MMTU-benchmark/MMTU huggingface dataset:
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| day        |
|:-----------|
| m          |
| m t w th f |
| arranged   |
| t th       |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| fuel type            |
|:---------------------|
| wind                 |
| solar                |
|                      |
| coal seam methane    |
| water                |
| natural gas pipeline |
| tba                  |
| wood waste           |
| bagasse              |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
|   homeowner (owned outright) |
|-----------------------------:|
|                   0.0865439  |
|                   0.0292154  |
|                   0.00900859 |
|                   0.029934   |
|                   0.0169207  |
|                   0.00222753 |
|                   0.150146   |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| name                   |
|:-----------------------|
| kade burgess           |
| nicholas grinter       |
| nicholas thorne        |
| michael jorritsma      |
| nicholas dando         |
| josh froggatt          |
| kyle mclean            |
| david bale             |
| daniel guthrie         |
| yuri vdovichenko       |
| anker brodersen        |
| paul quinn             |
| steve walker           |
| david ashton           |
| luke mcgowan           |
| paul hitchman          |
| rene bol               |
| glen stretton          |
| mark slockee           |
| rod bransgrove         |
| brian curtis           |
| greg gourley           |
| peter steele           |
| richard phiillps       |
| peter fisk             |
| adrian davis           |
| troy watkins           |
| andreas falkenau       |
| brian green            |
| bernard robertson-dunn |
| thomas boyce           |
| richard flockhart      |
| jon mcrae              |
| john collis            |
| ian hampton            |
| gary stutsel           |
| david mortlock         |
| max van gelder         |


label: richard phiillps
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| nombre          |
|:----------------|
| neuron bio      |
| npg technology  |
| pangaea onc     |
| robot_ s.a.     |
| secuoya         |
| tecnoquark      |
| think smart     |
| vousse cor      |
| voztelecom      |
| zinkia          |
| 1nkemia i       |
| ab-biotics      |
| agile content   |
| altia consul    |
| atrys           |
| bionaturis      |
| carbures        |
| catenon         |
| cerbium holding |
| clerhp estr.    |
| clever glo.     |
| commcenter      |
| ebioss          |
| elzinc          |
| euroconsult     |
| euroespes       |
| eurona wirel    |
| facephi bio     |
| gigas hosting   |
| greenalia       |
| grenergy        |
| griño           |
| home meal       |
| imaginarium     |
| inclam          |
| let s gowex     |
| lleidanetworks  |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| summary                        |
|:-------------------------------|
| importers by type of product   |
| type of product by importers   |
| country of origin by importer  |
| importers by country of origin |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| snacks                                  |
|:----------------------------------------|
| chocolate_ cheetos                      |
| cheeze-its_ doritos_ cheese & sausage   |
| popcorn                                 |
| almonds                                 |
| popcorn_ veggies_ fruit                 |
| fruit_ veggies                          |
| flavored almonds_ spicy cheetos         |
| reduced fat cheese-its                  |
| cheese popcorn_ almonds                 |
| chips_ crackers_ almonds                |
| any                                     |
| dried fruit_ chips_ nuts                |
| cheeze-its_ cheetos                     |
| nuts_ anything low-carb                 |
| dark chocolate_ chips & guacamole       |
| salty & sweet                           |
| goldfish_ pretzels                      |
| chips_ twizzlers                        |
| almond butter biscuits                  |
| healthy snacks                          |
| trail mix                               |
| pretzels                                |
| sour cream & onion chips                |
| granola bars_ chobani strawberry yogurt |
| cinnamon almonds_ kettlecorn            |
| fruit_ goldfish_ popcorn                |
| dark chocolate_ kind bars               |
| guacamole & veggies                     |
| anything cheesy                         |
| cheetos                                 |
| anything organic/non-gmo                |
| trail mix or nut mixes                  |
| skittles_ pretzels                      |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| miejsce realizacji - województwo   |
|:-----------------------------------|
| dolnośląskie                       |
| kujawsko-pomorskie                 |
| lubelskie                          |
| lubuskie                           |
| łódzkie                            |
| małopolskie                        |
| mazowieckie                        |
| opolskie                           |
| podkarpackie                       |
| podlaskie                          |
| pomorskie                          |
| śląskie                            |
| świętokrzyskie                     |
| warmińsko-mazurskie                |
| wielkopolskie                      |
| zachodniopomorskie                 |
| cały kraj                          |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
|   medical exemptions |
|---------------------:|
|           0.001689   |
|           0.0013413  |
|           0.00141152 |
|           0.00205226 |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| group 1   |
|:----------|
| g2        |
| g3        |
| g4        |
| g5        |
| g6        |
| g7        |
| g8        |
| g9        |
| g10       |
| g11       |
| g12       |
| g13       |
| g14       |
| g15       |
| g16       |
| g17       |
| g18       |
| g19       |
| g20       |
| g21       |
| g22       |
| g23       |
| g24       |
| g25       |
| g26       |
| g27       |
| g28       |
| g29       |
| g30       |
| g31       |
| g32       |
| g33       |
| g34       |
| g35       |
| g36       |
| g37       |
| g38       |
| g39       |
| g40       |
| g41       |
| g42       |
| g43       |
| g44       |
| g45       |
| g46       |
| g47       |
| g48       |
| g49       |
| g50       |
| g51       |
| g52       |
| g53       |
| g54       |
| g55       |
| g56       |
| g57       |
| g58       |
| g59       |
| g60       |
| g61       |
| g62       |
| g63       |
| g64       |
| g65       |
| g66       |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| domains in tweet             |
|:-----------------------------|
| com.au                       |
| co.uk                        |
| therecruitmentexchange.com   |
| expathiring.com              |
| oracle.com                   |
| feedburner.com               |
| oaj.fi                       |
| budget4me.ru                 |
| bullhornreach.com            |
| linkedin.com                 |
| teaminformatics.com          |
| vitrue.com                   |
| edudip.com                   |
| function1.com                |
| ziprecruiter.com             |
| ebayclassifieds.com          |
| sciabarra.com                |
| iwantanewjob.ie              |
| jobmagic.com                 |
| jobsforengineersnow.com      |
| sharedby.co                  |
| donanza.com                  |
| mrwork.nl                    |
| jobviewtrack.com             |
| webcenter.kz                 |
| simplyhired.com              |
| vk.com                       |
| twitterjobsearch.com         |
| zelfstandigprofessionals.nl  |
| google.com mkse.com          |
| aurionprosena.com            |
| ict-services.nl              |
| gov.br                       |
| nationalmuseum.sg            |
| panduit.com                  |
| yfrog.com                    |
| jobsearchpro.com             |
| wix.com                      |
| youtube.com                  |
| catsone.com                  |
| dawsoncareers.com            |
| sapo.pt                      |
| foster.com                   |
| redstonecontentsolutions.com |
| freelancermap.de             |
| jobs96.com                   |
| zillionjobs.com              |
| google.com                   |
| vr-bankmodul.de              |
| pinterest.com                |
| climber.com                  |
| boqnews.com gov.br           |
| jobup.ch                     |
| jobsticker.com               |
| jobsticker.de                |
| verkaufer.net                |
| indeed.com                   |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| referral #   |
|:-------------|
| ge-004-156   |
| ge-005-156   |
| ge-006-156   |
| ge-007-156   |
| ge-008-156   |
| ge-009-156   |
| ge-010-156   |
| ge-011-156   |
| ge-012-156   |
| ge-013-156   |
| ge-014-156   |
| ge-015-156   |
| ge-016-156   |
| ge-017-156   |
| ge-018-156   |
| ge-019-156   |
| ge-020-156   |
| ge-021-156   |
| ge-022-156   |
| ge-023-156   |
| ge-024-156   |
| ge-025-156   |
| ge-026-156   |
| ge-027-156   |
| ge-028-156   |
| ge-029-156   |
| ge-030-156   |
| ge-031-156   |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| ad soyad                |
|:------------------------|
| mustafa karataş         |
| fevzi bilal tezcan      |
| abdüssamed ali bal      |
| emre evcilioğlu         |
| ahmet tetik             |
| derya yildirim          |
| ismail çakir            |
| selin ünsal             |
| tekin tahtaci           |
| mücahit kurtoğlu        |
| emre şengül             |
| kübra düzgün            |
| hasan kuzucu            |
| kemal tekin             |
| ramazan gürbüz          |
| mehmet çerikci          |
| hasan fatih çağlayan    |
| cihan ziyanak           |
| haluk kocalar           |
| ali aydoğan             |
| mustafa cihangir dursun |
| iklim aydindağ          |
| türkuaz destebaşi       |
| adalet erdal            |
| hüseyin doğangüç aydin  |
| süleyman bayindir       |
| aziz aktaş              |
| esen gökay evren        |
| tuğba uysal             |
| gözde dolayman          |
| taha mesut şahin        |
| yusuf alper talha özcan |
| ekrem uzun              |
| gökhan mertol           |
| aziz can kurnaz         |
| seyfi canbay            |
| betül tançaği           |
| asude evcilioğlu        |
| çağatay şahin           |
| emre tuç                |
| fatih ardiç             |
| damla usta              |
| cesim çetin             |
| doğukan kozan           |
| tolga ünal              |
| soner göçer             |
| yasin savaş             |
| tayfun karataş          |
| ahmet biçicioğlu        |
| özgün ertem             |
| mustafa türkmen         |
| taha öztürk             |
| hasan durusoy           |
| taceddin pençereci      |
| emre öztürk             |
| mehmet emin erdemir     |
| embiya kaya             |
| hülya bütüner           |
| fırat aydin             |
| mehmet doğan            |
| cemil şentürk           |
| okan öz                 |
| ibrahim kortan kilinç   |
| oktay öztürk            |
| faruk tağrikulu         |
| volkan hasan üstündağ   |
| ferhan çiloğlu          |
| osman yildiz            |
| ezgi toker              |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| r                     |
|:----------------------|
| -                     |
| 3.7911366389754238e-2 |
| 4.0175496031746034e-2 |
| 3.983238904627006e-2  |
| 3.8213425925925922e-2 |
| 3.8203464614849587e-2 |
| 3.8918065273974914e-2 |
| 3.9696217079632223e-2 |
| 3.8183611884865368e-2 |
| 3.8638786008230452e-2 |
| 3.6125715307582261e-2 |
| 4.0456391242937852e-2 |
| 3.8831627680311891e-2 |
| 3.9800149936868694e-2 |
| 3.9091781874039944e-2 |
| 4.08783960292581e-2   |
| 4.1161916786916782e-2 |
| 3.9820565225035166e-2 |
| 3.9930661577608148e-2 |
| 3.9373561978717289e-2 |
| 4.0630973715651136e-2 |
| 4.2029707895574649e-2 |
| 4.0897358702633273e-2 |
| 4.0368645471104476e-2 |
| 4.0006127450980394e-2 |
| 4.0642336344904099e-2 |
| 3.9084271496165765e-2 |
| 4.0465860742922598e-2 |
| 4.0484843164794004e-2 |
| 4.0227190310414856e-2 |
| 4.1185004340277775e-2 |
| 4.1543494738719468e-2 |
| 4.0620237029805009e-2 |
| 4.1997347987751531e-2 |
| 4.1122185741088176e-2 |
| 4.1018273564569864e-2 |
| 4.0662583019206604e-2 |
| 4.1067631964809385e-2 |
| 4.3477233676975952e-2 |
| 4.089110836536701e-2  |
| 4.3073244234800849e-2 |
| 4.1543030134960253e-2 |
| 4.2021641334730957e-2 |
| 4.1868169398907105e-2 |


label: -
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| c204                  |
|:----------------------|
| 8.0581200000000004e-4 |
| 9.9142999999999996e-4 |
| 3.67697e-4            |
| 5.8110499999999995e-4 |
| ?                     |
| 6.8245859999999997e-3 |
| 7.3608244000000003e-2 |


label: ?
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| resp financeira        |
|:-----------------------|
| ars norte              |
| ars centro             |
| uls matosinhos         |
| uls alto minho         |
| uls nordeste           |
| uls guarda             |
| ars lvt                |
| uls norte alentejano   |
| uls castelo branco     |
| ars alentejo           |
| uls litoral alentejano |
| uls baixo alentejo     |
| ars algarve            |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| l    |
|:-----|
| nvit |
| ssst |
| rdbt |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| b      |
|:-------|
| april  |
| may    |
| june   |
| july   |
| august |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| no of axles   |
|:--------------|
| 4 axles       |
| >7 axles      |
| 6 axles       |
| 3 axles       |
| 7 axles       |
| 5 axles       |
| 2 axles       |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| group 2   |
|:----------|
| g5        |
| g9        |
| g20       |
| g21       |
| g2        |
| g1        |
| g3        |
| g4        |
| g6        |
| g7        |
| g8        |
| g10       |
| g11       |
| g12       |
| g13       |
| g14       |
| g15       |
| g16       |
| g17       |
| g18       |
| g19       |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| Unnamed: 0                 |
|:---------------------------|
| exempt                     |
| individual/sole proprietor |
| llc                        |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
|  i(mi0014) 00 2003 n/rc category code                                  |
|:-----------------------------------------------------------------------|
| 6 - low need                                                           |
| 5 - average need                                                       |
| 3 - high need urban suburban                                           |
| 4 - high need rural                                                    |
| 2 - urban high need                                                    |
| 1 nyc - high need                                                      |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| kriterium                    |
|:-----------------------------|
| arbeitsplan eingehalten      |
| arbeitsplan erstellt         |
| selbstkontrolle durchgeführt |
| auswahl arbeitsmittel        |
| umgang intern                |
| umgang extern                |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| b            |
|:-------------|
| cipc-ca      |
| cipc-ca-enum |
| grap         |
| mfma         |
| pfma         |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| 父件名称   |
|:-----------|
| 产品1      |
| 产品2      |
| 产品3      |
| 部件1      |
| 部件2      |
| 部件3      |
| 组件1      |
| 组件2      |
| 组件3      |
| 零件1      |
| 零件2      |
| 零件3      |
| 零件4      |
| 零件5      |
| 零件6      |
| 零件7      |
| 零件8      |
| 零件9      |
| 零件10     |
| 材料1      |
| 材料2      |
| 材料3      |
| 材料4      |
| 材料5      |
| 材料6      |
| 材料7      |
| 材料8      |
| 材料9      |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| частота использования             |
|:----------------------------------|
| 1. знать наизусть                 |
| 2. иметь представление_ что и как |
| 3. знать о существовании          |
| 4. редкая птица…                  |
| 5. узок круг этих революционеров  |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| full commercial use date   |
|:---------------------------|
| jan 2020                   |
| mar 2020                   |
| feb 2019                   |
| jul 2019                   |
| jun 2020                   |
| aug 2018                   |
| aug 2019                   |
| early 2019_x000d           |
| _dec 2018                  |
| sep 2018                   |
| nov-2019                   |
| nov-2020                   |
| nov-2022                   |
| summer 2018-19_x000d       |
| _early 2019_x000d          |
| _dec 2022                  |
| may 2019                   |
| apr 2019                   |
| oct 2018                   |
| sep 2020                   |
| nov 2019                   |
| mar 2019                   |
| feb 2020                   |
| mar 2021                   |
| sep 2023                   |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| area   |
|:-------|
| north  |
| south  |
| east   |
| west   |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
|            f |
|-------------:|
| -0.00366205  |
| -0.000518001 |
| -0.00653083  |
| -0.000535762 |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| Unnamed: 0   |
|:-------------|
| no           |
| yes*         |
| yes          |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| tipo de oficina   |
|:------------------|
| b                 |
| a                 |
| pendiente         |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| f         |
|:----------|
| uae       |
| malaysia  |
| indonasia |
| kenya     |
| qatar     |
| iraq      |
| russia    |
| canada    |
| oman      |


label: indonasia
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| top word pairs in tweet in g5   |
|:--------------------------------|
| hardrock_songs                  |
| songs_newsong                   |
| newsong_newalbum                |
| newalbum_cdbaby                 |
| van_halen                       |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
|          u |
|-----------:|
| 0.00358887 |
| 0.0389792  |
| 0.0653973  |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

Task: 
You are given a column of data from a table. Your job is to examine each value in the column, and identify if there is any clear data error in the column. An data error in the context of the column, can be values that are misspelled (typos), values that are formatted incorrectly, or values that are semantically incompatible with the rest of the column, etc., such that we can determine without ambiguity that such values are errors in the column. 

Since real-world data can vary widely in meaning and format, if you are uncertain whether a value is an error, please do not mark it as one. Only flag a value if you are confident that it is clearly a data error.

No explanation and return the final answer as a JSON object, {"obvious_error": "<CELL>"}. If you detect no error in the data, return {"obvious_error": null}.

Column:
| Unnamed: 0                                                                             |
|:---------------------------------------------------------------------------------------|
| n. employees with illnesses or open wounds should be prohibited from handling produce. |
| o. employees should have adequate restroom facilities.                                 |
| p. employees should have a separate area for breaks and meals.                         |