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:
| sted2                      |
|:---------------------------|
| 7089 heimdal               |
| 2943 rogne                 |
| 5451 valen                 |
| 3175 ramnes                |
| 5410 sagvåg                |
| 9170 longyearbyen          |
| 7234 ler                   |
| 1930 aurskog               |
| 2162 brårud                |
| 3402 lier                  |
| 5706 voss                  |
| 9600 hammerfest            |
| 7250 melandsjø             |
| 1359 eiksmarka             |
| 7860 skage                 |
| 7660 vuki                  |
| 7609 levanger              |
| 7630 åsen                  |
| 6612 grøa                  |
| 5099 bergen                |
| 6610 øksendal              |
| 5499 åkra                  |
| 9730 karasjok              |
| 7387 singsås               |
| 6421 molde                 |
| 5629 jondal                |
| 6416 molde                 |
| 5419 fitjar                |
| 7802 namsos                |
| 1735 varteig               |
| 1850 mysen                 |
| 7717 steinkjer             |
| 1708 sarpsborg             |
| 5453 utåker                |
| 2966 slidre                |
| fjerdingby                 |
| 5414 stord                 |
| 2450 rena                  |
| 7320 fannrem               |
| 7391 rennebu               |
| 7740 steinsdalen           |
| 6694 foldfjorden           |
| 2584 dalholen              |
| 5382 skogsvåg              |
| 7105 statsbygd             |
| 9060 lyngseidet            |
| 2910 aurdal                |
| 7028 trondheim             |
| 6260 skodje                |
| 8202 bodø                  |
| 7070 bosbert               |
| 5450 sunde i sunnhordaland |
| 2423 østby                 |
| 4130 hjelmeland            |
| 3729 skien                 |
| 4026 stavanger             |
| 8630 storforshei           |
| 6896 fresvik               |
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:
| product   |
|:----------|
| licut     |
| fanon     |
| picin     |
| nedik     |
| tokam     |
| jikut     |
| wakun     |
| janic     |
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:
| 42          |
|:------------|
| viking      |
| wallup      |
| wyalkatchem |
| ventura     |
| sunvale     |
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:
| place id         |
|:-----------------|
| 0073b76548e5984f |
| 44f6ab36310a38db |
| fd70c22040963ac7 |
| 3078869807f9dd36 |
| 10097ef0a6522c1a |
| 894c8c60d80227e7 |
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:
| references                                                   |
|:-------------------------------------------------------------|
| ranta et al._ nat genet; 1999                                |
| ranta et al._ hum mut; 2004                                  |
| ranta et al_ hum. mutat 2004_ kousi et al_ hum. mutat 2012   |
| kousi et al._ 2011                                           |
| cannelli et al._ neurogenetics; 2006                         |
| a. dobbie_ pers comm                                         |
| siintola et al._ biochim biophys acta; 2006                  |
| reinhardt et al._ clin genet; 2010                           |
| kousi et al._ brain_ 2009                                    |
| zelnik et al._ pediatr neurol. 2007;mahajnah and zelnik 2012 |
| helland (pers comm)                                          |
| vantaggiato et al._ hum mut; 2009                            |
| allen et al 2012 clin genet 81:602-4                         |
| a. simonati_ pers comm                                       |
| r. williams pers comm                                        |
| katata et al. brain&dev (2016)                               |
| sahin et al. acta neurol belg (2017)                         |
| i. helland (pers comm)                                       |
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:
| data type     |
|:--------------|
| alpha         |
| alpha-numeric |
| numeric       |
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:
| nadleśnictwo   |
|:---------------|
| czersk         |
| dobrzejowice   |
| golub-dobrzyń  |
| lutówko        |
| miradz         |
| osie           |
| różanna        |
| runowo         |
| rytel          |
| skrwilno       |
| szubin         |
| toruń          |
| tuchola        |
| woziwoda       |
| zamrzenica     |
| żołędowo       |
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:
| 列11           |
|:---------------|
| ２年           |
| １年           |
| 共通キョウツウ |
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:
| place bounding box                                                                             |
|:-----------------------------------------------------------------------------------------------|
| 6.944769_43.5050121  7.0740105_43.5050121  7.0740105_43.5747458  6.944769_43.5747458           |
| 2.2241006_48.8155214  2.4699053_48.8155214  2.4699053_48.9021461  2.2241006_48.9021461         |
| -58.5317922_-34.674453  -58.353494_-34.674453  -58.353494_-34.534177  -58.5317922_-34.534177   |
| -122.514926_37.708075  -122.357031_37.708075  -122.357031_37.833238  -122.514926_37.833238     |
| -66.125093_18.29645  -65.991643_18.29645  -65.991643_18.471881  -66.125093_18.471881           |
| 27.7518557_-26.5126489  28.1843404_-26.5126489  28.1843404_-26.0396281  27.7518557_-26.0396281 |
| -0.213503_51.512805  -0.105303_51.512805  -0.105303_51.572068  -0.213503_51.572068             |
| -0.6329393_51.283513  -0.4647322_51.283513  -0.4647322_51.349531  -0.6329393_51.349531         |
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:
| c                |
|:-----------------|
| edward gordon    |
| quinton kotze    |
| loumari grobler  |
| peter de la rey  |
| jerry lombard    |
| keith moonsamy   |
| jacques claasens |
| wouter wagner    |
| morne maritz     |
| johnny maritz    |
| anthony cunning  |
| jossie mcdonald  |
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:
| symbol   |
|:---------|
| dhd      |
| dhdtr    |
| dwc      |
| dwctr    |
| dwe      |
| dwetr    |
| dwj      |
| dwjtr    |
| dwo      |
| dwotr    |
| dwr      |
| dwrtr    |
| dwu      |
| dwutr    |
| dwy      |
| dwytr    |
| dyh      |
| dyhtr    |
| dyi      |
| dyintr   |
| dyitr    |
| dyo      |
| dyotr    |
| dzc      |
| dzctr    |
| dze      |
| dzetr    |
| dzf      |
| dzftr    |
| dzh      |
| dzhtr    |
| dzl      |
| dzltr    |
| dzm      |
| dzmtr    |
| dzn      |
| dzntr    |
| dzo      |
| dzotr    |
| dzr      |
| dzrtr    |
| ezb      |
| ezbtr    |
| ezf      |
| ezftr    |
| ezk      |
| ezktr    |
| ezl      |
| ezltr    |
| ezs      |
| ezstr    |
| ezv      |
| ezvtr    |
| ezx      |
| ezxtr    |
| ezz      |
| ezztr    |
| ieb      |
| iebtr    |
| iek      |
| iektr    |
| iey      |
| ieytr    |
| ilh      |
| ilhtr    |
| ilj      |
| iljtr    |
| ilk      |
| ilktr    |
| ilp      |
| ilptr    |
| ilr      |
| ilrtr    |
| ilu      |
| ilutr    |
| ilw      |
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:
| given name   |
|:-------------|
| boyd         |
| simpson      |
| rivard       |
| atkins       |
| baldoni      |
| lemay        |
| zielonka     |
| kryworuchko  |
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 hashtags in tweet in entire graph   |
|:----------------------------------------|
| moms                                    |
| babies                                  |
| cesarean                                |
| dads                                    |
| pregnancy                               |
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:
| alumne                   |
|:-------------------------|
| 3. dr wayne w. dyer      |
| 4. thich nhat hanh       |
| 5. deepak chopra         |
| 6. louise l. hay         |
| 7. paolo coelho          |
| 8. oprah winfrey         |
| 9. ken wilber            |
| 10. rhonda byrne         |
| 11. james redfield       |
| 12. neale donald walsch  |
| 13. doreen virtue        |
| 14. alejandro jodorowsky |
| 15. richard bach         |
| 16. alex grey            |
| 17. byron katie          |
| 18. masaru emoto         |
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                          |
|:---------------------------|
| does not have a sixth form |
| has a sixth form           |
| not applicable             |
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                               |
|:-----------------------------------------|
| associate of arts                        |
| associate of science                     |
| bachelor of arts                         |
| bachelor of nursing                      |
| bachelor of science                      |
| bachelor of science in nursing           |
| certificate                              |
| certified adult nurse practitioner       |
| certified medical assistant              |
| certified nurse practitioner             |
| certified nurse midwife                  |
| certified nurse’s assistant              |
| certified registered nurse               |
| certified nurse specialist               |
| certified pediatric nurse practitioner   |
| diploma                                  |
| doctor of philosophy                     |
| doctor of medicine                       |
| doctor of osteopathy                     |
| emergency medical technician             |
| emergency medical technician – paramedic |
| family practice nurse practitioner       |
| high school graduate                     |
| juris doctor                             |
| licensed practical nurse                 |
| master of arts                           |
| master of business administration        |
| master of public health                  |
| master of science                        |
| master of science – nursing              |
| medical assistant                        |
| medical technician                       |
| non-graduate                             |
| nurse practitioner                       |
| doctor of pharmacy                       |
| physician assistant                      |
| public health nurse                      |
| registered medical assistant             |
| registered nurse                         |
| registered pharmacist                    |
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:
| pesticide(s)involved                |
|:------------------------------------|
| nitroxynil                          |
| not released                        |
| alphachloralose                     |
| bromadiolone                        |
| brodifacoum                         |
| bromadiolone difenacoum metaldehyde |
| bendiocarb                          |
| aldicarb                            |
| carbofuran                          |
| metaldehyde                         |
| fenthion                            |
| difenacoum                          |
| strychnine                          |
| diazinon                            |
| phorate                             |
| permethrin                          |
| alphachloralose bendiocarb          |
| dimethoate prochloraz tebuconazole  |
| fipronil                            |
| cypermethrin-alpha tetramethrin     |
| coumatetralyl                       |
| bendiocarb imidacloprid permethrin  |
| bromadiolone brodifacoum            |
| brodifacoum bromadiolone difenacoum |
| difethialone                        |
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:
| language                 |
|:-------------------------|
| english                  |
| german/english           |
| portugese                |
| spanish                  |
| danish/english           |
| french                   |
| english/irish            |
| english/japanese         |
| dutch (ba)/english (ma)  |
| norwegian/english        |
| portuguese/english       |
| spanish/english          |
| spanish/catalan          |
| french (ba)/english (ma) |


label: portugese
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 g7   |
|:--------------------------------|
| centricity_impacts              |
| impacts_marketing               |
| marketing_organization          |
| organization_bigdata            |
| analytics_kimwhitler            |
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:
| c                                        |
|:-----------------------------------------|
| allahabad bank_new_661                   |
| bank of india_new_649                    |
| canara bank                              |
| csc e- gov. services india ltd.-206      |
| dena bank                                |
| dena bank_new_664                        |
| doit (108)                               |
| govt. of maharashtra                     |
| indian bank_new_651                      |
| karur vysya bank ###n###(640)###n###     |
| mpsedc (820)                             |
| mpsedc###n###(820) ###n###               |
| nsdl e-gov. infrastructure ltd.          |
| odisha computer application centre       |
| oriental bank of commerce (652)          |
| punjab and sind bank                     |
| punjab national bank_new_653             |
| state bank of india (654)                |
| syndicate bank                           |
| u p electronics corporation limited      |
| u.p. development systems corporation ltd |
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:
| a.k.a.             |
|:-------------------|
| light sour         |
| medium-medium sour |
| medium sour        |
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:
|         e |
|----------:|
| 0.0215731 |
| 0.0433243 |
| 0.0254367 |
| 0.0363829 |
| 0.043115  |
| 0.024041  |
| 0.0294838 |
| 0.0369822 |
| 0.0330249 |
| 0.0566535 |
| 0.0265977 |
| 0.0540264 |
| 0.060147  |
| 0.0343098 |
| 0.0424191 |
| 0.0301986 |
| 0.0689375 |
| 0.0488732 |
| 0.0428807 |
| 0.0261421 |
| 0.0223011 |
| 0.0292468 |
| 0.0259808 |
| 0.0278771 |
| 0.0698738 |
| 0.0301684 |
| 0.0322765 |
| 0.0250713 |
| 0.0213859 |
| 0.0427713 |
| 0.0248183 |
| 0.0402303 |
| 0.0279321 |
| 0.0565774 |
| 0.0244348 |
| 0.024967  |
| 0.0756388 |
| 0.0228332 |
| 0.0446125 |
| 0.0728485 |
| 0.0250382 |
| 0.029353  |
| 0.0626678 |
| 0.0317741 |
| 0.0266675 |
| 0.0252782 |
| 0.0327608 |
| 0.0244043 |
| 0.0292276 |
| 0.0612162 |
| 0.049562  |
| 0.0248795 |
| 0.0310048 |
| 0.0450065 |
| 0.0221729 |
| 0.0297372 |
| 0.0581733 |
| 0.026447  |
| 0.0329999 |
| 0.0327175 |
| 0.029976  |
| 0.0366631 |
| 0.0502008 |
| 0.0397436 |
| 0.0268613 |
| 0.0334324 |
| 0.0525599 |
| 0.0584855 |
| 0.0250977 |
| 0.0418395 |
| 0.0348648 |
| 0.0245538 |
| 0.0707248 |
| 0.0236242 |
| 0.0411439 |
| 0.0253269 |
| 0.0316629 |
| 0.0254134 |
| 0.0638657 |
| 0.0460621 |
| 0.0243159 |
| 0.0634962 |
| 0.0322406 |
| 0.0209943 |
| 0.022063  |
| 0.033101  |
| 0.0471592 |
| 0.049109  |
| 0.0279687 |
| 0.0503775 |
| 0.0408163 |
| 0.0469363 |
| 0.0678965 |
| 0.0245581 |
| 0.0269732 |
| 0.0416942 |
| 0.0359096 |
| 0.0293245 |
| 0.0695322 |
| 0.0435767 |
| 0.0355923 |
| 0.0601954 |
| 0.0348605 |
| 0.0463103 |
| 0.0413461 |
| 0.0314848 |
| 0.0180874 |
| 0.0354318 |
| 0.0429731 |
| 0.0454545 |
| 0.0550183 |
| 0.0438493 |
| 0.0413011 |
| 0.0415267 |
| 0.0351778 |
| 0.031068  |
| 0.0289931 |
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:
| agency type   |
|:--------------|
| task force    |
| local         |
| state         |
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 procedimiento                        |
|:---------------------------------------------|
| comparación de precios de obras              |
| comparación de precios de bienes y servicios |
| compras menores                              |
| compras por debajo del umbral                |
| suplidor único                               |
| licitación pública nacional                  |
| suplidor especializado                       |
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:
| data_type   |
|:------------|
| character   |
| numeric     |
| integer     |
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:
| 3 standard deviation   |
|:-----------------------|
| not significant        |
| good practice          |
| alarm                  |
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:
| vehicle grade   |
|:----------------|
| grade 4.9       |
| grade 4.6       |
| grade 4.2       |
| grade 4.1       |
| grade 4.4       |
| grade 4.3       |
| grade 3.7       |
| grade 4.5       |
| grade 3.6       |
| grade 4.0       |
| grade 3.9       |
| grade 3.8       |
| grade 3.3       |
| grade 3.5       |
| grade 3.0       |
| grade 3.1       |
| grade 2.7       |
| grade 2.9       |
| grade 2.6       |
| grade 2.3       |
| grade 2.1       |
| grade 1.8       |
| grade 2.8       |
| grade 1.5       |
| grade 2.5       |
| grade 2.2       |
| grade 3.2       |
| grade 2.4       |
| grade 1.6       |
| grade 4.7       |
| grade 5.0       |
| grade 1.3       |
| grade 1.9       |
| grade 1.7       |
| grade 3.4       |
| grade 0.0       |
| grade 4.8       |
| grade 1.2       |
| grade 1.0       |
| grade 2.0       |
| grade 1.4       |
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                                        |
|:-----------------------------------------|
| buyer portal                             |
| need identification                      |
| request through pay                      |
| catalog capability                       |
| sourcing/bid management                  |
| contract management                      |
| vendor performance                       |
| purchasing/data analytics                |
| integration to finance system            |
| <other item for vendor input (optional)> |
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:
| type_const       |
|:-----------------|
| semi-dur         |
| composite        |
| traditionnel     |
| dur              |
| bois             |
| hangar en paille |
| hangar en toles  |
| bache            |
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:
| prenom      |
|:------------|
| lucas       |
| teddy       |
| loic        |
| franck      |
| sylvie      |
| jean michel |
| mael        |
| christelle  |
| michel      |
| nathan      |
| cyril       |
| matea       |
| kenny       |
| ludovic     |
| beatrice    |
| pascal      |
| tomas       |
| remi        |
| jean-pierre |
| florent     |
| manon       |
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:
| a                                               |
|:------------------------------------------------|
| i have been furloughed                          |
| temporary closure of own business               |
| permanent closure of own business               |
| redundancy                                      |
| asked to take leave                             |
| unable to take leave                            |
| working long hours with reduced breaks          |
| finding working from home difficult             |
| i am worried about my health and safety at work |
| asked to work from home                         |
| i have to work around childcare                 |
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:
|         k |
|----------:|
| 0.0333333 |
| 0.0340278 |
| 0.0347222 |
| 0.0354167 |
| 0.0361111 |
| 0.0368056 |
| 0.0375    |
| 0.0381944 |
| 0.0388889 |
| 0.0395833 |
| 0.0402778 |
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:
| stem        |
|:------------|
| tied        |
| steadfast   |
| degenerate  |
| silly       |
| tiny        |
| electrum    |
| rainy       |
| great       |
| polite      |
| faithful    |
| stringy     |
| thrifty     |
| wizened     |
| plentiful   |
| quiet       |
| pompous     |
| turgid      |
| fresh       |
| flaming     |
| disgruntled |
| severed     |
| white       |
| slimy       |
| reliable    |
| chromatic   |
| lame        |
| confident   |
| thirsty     |
| deaf        |
| sinewy      |
| ignorant    |
| meaty       |
| clumsy      |
| silver      |
| graceful    |
| hailing     |
| pious       |
| tasty       |
| moist       |
| oily        |
| miserly     |
| lean        |
| dusty       |
| terrible    |
| sleepy      |
| cheap       |
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   |
|:-------------|
| g.c.01       |
| g.c.02       |
| g.c.03       |
| g.e.01       |
| g.r.07       |
| g.r.01       |
| g.h.01       |
| g.bm.13      |