niryuu/sni-each-converted huggingface dataset:
most_eq { all_rows ; vacator ; d } = true
Output: for the vacator records of all rows , most of them fuzzily match to d .
round_eq { sum { all_rows ; crowd } ; 79,000 } = true
Output: the sum of the crowd record of all rows is 79,000 .
eq { count { filter_eq { all_rows ; platform ; unix } } ; 1 } = true
Output: select the rows whose platform record fuzzily matches to unix . the number of such rows is 1 .
eq { hop { argmin { all_rows ; position } ; pilot } ; mario kiessling } = true
Output: select the row whose position record of all rows is minimum . the pilot record of this row is mario kiessling .
eq { hop { argmax { all_rows ; total } ; nation } ; france } = true
Output: select the row whose total record of all rows is maximum . the nation record of this row is france .
greater { hop { filter_eq { all_rows ; runner ( s ) - up ; phil mickelson } ; margin of victory } ; hop { filter_eq { all_rows ; runner ( s ) - up ; dudley hart } ; margin of victory } } = true
Output: select the rows whose runner  s   up record fuzzily matches to phil mickelson . take the margin of victory record of this row . select the rows whose runner  s   up record fuzzily matches to dudley hart . take the margin of victory record of this row . the first record is greater than the second record .
eq { hop { nth_argmax { all_rows ; original air date ; 2 } ; title } ; the western isles and shetland } = true
Output: select the row whose original air date record of all rows is 2nd maximum . the title record of this row is the western isles and shetland .
eq { count { filter_eq { all_rows ; nationality ; bulgaria } } ; 2 } = true
Output: select the rows whose nationality record fuzzily matches to bulgaria . the number of such rows is 2 .
eq { hop { argmax { all_rows ; gold } ; nation } ; germany } = true
Output: select the row whose gold record of all rows is maximum . the nation record of this row is germany .
round_eq { sum { filter_eq { all_rows ; constructor ; ferrari } ; laps } ; 140 } = true
Output: select the rows whose constructor record fuzzily matches to ferrari . the sum of the laps record of these rows is 140 .
eq { count { filter_all { all_rows ; season } } ; 5 } = true
Output: select the rows whose season record is arbitrary . the number of such rows is 5 .
most_greater { all_rows ; indian population ; 10000 } = true
Output: for the indian population records of all rows , most of them are greater than 10000 .
eq { count { filter_greater { all_rows ; rec ; 50 } } ; 4 } = true
Output: select the rows whose rec record is greater than 50 . the number of such rows is 4 .
round_eq { sum { all_rows ; wins } ; 4 } = true
Output: the sum of the wins record of all rows is 4 .
most_eq { all_rows ; surface ; carpet } = true
Output: for the surface records of all rows , most of them fuzzily match to carpet .
eq { count { filter_eq { filter_eq { all_rows ; arena ; honda center } ; attendance ; 17174 } } ; 5 } = true
Output: select the rows whose arena record fuzzily matches to honda center . among these rows , select the rows whose attendance record is equal to 17174 . the number of such rows is 5 .
eq { hop { argmin { all_rows ; original air date } ; title } ; a doomed christmas } = true
Output: select the row whose original air date record of all rows is minimum . the title record of this row is a doomed christmas .
eq { count { filter_eq { all_rows ; crowd ; 10000 } } ; 2 } = true
Output: select the rows whose crowd record is equal to 10000 . the number of such rows is 2 .
eq { hop { argmax { all_rows ; total ( kg ) } ; name } ; idalberto aranda ( cub ) } = true
Output: select the row whose total  kg  record of all rows is maximum . the name record of this row is idalberto aranda  cub  .
eq { hop { argmin { all_rows ; hosted 4 teams since } ; metropolitan area } ; detroit , michigan } = true
Output: select the row whose hosted 4 teams since record of all rows is minimum . the metropolitan area record of this row is detroit , michigan .
most_eq { filter_eq { all_rows ; ; t2 } ; country ; australia } = true
Output: select the rows whose record fuzzily matches to t2 . for the country records of these rows , most of them fuzzily match to australia .
and { only { filter_eq { all_rows ; tv network ( s ) ; fox8 } } ; eq { hop { filter_eq { all_rows ; tv network ( s ) ; fox8 } ; country } ; australia } } = true
Output: select the rows whose tv network  s  record fuzzily matches to fox8 . there is only one such row in the table . the country record of this unqiue row is australia .
eq { count { filter_eq { all_rows ; college ; ohio state } } ; 3 } = true
Output: select the rows whose college record fuzzily matches to ohio state . the number of such rows is 3 .
round_eq { avg { all_rows ; total } ; 27.42 } = true
Output: the average of the total record of all rows is 27.42 .
most_greater { all_rows ; shot pct ; 65 % } = true
Output: for the shot pct records of all rows , most of them are greater than 65  .
eq { hop { argmax { all_rows ; attendance } ; location } ; memorial stadium clemson , sc } = true
Output: select the row whose attendance record of all rows is maximum . the location record of this row is memorial stadium clemson , sc .
round_eq { avg { filter_less_eq { all_rows ; rank ; 3 } ; time } ; 7:30.63 } = true
Output: select the rows whose rank record is less than or equal to 3 . the average of the time record of these rows is 7:30.63 .
round_eq { avg { filter_eq { all_rows ; high assists ; beno udrih } ; high assists } ; 6.29 } = true
Output: select the rows whose high assists record fuzzily matches to beno udrih . the average of the high assists record of these rows is 6.29 .
round_eq { sum { all_rows ; us viewers ( millions ) } ; 78.52 } = true
Output: the sum of the us viewers  millions  record of all rows is 78.52 .
and { only { filter_eq { all_rows ; music ; danu huntrakul } } ; eq { hop { filter_eq { all_rows ; music ; danu huntrakul } ; year } ; 2004 } } = true
Output: select the rows whose music record fuzzily matches to danu huntrakul . there is only one such row in the table . the year record of this unqiue row is 2004 .
eq { hop { nth_argmin { all_rows ; wins ; 2 } ; club } ; cd alcoyano } = true
Output: select the row whose wins record of all rows is 2nd minimum . the club record of this row is cd alcoyano .
only { filter_eq { all_rows ; result ; t } } = true
Output: select the rows whose result record fuzzily matches to t . there is only one such row in the table .
most_eq { all_rows ; location ; ghent } = true
Output: for the location records of all rows , most of them fuzzily match to ghent .
eq { count { filter_eq { all_rows ; incumbent ; none ( district created ) } } ; 2 } = true
Output: select the rows whose incumbent record fuzzily matches to none  district created  . the number of such rows is 2 .
eq { count { filter_less { all_rows ; metres ; 200 } } ; 3 } = true
Output: select the rows whose metres record is less than 200 . the number of such rows is 3 .
eq { count { filter_eq { all_rows ; remixed by ; laurent boutonnat } } ; 2 } = true
Output: select the rows whose remixed by record fuzzily matches to laurent boutonnat . the number of such rows is 2 .
eq { hop { argmax { all_rows ; played } ; manager } ; juan carlos chávez } = true
Output: select the row whose played record of all rows is maximum . the manager record of this row is juan carlos chávez .
most_eq { all_rows ; chassis ; march } = true
Output: for the chassis records of all rows , most of them fuzzily match to march .
and { only { filter_eq { all_rows ; status ; display only } } ; eq { hop { filter_eq { all_rows ; status ; display only } ; number } ; 1 } } = true
Output: select the rows whose status record fuzzily matches to display only . there is only one such row in the table . the number record of this unqiue row is 1 .
greater { hop { filter_eq { all_rows ; rider ; steve plater } ; speed } ; hop { filter_eq { all_rows ; rider ; denver robb } ; speed } } = true
Output: select the rows whose rider record fuzzily matches to steve plater . take the speed record of this row . select the rows whose rider record fuzzily matches to denver robb . take the speed record of this row . the first record is greater than the second record .
eq { hop { argmax { all_rows ; home team score } ; home team } ; hawthorn } = true
Output: select the row whose home team score record of all rows is maximum . the home team record of this row is hawthorn .
eq { hop { argmin { all_rows ; power } ; city of license } ; gaspé } = true
Output: select the row whose power record of all rows is minimum . the city of license record of this row is gaspé .
eq { hop { argmax { all_rows ; apps } ; name } ; john hall } = true
Output: select the row whose apps record of all rows is maximum . the name record of this row is john hall .
and { not_eq { hop { filter_eq { all_rows ; player ; steve pepin } ; nhl team } ; hop { filter_eq { all_rows ; player ; john miner } ; nhl team } } ; and { eq { hop { filter_eq { all_rows ; player ; steve pepin } ; nhl team } ; chicago black hawks } ; eq { hop { filter_eq { all_rows ; player ; john miner } ; nhl team } ; edmonton oilers } } } = true
Output: select the rows whose player record fuzzily matches to steve pepin . take the nhl team record of this row . select the rows whose player record fuzzily matches to john miner . take the nhl team record of this row . the first record does not match to the second record . the nhl team record of the first row is chicago black hawks . the nhl team record of the second row is edmonton oilers .
eq { count { filter_eq { all_rows ; directed by ; david grossman } } ; 5 } = true
Output: select the rows whose directed by record fuzzily matches to david grossman . the number of such rows is 5 .
and { only { filter_greater { all_rows ; yards ; 1400 } } ; eq { hop { filter_greater { all_rows ; yards ; 1400 } ; player } ; derrick lewis } } = true
Output: select the rows whose yards record is greater than 1400 . there is only one such row in the table . the player record of this unqiue row is derrick lewis .
eq { hop { argmax { all_rows ; time / retired } ; rider } ; dani pedrosa } = true
Output: select the row whose time  retired record of all rows is maximum . the rider record of this row is dani pedrosa .
and { only { filter_eq { all_rows ; result ; ot } } ; eq { hop { filter_eq { all_rows ; result ; ot } ; date } ; november 24 , 1985 } } = true
Output: select the rows whose result record fuzzily matches to ot . there is only one such row in the table . the date record of this unqiue row is november 24 , 1985 .
eq { count { filter_eq { all_rows ; entrant ; british racing partnership } } ; 2 } = true
Output: select the rows whose entrant record fuzzily matches to british racing partnership . the number of such rows is 2 .
and { only { filter_eq { all_rows ; subject ; andy harris } } ; and { eq { hop { filter_eq { all_rows ; subject ; andy harris } ; year } ; 2010 } ; eq { hop { filter_eq { all_rows ; subject ; andy harris } ; office } ; us house , maryland 's 1st district } } } = true
Output: select the rows whose subject record fuzzily matches to andy harris . there is only one such row in the table . the year record of this unqiue row is 2010 . the office record of this unqiue row is us house , maryland s 1st district .
eq { count { filter_eq { all_rows ; written by ; julian jones } } ; 6 } = true
Output: select the rows whose written by record fuzzily matches to julian jones . the number of such rows is 6 .
eq { count { filter_less { all_rows ; to par ; 0 } } ; 4 } = true
Output: select the rows whose to par record is less than 0 . the number of such rows is 4 .
only { filter_eq { all_rows ; studio ; tristar pictures } } = true
Output: select the rows whose studio record fuzzily matches to tristar pictures . there is only one such row in the table .
and { only { filter_eq { all_rows ; nationality ; bosnia and herzegovina } } ; eq { hop { filter_eq { all_rows ; nationality ; bosnia and herzegovina } ; previous team } ; golden state warriors } } = true
Output: select the rows whose nationality record fuzzily matches to bosnia and herzegovina . there is only one such row in the table . the previous team record of this unqiue row is golden state warriors .
all_eq { all_rows ; composition ; coed } = true
Output: for the composition records of all rows , all of them fuzzily match to coed .
most_greater { all_rows ; crowd ; 6000 } = true
Output: for the crowd records of all rows , most of them are greater than 6000 .
round_eq { avg { all_rows ; height } ; 6 ' 4 " } = true
Output: the average of the height record of all rows is 6  4  .
and { only { filter_eq { all_rows ; sprints classification ; no award } } ; eq { hop { filter_eq { all_rows ; sprints classification ; no award } ; stage } ; 1 } } = true
Output: select the rows whose sprints classification record fuzzily matches to no award . there is only one such row in the table . the stage record of this unqiue row is 1 .
eq { count { filter_eq { all_rows ; college / junior / club team ; regina pats } } ; 2 } = true
Output: select the rows whose college  junior  club team record fuzzily matches to regina pats . the number of such rows is 2 .
eq { hop { nth_argmax { all_rows ; attendance ; 1 } ; acc team } ; 4 duke } = true
Output: select the row whose attendance record of all rows is 1st maximum . the acc team record of this row is 4 duke .
eq { hop { nth_argmax { all_rows ; score ; 1 } ; ground } ; humber college north } = true
Output: select the row whose score record of all rows is 1st maximum . the ground record of this row is humber college north .
eq { hop { argmax { all_rows ; attendance } ; opponent } ; new york giants } = true
Output: select the row whose attendance record of all rows is maximum . the opponent record of this row is new york giants .
eq { count { filter_eq { all_rows ; surface ; carpet } } ; 2 } = true
Output: select the rows whose surface record fuzzily matches to carpet . the number of such rows is 2 .
eq { hop { argmax { all_rows ; length } ; tracks } ; empty pages } = true
Output: select the row whose length record of all rows is maximum . the tracks record of this row is empty pages .
round_eq { avg { all_rows ; field goals } ; 113 } = true
Output: the average of the field goals record of all rows is 113 .
less { hop { filter_eq { all_rows ; oberbayern a ; bsc sendling } ; season } ; hop { filter_eq { all_rows ; oberbayern a ; fc oberau } ; season } } = true
Output: select the rows whose oberbayern a record fuzzily matches to bsc sendling . take the season record of this row . select the rows whose oberbayern a record fuzzily matches to fc oberau . take the season record of this row . the first record is less than the second record .
eq { hop { argmax { all_rows ; win % } ; coach } ; rick barnes } = true
Output: select the row whose win  record of all rows is maximum . the coach record of this row is rick barnes .
and { only { filter_not_eq { all_rows ; result ; re - elected } } ; eq { hop { filter_not_eq { all_rows ; result ; re - elected } ; incumbent } ; brooks hays } } = true
Output: select the rows whose result record does not match to re  elected . there is only one such row in the table . the incumbent record of this unqiue row is brooks hays .
and { eq { hop { filter_eq { all_rows ; incumbent ; william h perry } ; first elected } ; hop { filter_eq { all_rows ; incumbent ; william elliott } ; first elected } } ; and { eq { hop { filter_eq { all_rows ; incumbent ; william h perry } ; first elected } ; 1884 } ; eq { hop { filter_eq { all_rows ; incumbent ; william elliott } ; first elected } ; 1884 } } } = true
Output: select the rows whose incumbent record fuzzily matches to william h perry . take the first elected record of this row . select the rows whose incumbent record fuzzily matches to william elliott . take the first elected record of this row . the first record is equal to the second record . the first elected record of the first row is 1884 . the first elected record of the second row is 1884 .
and { only { filter_eq { all_rows ; written by ; sang kyu kim } } ; eq { hop { filter_eq { all_rows ; written by ; sang kyu kim } ; title } ; road narrows } } = true
Output: select the rows whose written by record fuzzily matches to sang kyu kim . there is only one such row in the table . the title record of this unqiue row is road narrows .
and { only { filter_eq { all_rows ; surface ; grass } } ; eq { hop { filter_eq { all_rows ; surface ; grass } ; tournament } ; australia f2 , australia } } = true
Output: select the rows whose surface record fuzzily matches to grass . there is only one such row in the table . the tournament record of this unqiue row is australia f2 , australia .
eq { count { filter_eq { all_rows ; venue ; atlanta } } ; 5 } = true
Output: select the rows whose venue record fuzzily matches to atlanta . the number of such rows is 5 .
round_eq { sum { all_rows ; points } ; 97 } = true
Output: the sum of the points record of all rows is 97 .
eq { hop { nth_argmax { all_rows ; year to open ; 1 } ; name } ; izmit bay bridge } = true
Output: select the row whose year to open record of all rows is 1st maximum . the name record of this row is izmit bay bridge .
most_less { all_rows ; total points ; 2000 } = true
Output: for the total points records of all rows , most of them are less than 2000 .
eq { hop { nth_argmax { all_rows ; floors ; 2 } ; name } ; aul tower } = true
Output: select the row whose floors record of all rows is 2nd maximum . the name record of this row is aul tower .
greater { hop { filter_eq { all_rows ; english title ; lives of omission } ; peak } ; hop { filter_eq { all_rows ; english title ; the rippling blossom } ; peak } } = true
Output: select the rows whose english title record fuzzily matches to lives of omission . take the peak record of this row . select the rows whose english title record fuzzily matches to the rippling blossom . take the peak record of this row . the first record is greater than the second record .
only { filter_eq { all_rows ; surface ; grass } } = true
Output: select the rows whose surface record fuzzily matches to grass . there is only one such row in the table .
eq { hop { argmax { all_rows ; distance } ; race } ; cox plate } = true
Output: select the row whose distance record of all rows is maximum . the race record of this row is cox plate .
round_eq { sum { all_rows ; population } ; 22,500 } = true
Output: the sum of the population record of all rows is 22,500 .
eq { hop { nth_argmin { all_rows ; date ; 1 } ; opponent } ; cincinnati bengals } = true
Output: select the row whose date record of all rows is 1st minimum . the opponent record of this row is cincinnati bengals .
most_greater_eq { all_rows ; power rpm ; 6000 } = true
Output: for the power rpm records of all rows , most of them are greater than or equal to 6000 .
eq { count { filter_eq { all_rows ; mlb draft ; pirates } } ; 2 } = true
Output: select the rows whose mlb draft record fuzzily matches to pirates . the number of such rows is 2 .
and { only { filter_eq { all_rows ; flagship station ; wrko } } ; eq { hop { filter_eq { all_rows ; flagship station ; wrko } ; color commentator ( s ) } ; cedric maxwell } } = true
Output: select the rows whose flagship station record fuzzily matches to wrko . there is only one such row in the table . the color commentator  s  record of this unqiue row is cedric maxwell .
round_eq { avg { all_rows ; runs } ; 87.7 } = true
Output: the average of the runs record of all rows is 87.7 .
eq { count { filter_eq { all_rows ; law preservation ticket ; ( none ) } } ; 4 } = true
Output: select the rows whose law preservation ticket record fuzzily matches to  none  . the number of such rows is 4 .
less { hop { filter_eq { all_rows ; venue ; athens , greece } ; date } ; hop { filter_eq { all_rows ; venue ; oslo , norway } ; date } } = true
Output: select the rows whose venue record fuzzily matches to athens , greece . take the date record of this row . select the rows whose venue record fuzzily matches to oslo , norway . take the date record of this row . the first record is less than the second record .
eq { count { filter_greater { filter_eq { all_rows ; location ; groves stadium winston - salem , nc } ; attendance ; 20000 } } ; 3 } = true
Output: select the rows whose location record fuzzily matches to groves stadium winston  salem , nc . among these rows , select the rows whose attendance record is greater than 20000 . the number of such rows is 3 .
eq { count { filter_less { all_rows ; attendance ; 40000 } } ; 4 } = true
Output: select the rows whose attendance record is less than 40000 . the number of such rows is 4 .
most_greater { all_rows ; area ( km square ) ; 40 } = true
Output: for the area  km square  records of all rows , most of them are greater than 40 .
and { only { filter_less { all_rows ; height ; 6-0 } } ; eq { hop { filter_less { all_rows ; height ; 6-0 } ; player } ; myles mack } } = true
Output: select the rows whose height record is less than 60 . there is only one such row in the table . the player record of this unqiue row is myles mack .
most_eq { all_rows ; president ; ray kaduck } = true
Output: for the president records of all rows , most of them fuzzily match to ray kaduck .
and { only { filter_eq { all_rows ; ceased operation ; failed at launch } } ; eq { hop { filter_eq { all_rows ; ceased operation ; failed at launch } ; name } ; injun 2 } } = true
Output: select the rows whose ceased operation record fuzzily matches to failed at launch . there is only one such row in the table . the name record of this unqiue row is injun 2 .
and { only { filter_eq { all_rows ; segment d ; wigs } } ; eq { hop { filter_eq { all_rows ; segment d ; wigs } ; episode } ; 71 } } = true
Output: select the rows whose segment d record fuzzily matches to wigs . there is only one such row in the table . the episode record of this unqiue row is 71 .
less { hop { filter_eq { all_rows ; goalkeeper ; igor etxebarrieta } ; matches } ; hop { filter_eq { all_rows ; goalkeeper ; daniel giménez } ; matches } } = true
Output: select the rows whose goalkeeper record fuzzily matches to igor etxebarrieta . take the matches record of this row . select the rows whose goalkeeper record fuzzily matches to daniel giménez . take the matches record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; time / retired ; engine } } ; eq { hop { filter_eq { all_rows ; time / retired ; engine } ; driver } ; jo bonnier } } = true
Output: select the rows whose time  retired record fuzzily matches to engine . there is only one such row in the table . the driver record of this unqiue row is jo bonnier .
most_eq { all_rows ; quantity preserved ; 0 } = true
Output: for the quantity preserved records of all rows , most of them are equal to 0 .
eq { count { filter_eq { all_rows ; round ; 7 } } ; 2 } = true
Output: select the rows whose round record is equal to 7 . the number of such rows is 2 .
eq { count { filter_eq { all_rows ; country ; united states } } ; 3 } = true
Output: select the rows whose country record fuzzily matches to united states . the number of such rows is 3 .
eq { hop { argmax { all_rows ; length } ; name ( year commissioned ) } ; dampier to bunbury natural gas pipeline ( 1984 ) } = true
Output: select the row whose length record of all rows is maximum . the name  year commissioned  record of this row is dampier to bunbury natural gas pipeline  1984  .
only { filter_eq { all_rows ; date ; florida } } = true
Output: select the rows whose date record fuzzily matches to florida . there is only one such row in the table .
and { only { filter_eq { all_rows ; original air date ( uk ) ; july } } ; eq { hop { filter_eq { all_rows ; original air date ( uk ) ; july } ; episode title } ; night games } } = true
Output: select the rows whose original air date  uk  record fuzzily matches to july . there is only one such row in the table . the episode title record of this unqiue row is night games .
all_eq { all_rows ; coach ; john cartwright } = true
Output: for the coach records of all rows , all of them fuzzily match to john cartwright .
less { hop { filter_eq { all_rows ; name ; franck esposito } ; time } ; hop { filter_eq { all_rows ; name ; andrew livingston } ; time } } = true
Output: select the rows whose name record fuzzily matches to franck esposito . take the time record of this row . select the rows whose name record fuzzily matches to andrew livingston . take the time record of this row . the first record is less than the second record .
and { only { filter_eq { all_rows ; owner ; four roses thoroughbreds llc } } ; eq { hop { filter_eq { all_rows ; owner ; four roses thoroughbreds llc } ; horse } ; anak nakal } } = true
Output: select the rows whose owner record fuzzily matches to four roses thoroughbreds llc . there is only one such row in the table . the horse record of this unqiue row is anak nakal .
eq { count { filter_all { all_rows ; country } } ; 12 } = true
Output: select the rows whose country record is arbitrary . the number of such rows is 12 .
eq { hop { argmax { all_rows ; score } ; team 1 } ; fc nantes ( d1 ) } = true
Output: select the row whose score record of all rows is maximum . the team 1 record of this row is fc nantes  d1  .
eq { hop { nth_argmin { filter_eq { all_rows ; location in park ; baja ridge } ; year first opened ; 1 } ; current name } ; revolution } = true
Output: select the rows whose location in park record fuzzily matches to baja ridge . select the row whose year first opened record of these rows is 1st minimum . the current name record of this row is revolution .
and { eq { hop { filter_eq { all_rows ; chassis ; mclaren } ; year } ; hop { filter_eq { all_rows ; chassis ; mclaren } ; year } } ; and { eq { hop { filter_eq { all_rows ; chassis ; mclaren } ; year } ; 1981 } ; eq { hop { filter_eq { all_rows ; chassis ; mclaren } ; year } ; 1981 } } } = true
Output: select the rows whose chassis record fuzzily matches to mclaren . take the year record of this row . select the rows whose chassis record fuzzily matches to mclaren . take the year record of this row . the first record is equal to the second record . the year record of the first row is 1981 . the year record of the second row is 1981 .
greater { hop { filter_eq { all_rows ; player ; b lee } ; wkts } ; hop { filter_eq { all_rows ; player ; z khan } ; wkts } } = true
Output: select the rows whose player record fuzzily matches to b lee . take the wkts record of this row . select the rows whose player record fuzzily matches to z khan . take the wkts record of this row . the first record is greater than the second record .
less { hop { filter_eq { all_rows ; institution ; webber international university } ; founded } ; hop { filter_eq { all_rows ; institution ; ave maria university } ; founded } } = true
Output: select the rows whose institution record fuzzily matches to webber international university . take the founded record of this row . select the rows whose institution record fuzzily matches to ave maria university . take the founded record of this row . the first record is less than the second record .
round_eq { avg { all_rows ; time } ; 2:40 } = true
Output: the average of the time record of all rows is 2:40 .
eq { count { filter_eq { all_rows ; date ; april } } ; 4 } = true
Output: select the rows whose date record fuzzily matches to april . the number of such rows is 4 .
round_eq { avg { all_rows ; to par } ; -2.19 } = true
Output: the average of the to par record of all rows is 2.19 .
most_eq { all_rows ; country ; united arab emirates } = true
Output: for the country records of all rows , most of them fuzzily match to united arab emirates .
and { only { filter_eq { all_rows ; college ; florida state } } ; eq { hop { filter_eq { all_rows ; college ; florida state } ; name } ; e g green } } = true
Output: select the rows whose college record fuzzily matches to florida state . there is only one such row in the table . the name record of this unqiue row is e g green .
round_eq { avg { all_rows ; height ( m ) } ; 670 } = true
Output: the average of the height  m  record of all rows is 670 .
round_eq { sum { all_rows ; laps } ; 3670 } = true
Output: the sum of the laps record of all rows is 3670 .
eq { hop { nth_argmin { all_rows ; date ; 1 } ; opponent } ; matea mezak } = true
Output: select the row whose date record of all rows is 1st minimum . the opponent record of this row is matea mezak .
round_eq { avg { all_rows ; home team score } ; 14.27 } = true
Output: the average of the home team score record of all rows is 14.27 .
eq { count { filter_eq { filter_eq { all_rows ; sport ; boxing } ; event ; men 's light flyweight } } ; 2 } = true
Output: select the rows whose sport record fuzzily matches to boxing . among these rows , select the rows whose event record fuzzily matches to men s light flyweight . the number of such rows is 2 .
eq { hop { nth_argmin { all_rows ; pick ; 2 } ; player } ; burns mcpherson } = true
Output: select the row whose pick record of all rows is 2nd minimum . the player record of this row is burns mcpherson .
most_less { all_rows ; shot pct ; 80 % } = true
Output: for the shot pct records of all rows , most of them are less than 80  .
and { only { filter_eq { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) ; 44 } } ; eq { hop { filter_eq { filter_eq { all_rows ; state ; jammu & kashmir } ; total capacity ( mw ) ; 44 } ; power plant } ; chutak } } = true
Output: select the rows whose state record fuzzily matches to jammu  kashmir . among these rows , select the rows whose total capacity  mw  record is equal to 44 . there is only one such row in the table . the power plant record of this unqiue row is chutak .
greater { hop { filter_eq { all_rows ; date ; september 8 , 2002 } ; attendance } ; hop { filter_eq { all_rows ; date ; september 15 , 2002 } ; attendance } } = true
Output: select the rows whose date record fuzzily matches to september 8 , 2002 . take the attendance record of this row . select the rows whose date record fuzzily matches to september 15 , 2002 . take the attendance record of this row . the first record is greater than the second record .
round_eq { avg { all_rows ; apparent magnitude } ; 11.7 } = true
Output: the average of the apparent magnitude record of all rows is 11.7 .
eq { count { filter_eq { all_rows ; district ; sagar } } ; 3 } = true
Output: select the rows whose district record fuzzily matches to sagar . the number of such rows is 3 .
eq { diff { hop { filter_eq { all_rows ; player ; rudy harris } ; round } ; hop { filter_eq { all_rows ; player ; john lynch } ; round } } ; 1 } = true
Output: select the rows whose player record fuzzily matches to rudy harris . take the round record of this row . select the rows whose player record fuzzily matches to john lynch . take the round record of this row . the first record is 1 larger than the second record .
eq { hop { argmax { all_rows ; wins } ; year } ; 1995 } = true
Output: select the row whose wins record of all rows is maximum . the year record of this row is 1995 .
and { only { filter_eq { all_rows ; won ; 16 } } ; eq { hop { filter_eq { all_rows ; won ; 16 } ; club } ; northampton saints ( sf ) } } = true
Output: select the rows whose won record is equal to 16 . there is only one such row in the table . the club record of this unqiue row is northampton saints  sf  .
and { only { filter_eq { all_rows ; weight ( kg ) ; 59 } } ; eq { hop { filter_eq { all_rows ; weight ( kg ) ; 59 } ; distance } ; 1000 m } } = true
Output: select the rows whose weight  kg  record is equal to 59 . there is only one such row in the table . the distance record of this unqiue row is 1000 m .
and { only { filter_eq { all_rows ; tournament ; the open championship } } ; eq { hop { filter_eq { all_rows ; tournament ; the open championship } ; wins } ; 1 } } = true
Output: select the rows whose tournament record fuzzily matches to the open championship . there is only one such row in the table . the wins record of this unqiue row is 1 .
round_eq { avg { all_rows ; candidates } ; 58.3 } = true
Output: the average of the candidates record of all rows is 58.3 .
greater { hop { filter_eq { all_rows ; official name ; hampton } ; area km 2 } ; hop { filter_eq { all_rows ; official name ; sussex corner } ; area km 2 } } = true
Output: select the rows whose official name record fuzzily matches to hampton . take the area km 2 record of this row . select the rows whose official name record fuzzily matches to sussex corner . take the area km 2 record of this row . the first record is greater than the second record .
most_less { all_rows ; attendance ; 17000 } = true
Output: for the attendance records of all rows , most of them are less than 17000 .
and { only { filter_eq { all_rows ; points ; 2 } } ; eq { hop { filter_eq { all_rows ; points ; 2 } ; pos } ; 7 } } = true
Output: select the rows whose points record is equal to 2 . there is only one such row in the table . the pos record of this unqiue row is 7 .
and { only { filter_eq { filter_eq { all_rows ; language ; french } ; points ; 14 } } ; eq { hop { filter_eq { filter_eq { all_rows ; language ; french } ; points ; 14 } ; artist } ; tonia } } = true
Output: select the rows whose language record fuzzily matches to french . among these rows , select the rows whose points record is equal to 14 . there is only one such row in the table . the artist record of this unqiue row is tonia .
eq { hop { nth_argmax { all_rows ; crowd ; 2 } ; home team } ; south melbourne } = true
Output: select the row whose crowd record of all rows is 2nd maximum . the home team record of this row is south melbourne .
eq { hop { filter_eq { all_rows ; tournament ; us open } ; cuts made } ; hop { filter_eq { all_rows ; tournament ; pga championship } ; cuts made } } = true
Output: select the rows whose tournament record fuzzily matches to us open . take the cuts made record of this row . select the rows whose tournament record fuzzily matches to pga championship . take the cuts made record of this row . the first record is equal to the second record .
and { eq { min { all_rows ; opened } ; 1877 } ; eq { hop { argmin { all_rows ; opened } ; stadium } ; stamford bridge } } = true
Output: the minimum opened record of all rows is 1877 . the stadium record of the row with superlative opened record is stamford bridge .
and { only { filter_less { all_rows ; crowd ; 10000 } } ; eq { hop { filter_less { all_rows ; crowd ; 10000 } ; venue } ; corio oval } } = true
Output: select the rows whose crowd record is less than 10000 . there is only one such row in the table . the venue record of this unqiue row is corio oval .
eq { hop { nth_argmin { all_rows ; population ; 2 } ; english name } ; liancheng county } = true
Output: select the row whose population record of all rows is 2nd minimum . the english name record of this row is liancheng county .
round_eq { avg { all_rows ; time } ; 3:29 } = true
Output: the average of the time record of all rows is 3:29 .
eq { count { filter_eq { all_rows ; entrant ; martini racing } } ; 4 } = true
Output: select the rows whose entrant record fuzzily matches to martini racing . the number of such rows is 4 .
eq { hop { argmin { all_rows ; time } ; athlete } ; jean - baptiste macquet , adrien hardy } = true
Output: select the row whose time record of all rows is minimum . the athlete record of this row is jean  baptiste macquet , adrien hardy .
most_eq { all_rows ; location ; kabul } = true
Output: for the location records of all rows , most of them fuzzily match to kabul .
and { only { filter_eq { all_rows ; date of appointment ; 3 november 2008 } } ; eq { hop { filter_eq { all_rows ; date of appointment ; 3 november 2008 } ; outgoing manager } ; osman özdemir } } = true
Output: select the rows whose date of appointment record fuzzily matches to 3 november 2008 . there is only one such row in the table . the outgoing manager record of this unqiue row is osman özdemir .
eq { hop { nth_argmax { all_rows ; episodes ; 4 } ; series } ; 1 } = true
Output: select the row whose episodes record of all rows is 4th maximum . the series record of this row is 1 .
eq { count { filter_eq { all_rows ; opponent ; unopposed } } ; 4 } = true
Output: select the rows whose opponent record fuzzily matches to unopposed . the number of such rows is 4 .
eq { count { filter_eq { all_rows ; team ; ducati } } ; 3 } = true
Output: select the rows whose team record fuzzily matches to ducati . the number of such rows is 3 .
round_eq { avg { all_rows ; home team score } ; 15.18 } = true
Output: the average of the home team score record of all rows is 15.18 .
less { hop { filter_eq { all_rows ; dam constructed ; tooma dam } ; year completed } ; hop { filter_eq { all_rows ; dam constructed ; talbingo dam } ; year completed } } = true
Output: select the rows whose dam constructed record fuzzily matches to tooma dam . take the year completed record of this row . select the rows whose dam constructed record fuzzily matches to talbingo dam . take the year completed record of this row . the first record is less than the second record .
eq { hop { argmin { all_rows ; date } ; stadium } ; port macquarie regional stadium } = true
Output: select the row whose date record of all rows is minimum . the stadium record of this row is port macquarie regional stadium .
round_eq { avg { all_rows ; points } ; 58 } = true
Output: the average of the points record of all rows is 58 .
greater { hop { filter_eq { all_rows ; date ; 11 october } ; result } ; hop { filter_eq { all_rows ; date ; 19 october } ; result } } = true
Output: select the rows whose date record fuzzily matches to 11 october . take the result record of this row . select the rows whose date record fuzzily matches to 19 october . take the result record of this row . the first record is greater than the second record .
most_eq { all_rows ; last appearance ; today is life } = true
Output: for the last appearance records of all rows , most of them fuzzily match to today is life .
and { only { filter_eq { all_rows ; directed by ; steve scaini } } ; eq { hop { filter_eq { all_rows ; directed by ; steve scaini } ; } ; 11 } } = true
Output: select the rows whose directed by record fuzzily matches to steve scaini . there is only one such row in the table . the record of this unqiue row is 11 .
and { eq { hop { nth_argmax { all_rows ; gross ; 5 } ; title } ; the color purple } ; eq { hop { nth_argmax { all_rows ; gross ; 5 } ; director } ; steven spielberg } } = true
Output: select the row whose gross record of all rows is 5th maximum . the title record of this row is the color purple . the director record of this row is steven spielberg .
eq { count { filter_greater { all_rows ; average ; 500 } } ; 2 } = true
Output: select the rows whose average record is greater than 500 . the number of such rows is 2 .
and { only { filter_eq { all_rows ; aspect ; 16:9 } } ; eq { hop { filter_eq { all_rows ; aspect ; 16:9 } ; channel } ; 31.1 } } = true
Output: select the rows whose aspect record fuzzily matches to 16:9 . there is only one such row in the table . the channel record of this unqiue row is 31.1 .
round_eq { avg { all_rows ; capacity } ; 9900 } = true
Output: the average of the capacity record of all rows is 9900 .
eq { count { filter_eq { all_rows ; high points ; richard jefferson } } ; 5 } = true
Output: select the rows whose high points record fuzzily matches to richard jefferson . the number of such rows is 5 .
eq { count { filter_greater { filter_less { all_rows ; home team score ; 15 } ; crowd ; 20000 } } ; 3 } = true
Output: select the rows whose home team score record is less than 15 . among these rows , select the rows whose crowd record is greater than 20000 . the number of such rows is 3 .
eq { hop { nth_argmax { all_rows ; loses ; 3 } ; club } ; nevėžis kėdainiai } = true
Output: select the row whose loses record of all rows is 3rd maximum . the club record of this row is nevėžis kėdainiai .
eq { hop { nth_argmin { all_rows ; qual 2 ; 2 } ; name } ; paul tracy } = true
Output: select the row whose qual 2 record of all rows is 2nd minimum . the name record of this row is paul tracy .
eq { count { filter_eq { all_rows ; country ; soviet union } } ; 7 } = true
Output: select the rows whose country record fuzzily matches to soviet union . the number of such rows is 7 .
eq { count { filter_eq { all_rows ; winning driver ; aj foyt } } ; 4 } = true
Output: select the rows whose winning driver record fuzzily matches to aj foyt . the number of such rows is 4 .
eq { count { filter_eq { all_rows ; nationality ; czechoslovakia } } ; 3 } = true
Output: select the rows whose nationality record fuzzily matches to czechoslovakia . the number of such rows is 3 .
most_eq { all_rows ; laps ; 48 } = true
Output: for the laps records of all rows , most of them are equal to 48 .
eq { min { all_rows ; date } ; 13 , 14 , 16 dec 1901 } = true
Output: the minimum date record of all rows is 13 , 14 , 16 dec 1901 .
and { only { filter_eq { all_rows ; opponent in the final ; mark selby } } ; eq { hop { filter_eq { all_rows ; opponent in the final ; mark selby } ; championship } ; uk championship } } = true
Output: select the rows whose opponent in the final record fuzzily matches to mark selby . there is only one such row in the table . the championship record of this unqiue row is uk championship .
all_eq { all_rows ; date ; april } = true
Output: for the date records of all rows , all of them fuzzily match to april .
eq { max { filter_eq { all_rows ; technology ; rmi } ; date } ; september 16 , 2009 } = true
Output: select the rows whose technology record fuzzily matches to rmi . the maximum date record of these rows is september 16 , 2009 .
and { only { filter_eq { all_rows ; 2012 club ; vk primorje rijeka } } ; eq { hop { filter_eq { all_rows ; 2012 club ; vk primorje rijeka } ; name } ; xavier garcía } } = true
Output: select the rows whose 2012 club record fuzzily matches to vk primorje rijeka . there is only one such row in the table . the name record of this unqiue row is xavier garcía .
round_eq { avg { all_rows ; time } ; 2:08.00.0 } = true
Output: the average of the time record of all rows is 2:08.00.0 .
eq { count { filter_eq { all_rows ; nationality ; canada } } ; 2 } = true
Output: select the rows whose nationality record fuzzily matches to canada . the number of such rows is 2 .
eq { count { filter_less { all_rows ; us viewers ( million ) ; 1.0 } } ; 5 } = true
Output: select the rows whose us viewers  million  record is less than 1.0 . the number of such rows is 5 .
eq { count { filter_eq { all_rows ; constructor ; renault } } ; 4 } = true
Output: select the rows whose constructor record fuzzily matches to renault . the number of such rows is 4 .
round_eq { avg { all_rows ; enrollment } ; 1077.4 } = true
Output: the average of the enrollment record of all rows is 1077.4 .
most_less { all_rows ; crowd ; 20000 } = true
Output: for the crowd records of all rows , most of them are less than 20000 .
eq { hop { nth_argmax { all_rows ; original air date ; 1 } ; family / families } ; the haynes family and the potter family } = true
Output: select the row whose original air date record of all rows is 1st maximum . the family  families record of this row is the haynes family and the potter family .
and { eq { nth_min { all_rows ; date ; 2 } ; jun 25 } ; eq { hop { nth_argmin { all_rows ; date ; 2 } ; score } ; 2 - 3 } } = true
Output: the 2nd minimum date record of all rows is jun 25 . the score record of the row with 2nd minimum date record is 2  3 .
and { only { filter_eq { all_rows ; candidates ; unopposed } } ; eq { hop { filter_eq { all_rows ; candidates ; unopposed } ; incumbent } ; tony p hall } } = true
Output: select the rows whose candidates record fuzzily matches to unopposed . there is only one such row in the table . the incumbent record of this unqiue row is tony p hall .
eq { count { filter_greater_eq { all_rows ; year ( ceremony ) ; 2000 } } ; 2 } = true
Output: select the rows whose year  ceremony  record is greater than or equal to 2000 . the number of such rows is 2 .
round_eq { avg { all_rows ; age } ; 10.06 } = true
Output: the average of the age record of all rows is 10.06 .
and { only { filter_eq { all_rows ; location ; kohl center madison , wi } } ; eq { hop { filter_eq { all_rows ; location ; kohl center madison , wi } ; acc team } ; nc state } } = true
Output: select the rows whose location record fuzzily matches to kohl center madison , wi . there is only one such row in the table . the acc team record of this unqiue row is nc state .
and { only { filter_eq { all_rows ; height ft ( m ) ; 504 } } ; eq { hop { filter_eq { all_rows ; height ft ( m ) ; 504 } ; name } ; one indiana square } } = true
Output: select the rows whose height ft  m  record is equal to 504 . there is only one such row in the table . the name record of this unqiue row is one indiana square .
eq { count { filter_eq { all_rows ; frequency ; 450 mhz } } ; 2 } = true
Output: select the rows whose frequency record fuzzily matches to 450 mhz . the number of such rows is 2 .
and { only { filter_eq { all_rows ; winning driver ; archie scott brown } } ; eq { hop { filter_eq { all_rows ; winning driver ; archie scott brown } ; race name } ; i brscc formula 1 race } } = true
Output: select the rows whose winning driver record fuzzily matches to archie scott brown . there is only one such row in the table . the race name record of this unqiue row is i brscc formula 1 race .
round_eq { avg { all_rows ; lost } ; 28.2 } = true
Output: the average of the lost record of all rows is 28.2 .
and { only { filter_eq { all_rows ; result ; lost renomination democratic hold } } ; eq { hop { filter_eq { all_rows ; result ; lost renomination democratic hold } ; incumbent } ; clyde l garrett } } = true
Output: select the rows whose result record fuzzily matches to lost renomination democratic hold . there is only one such row in the table . the incumbent record of this unqiue row is clyde l garrett .
eq { count { filter_eq { filter_less { all_rows ; sets ; 5 } ; event ; wimbledon } } ; 2 } = true
Output: select the rows whose sets record is less than 5 . among these rows , select the rows whose event record fuzzily matches to wimbledon . the number of such rows is 2 .
eq { hop { nth_argmin { all_rows ; first air date ; 2 } ; city } ; cairns } = true
Output: select the row whose first air date record of all rows is 2nd minimum . the city record of this row is cairns .
and { only { filter_eq { all_rows ; mls cup winner ; columbus crew } } ; eq { hop { filter_eq { all_rows ; mls cup winner ; columbus crew } ; season } ; 2008 } } = true
Output: select the rows whose mls cup winner record fuzzily matches to columbus crew . there is only one such row in the table . the season record of this unqiue row is 2008 .
eq { hop { nth_argmin { all_rows ; year ; 1 } ; name } ; franklin exchange building } = true
Output: select the row whose year record of all rows is 1st minimum . the name record of this row is franklin exchange building .
and { only { filter_eq { all_rows ; result ; lost re - election republican gain } } ; eq { hop { filter_eq { all_rows ; result ; lost re - election republican gain } ; district } ; illinois 21 } } = true
Output: select the rows whose result record fuzzily matches to lost re  election republican gain . there is only one such row in the table . the district record of this unqiue row is illinois 21 .
eq { hop { argmax { all_rows ; foundation } ; organization } ; radiotelevisión del principado de asturias ( rtpa ) } = true
Output: select the row whose foundation record of all rows is maximum . the organization record of this row is radiotelevisión del principado de asturias  rtpa  .
most_less { all_rows ; points ; 10 } = true
Output: for the points records of all rows , most of them are less than 10 .
eq { diff { hop { filter_eq { all_rows ; club ; mold rfc } ; won } ; hop { filter_eq { all_rows ; club ; ruthin rfc } ; won } } ; 3 } = true
Output: select the rows whose club record fuzzily matches to mold rfc . take the won record of this row . select the rows whose club record fuzzily matches to ruthin rfc . take the won record of this row . the first record is 3 larger than the second record .
and { only { filter_less { all_rows ; top speed ( km / h ) ; 175 } } ; eq { hop { filter_less { all_rows ; top speed ( km / h ) ; 175 } ; model } ; 1.4 16v } } = true
Output: select the rows whose top speed  km  h  record is less than 175 . there is only one such row in the table . the model record of this unqiue row is 1.4 16v .
eq { hop { nth_argmin { all_rows ; week ; 1 } ; date } ; sun sep 1 } = true
Output: select the row whose week record of all rows is 1st minimum . the date record of this row is sun sep 1 .