.67 }
the average of the occurrences record of all rows is 10 .
Output: round_eq { avg { all_rows ; occurrences } ; 10 }
the sum of the points record of all rows is 97 .
Output: round_eq { sum { all_rows ; points } ; 97 }
select the rows whose graduated record is equal to 2009 . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; graduated ; 2009 } } ; 5 }
select the rows whose crowd record is less than 20000 . the number of such rows is 3 .
Output: eq { count { filter_less { all_rows ; crowd ; 20000 } } ; 3 }
select the rows whose first elected record is less than 2006 . there is only one such row in the table . the incumbent record of this unqiue row is john kline .
Output: and { only { filter_less { all_rows ; first elected ; 2006 } } ; eq { hop { filter_less { all_rows ; first elected ; 2006 } ; incumbent } ; john kline } }
select the rows whose high points record fuzzily matches to paul pierce . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; high points ; paul pierce } } ; 5 }
the average of the crowd record of all rows is 25000 .
Output: round_eq { avg { all_rows ; crowd } ; 25000 }
select the rows whose opponent record fuzzily matches to peterborough united . take the date record of this row . select the rows whose opponent record fuzzily matches to villarreal . take the date record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; opponent ; peterborough united } ; date } ; hop { filter_eq { all_rows ; opponent ; villarreal } ; date } }
select the rows whose tribunal record fuzzily matches to coimbra . take the executions in persona record of this row . select the rows whose tribunal record fuzzily matches to goa . take the executions in persona record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; tribunal ; coimbra } ; executions in persona } ; hop { filter_eq { all_rows ; tribunal ; goa } ; executions in persona } }
select the rows whose role record fuzzily matches to police officer lung . the number of such rows is 5 .
Output: eq { count { filter_eq { all_rows ; role ; police officer lung } } ; 5 }
select the rows whose team record fuzzily matches to honda . the sum of the wins record of these rows is 6 .
Output: round_eq { sum { filter_eq { all_rows ; team ; honda } ; wins } ; 6 }
the average of the attendance record of all rows is 30349 .
Output: round_eq { avg { all_rows ; attendance } ; 30349 }
select the rows whose country record fuzzily matches to india . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; country ; india } } ; 4 }
for the laps records of all rows , most of them are equal to 48 .
Output: most_eq { all_rows ; laps ; 48 }
the average of the podiums record of all rows is 2.89 .
Output: round_eq { avg { all_rows ; podiums } ; 2.89 }
select the rows whose year ( ceremony ) record fuzzily matches to 2006 ( 79th ) . take the year ( ceremony ) record of this row . select the rows whose year ( ceremony ) record fuzzily matches to 2007 ( 80th ) . take the year ( ceremony ) record of this row . the first record is less than the second record . the year ( ceremony ) record of the first row is 2006 ( 79th ) . the year ( ceremony ) record of the second row is 2007 ( 80th ) .
Output: and { less { hop { filter_eq { all_rows ; year ( ceremony ) ; 2006 ( 79th ) } ; year ( ceremony ) } ; hop { filter_eq { all_rows ; year ( ceremony ) ; 2007 ( 80th ) } ; year ( ceremony ) } } ; and { eq { hop { filter_eq { all_rows ; year ( ceremony ) ; 2006 ( 79th ) } ; year ( ceremony ) } ; 2006 ( 79th ) } ; eq { hop { filter_eq { all_rows ; year ( ceremony ) ; 2007 ( 80th ) } ; year ( ceremony ) } ; 2007 ( 80th ) } } }
select the rows whose host record fuzzily matches to hopton - on - sea . there is only one such row in the table . the season record of this unqiue row is 1998 / 99 .
Output: and { only { filter_eq { all_rows ; host ; hopton - on - sea } } ; eq { hop { filter_eq { all_rows ; host ; hopton - on - sea } ; season } ; 1998 / 99 } }
select the rows whose incumbent record fuzzily matches to weldon n edwards . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to romulus m saunders . 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 ; weldon n edwards } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; romulus m saunders } ; first elected } }
select the rows whose crowd record is greater than 20000 . among these rows , select the rows whose home team score record is greater than 10 . the number of such rows is 2 .
Output: eq { count { filter_greater { filter_greater { all_rows ; crowd ; 20000 } ; home team score ; 10 } } ; 2 }
select the rows whose opposing team record fuzzily matches to fiji . take the against record of this row . select the rows whose opposing team record fuzzily matches to taranaki . take the against record of this row . the first record is greater than the second record .
Output: greater { hop { filter_eq { all_rows ; opposing team ; fiji } ; against } ; hop { filter_eq { all_rows ; opposing team ; taranaki } ; against } }
the minimum year record of all rows is 1968 .
Output: eq { min { all_rows ; year } ; 1968 }
for the used as or integrated with records of all rows , most of them fuzzily match to software .
Output: most_eq { all_rows ; used as or integrated with ; software }
select the rows whose class record fuzzily matches to b . there is only one such row in the table . the drivers record of this unqiue row is alan jones peter fitzgerald .
Output: and { only { filter_eq { all_rows ; class ; b } } ; eq { hop { filter_eq { all_rows ; class ; b } ; drivers } ; alan jones peter fitzgerald } }
select the rows whose result record fuzzily matches to 1st . there is only one such row in the table . the tournament record of this unqiue row is european championships . the extra record of this unqiue row is 400 m .
Output: and { only { filter_eq { all_rows ; result ; 1st } } ; and { eq { hop { filter_eq { all_rows ; result ; 1st } ; tournament } ; european championships } ; eq { hop { filter_eq { all_rows ; result ; 1st } ; extra } ; 400 m } } }
select the rows whose year joined record is equal to 1953 . the number of such rows is 4 .
Output: eq { count { filter_eq { all_rows ; year joined ; 1953 } } ; 4 }
the sum of the points record of all rows is 473 .
Output: round_eq { sum { all_rows ; points } ; 473 }
the average of the saturated fat record of all rows is 22 .
Output: round_eq { avg { all_rows ; saturated fat } ; 22 }
select the rows whose builder record fuzzily matches to kerr stuart . there is only one such row in the table . the name record of this unqiue row is princess .
Output: and { only { filter_eq { all_rows ; builder ; kerr stuart } } ; eq { hop { filter_eq { all_rows ; builder ; kerr stuart } ; name } ; princess } }
select the rows whose candidate record fuzzily matches to blake chandler . take the result record of this row . select the rows whose candidate record fuzzily matches to mary - anne lowe . take the result record of this row . the first record is less than the second record .
Output: less { hop { filter_eq { all_rows ; candidate ; blake chandler } ; result } ; hop { filter_eq { all_rows ; candidate ; mary - anne lowe } ; result } }
select the rows whose height record fuzzily matches to 6 ' 4 . among these rows , select the rows whose round record is equal to 2 . the number of such rows is 2 .
Output: eq { count { filter_eq { filter_eq { all_rows ; height ; 6 ' 4 } ; round ; 2 } } ; 2 }
the average of the total record of all rows is 9.29 .
Output: round_eq { avg { all_rows ; total } ; 9.29 }
select the rows whose date record fuzzily matches to 01/1992 . there is only one such row in the table .
Output: only { filter_eq { all_rows ; date ; 01/1992 } }
select the rows whose position record fuzzily matches to defensive back . there is only one such row in the table . the player record of this unqiue row is george donnelly .
Output: and { only { filter_eq { all_rows ; position ; defensive back } } ; eq { hop { filter_eq { all_rows ; position ; defensive back } ; player } ; george donnelly } }
select the rows whose first elected record is less than 1800 . there is only one such row in the table . the incumbent record of this unqiue row is thomas newton , jr .
Output: and { only { filter_less { all_rows ; first elected ; 1800 } } ; eq { hop { filter_less { all_rows ; first elected ; 1800 } ; incumbent } ; thomas newton , jr } }
select the row whose overall wc points ( rank ) record of all rows is maximum . the name record of this row is gregor schlierenzauer .
Output: eq { hop { argmax { all_rows ; overall wc points ( rank ) } ; name } ; gregor schlierenzauer }
for the date records of all rows , all of them fuzzily match to 19 april 1975 .
Output: all_eq { all_rows ; date ; 19 april 1975 }
select the rows whose competition record fuzzily matches to world championships . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; competition ; world championships } } ; 3 }
select the rows whose points record is greater than 20 . the number of such rows is 3 .
Output: eq { count { filter_greater { all_rows ; points ; 20 } } ; 3 }
select the rows whose nation record fuzzily matches to united states . the average of the fastest time ( s ) record of these rows is 11.08 .
Output: round_eq { avg { filter_eq { all_rows ; nation ; united states } ; fastest time ( s ) } ; 11.08 }
for the reason for change records of all rows , all of them fuzzily match to died .
Output: all_eq { all_rows ; reason for change ; died }
select the row whose height record of all rows is minimum . the player record of this row is mithat demirel .
Output: eq { hop { argmin { all_rows ; height } ; player } ; mithat demirel }
select the rows whose high assists record fuzzily matches to blake griffin . the average of the high assists record of these rows is 9 .
Output: round_eq { avg { filter_eq { all_rows ; high assists ; blake griffin } ; high assists } ; 9 }
select the rows whose party record fuzzily matches to csv . there is only one such row in the table . the minister record of this unqiue row is marc fischbach .
Output: and { only { filter_eq { all_rows ; party ; csv } } ; eq { hop { filter_eq { all_rows ; party ; csv } ; minister } ; marc fischbach } }
select the row whose crowd record of all rows is maximum . the home team record of this row is collingwood .
Output: eq { hop { argmax { all_rows ; crowd } ; home team } ; collingwood }
select the rows whose assists record is equal to 10 . there is only one such row in the table . the name record of this unqiue row is terrell mcintyre .
Output: and { only { filter_eq { all_rows ; assists ; 10 } } ; eq { hop { filter_eq { all_rows ; assists ; 10 } ; name } ; terrell mcintyre } }
select the rows whose first elected record is equal to 1824 . the number of such rows is 3 .
Output: eq { count { filter_eq { all_rows ; first elected ; 1824 } } ; 3 }
select the rows whose date record fuzzily matches to september . the sum of the attendance record of these rows is under 200,000 .
Output: round_eq { sum { filter_eq { all_rows ; date ; september } ; attendance } ; under 200,000 }
the average of the laps record of all rows is 192 .
Output: round_eq { avg { all_rows ; laps } ; 192 }
select the row whose high points record of all rows is maximum . the high points record of this row is dalembert ( 23 ) .
Output: eq { hop { argmax { all_rows ; high points } ; high points } ; dalembert ( 23 ) }
select the rows whose area ( km square ) record is greater than 1000000 . for the population ( millions , 2011 ) records of these rows , most of them are greater than 6 .
Output: most_greater { filter_greater { all_rows ; area ( km square ) ; 1000000 } ; population ( millions , 2011 ) ; 6 }
select the rows whose team record fuzzily matches to season cancelled . there is only one such row in the table . the season record of this unqiue row is 2009 - 10 .
Output: and { only { filter_eq { all_rows ; team ; season cancelled } } ; eq { hop { filter_eq { all_rows ; team ; season cancelled } ; season } ; 2009 - 10 } }
select the rows whose state record fuzzily matches to cao . there is only one such row in the table . the name record of this unqiue row is zhong .
Output: and { only { filter_eq { all_rows ; state ; cao } } ; eq { hop { filter_eq { all_rows ; state ; cao } ; name } ; zhong } }