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:
| place bounding box                                                                         |
|:-------------------------------------------------------------------------------------------|
| 9.618752_45.655290  9.618752_45.731373  9.713634_45.731373  9.713634_45.655290             |
| -83.200169_39.832013  -82.7713751_39.832013  -82.7713751_40.157354  -83.200169_40.157354   |
| -82.620093_27.821353  -82.2652945_27.821353  -82.2652945_28.171836  -82.620093_28.171836   |
| -8.4811193_52.5812343  -7.6275721_52.5812343  -7.6275721_53.1677498  -8.4811193_53.1677498 |
| 24.782810_60.021032  24.782810_60.297910  25.254436_60.297910  25.254436_60.021032         |
| -2.659936_51.399367  -2.659936_51.516387  -2.510844_51.516387  -2.510844_51.399367         |
| -1.935533_53.696167  -1.935533_53.762979  -1.844528_53.762979  -1.844528_53.696167         |
| 151.283034_-33.550008  151.283034_-33.143286  151.601963_-33.143286  151.601963_-33.550008 |
| 88.013428_20.588879  88.013428_26.629909  92.673668_26.629909  92.673668_20.588879         |
| -7.660857_54.633631  -7.660857_60.845277  -0.740025_60.845277  -0.740025_54.633631         |
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:
| provider spoken - tagalog    |
|:-----------------------------|
| <11                          |
| 3.0000000000000001e-3        |
| 1e-3                         |
| 2e-3                         |
| 7.0000000000000001e-3        |
| 5.0000000000000001e-3        |
| 1.2999999999999999e-2        |
| 2.8000000000000001e-2        |
| 2.1999999999999999e-2        |
| 0.04                         |
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   |
|:----------|
| 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       |
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:
| unvan / title                 |
|:------------------------------|
| yrd. doç. dr. / assist. prof. |
| doç. dr. assoc. prof.         |
| yrd. doç. dr. assist. prof.   |
| prof.dr. assit.prof.          |
| araş. gör.  res. assist.      |
| dr. res assist.               |
| yrd. doç. dr. lecturer        |
| yrd. doç. dr.  assist. prof.  |
| doç. dr.  assoc. prof.        |
| öğr. gör. dr. lecturer        |
| araş. gör. res. assist.       |
| dr.                           |
| prof. dr.                     |
| yrd. doç dr.                  |
| yrd. doç. dr.                 |
| doç. dr.                      |
| doç. dr.  assist. prof.       |
| prof.  dr. assoc. prof.       |
| yrd. doç.  assist. prof.      |
| assist. prof.                 |
| doç. assosc.                  |
| doç.   assist. prof.          |
| doç.  dr. lecturer            |
| uzm. dr.  spec. dr.           |
| yrd. doç. dr. assoc. prof.    |
| araş. gör. dr. res. assist.   |
| doç. dr. assist. prof.        |
| prof. dr. assist. prof.       |
| yrd. doç. dr. assist prof.    |
| profi dr. assist. prof        |
| öğrenim-staj / sms-smp        |
| okutman instructor            |
| sağ.tek.                      |
| öğr. gör .dr.                 |
| prof. dr.assist. prof.        |
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         |
|:----------|
| jazz      |
| rock      |
| classical |
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:
| unidade                               |
|:--------------------------------------|
| centro de desenvolvimento tecnológico |
| centro de engenharias                 |
| centro de integração do mercosul      |
| centro de letras e comunicação        |
| escola superior de educação física    |
| faculdade arquitetura e urbanismo     |
| faculdade de administração e turismo  |
| faculdade de agronomia                |
| faculdade de direito                  |
| faculdade de educação                 |
| faculdade de enfermagem               |
| faculdade de medicina                 |
| faculdade de meteorologia             |
| faculdade de nutrição                 |
| faculdade de odontologia              |
| faculdade de veterinária              |
| instituto de biologia                 |
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:
| source                       |
|:-----------------------------|
| twitter for iphone           |
| twitter for android          |
| mobile web (m2)              |
| ios                          |
| tweetdeck                    |
| twitter web client           |
| hootsuite                    |
| mobile web (m5)              |
| twitter for ipad             |
| twitter for mac              |
| tweetbot for iοs             |
| instagram                    |
| buffer                       |
| feedblitz                    |
| ifttt                        |
| tweetcaster for android      |
| cloudhopper                  |
| echofon                      |
| twitter for windows          |
| google                       |
| wordpress.com                |
| sprout social                |
| dlvr.it                      |
| cheaper with coupons         |
| sprinklr                     |
| placetweet                   |
| life quotes app              |
| facebook                     |
| epicfailbot                  |
| weboutletmall feed           |
| twitterrific                 |
| colorado coupon club         |
| mplus rewards                |
| forthemommas                 |
| spredfast app                |
| tweetpressr                  |
| foursquare                   |
| roundteam                    |
| reshareyyjnow                |
| bitly                        |
| couponsdowork                |
| grocery coupon network       |
| tweetbot for mac             |
| put your button on any page! |
| comparably                   |
| curious cat                  |
| twitteralyssa                |
| tw blue                      |
| localbuzz                    |
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:
| primary area of focus              |
|:-----------------------------------|
| maternal and child health          |
| water and sanitation               |
| basic education and literacy       |
| disease prevention and treatment   |
| economic and community development |
| other                              |
| wheelchairs                        |
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:
| fonction            |
|:--------------------|
| responsable         |
| président           |
| présidente          |
| co-président        |
| secrétaire          |
| secrétaire générale |
| directeur général   |
| secrétaire général  |
| vice présidente     |
| coordinatrice       |
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   |
|:-----------|
| en         |
| und        |
| es         |
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     |
|:------|
| b5402 |
| b5403 |
| b5404 |
| b5405 |
| b5406 |
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:
| single stage dimming / dimmed traffic signals   |
|:------------------------------------------------|
| non dimmed                                      |
| single stage dimming                            |
| non dimmed traffic signals                      |
| dimmed traffic signals                          |
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:
| empresa    |
|:-----------|
| cice       |
| t. c. e.   |
| cargill    |
| tmv        |
| ssa        |
| sepsa      |
| vopak      |
| cpv        |
| excellence |
| apasco     |
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:
| ak   |
|:-----|
| w    |
| t    |
| f    |
| s    |
| m    |
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:
| last        |
|:------------|
| solar-greco |
| kevill      |
| kier        |
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:
|   actual change in rate |
|------------------------:|
|             -0.0317056  |
|              0.00600119 |
|             -0.00769921 |
|              0.0439608  |
|              0.0173143  |
|              0.0110705  |
|              0.0154309  |
|             -0.00869543 |
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:
| contact                                         |
|:------------------------------------------------|
| ms. lisa davey-bates executive director         |
| mr. phil dow executive director                 |
| mr. dan little executive director               |
| mr. matt boyer executive secretary              |
| ms. debbie pedersen executive director          |
| mr. bob perreault executive director            |
| ms. melissa cummins executive director          |
| mr. tim mcsorley_ executive director            |
| mr. richard tippett executive secretary         |
| mr. jon a. clark executive director             |
| mr. scott lanphier executive director           |
| mr. woodrow deloria executive director          |
| dr. mohammad qureshi executive director         |
| mr. daniel b. landon executive director         |
| mr. michael luken executive director            |
| mr. james corless chief executive officer       |
| mr. tim h. beals executive director             |
| joanne marchetta executive director             |
| mr. steve heminger executive director           |
| ms. maura twomey executive director             |
| ms. mary gilbert executive director             |
| mr. ronald l. decarli executive director        |
| ms. marjie kirn executive director              |
| mr. george dondero executive director           |
| ms. debbie hale executive director              |
| mr. tony boren executive director               |
| mr. ahron r. hakimi executive director          |
| ms. terri king executive director               |
| ms.  patricia taylor executive director         |
| mr. ted smalley executive director              |
| mr. clint quilter executive director            |
| mr. gerry le francois executive director        |
| mr. brian peters director community development |
| mr. john gedney executive director              |
| ms. amber collins executive director            |
| mr. mike healy interim_ executive director      |
| mr. patrick pittenger_ executive director       |
| mr. andy t. chesley executive director          |
| ms. rosa park executive director                |
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:
| nome_uf             |
|:--------------------|
| goiás               |
| minas gerais        |
| pará                |
| ceará               |
| bahia               |
| paraná              |
| santa catarina      |
| pernambuco          |
| tocantins           |
| maranhão            |
| rio grande do norte |
| piauí               |
| rio grande do sul   |
| mato grosso         |
| acre                |
| são paulo           |
| espírito santo      |
| paraíba             |
| alagoas             |
| mato grosso do sul  |
| rondônia            |
| roraima             |
| amazonas            |
| amapá               |
| sergipe             |
| rio de janeiro      |
| distrito federal    |
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_of_cells                   |
|:--------------------------------|
| tlymphocytes activated with il2 |
| lymphoblasts                    |
| amniocytes                      |
| fibroblasts                     |
| peripheral blood lymphocytes    |
| pbmc                            |
| myoblasts                       |
| cells                           |
| dna                             |
| tissue                          |
| leukocytes                      |
| whole blood                     |
| thymus                          |
| ipsc                            |
| nervous tissues (brains)        |
| chorionic villi (trophoblast)   |
| blood/dna                       |
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      |
|:-------|
| ü      |
| û      |
| option |
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:
| state   |
|:--------|
| fl      |
| nc      |
| id      |
| va      |
| ca      |
| az      |
| co      |
| md      |
| ri      |
| me      |
| ga      |
| hi      |
| tx      |
| nv      |
| al      |
| mo      |
| ok      |
| germany |
| nm      |
| ms      |
| il      |


label: germany
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         |
|:----------|
| katherine |
| louise    |
| will      |
| robert    |
| matthew   |
| vivienne  |
| claire    |
| debra     |
| steve     |
| julian    |
| alun      |
| andrew    |
| sharon    |
| lindsay   |
| jeff      |
| romana    |
| allister  |
| chris     |
| barry     |
| rhuanydd  |
| karen     |
| gemma     |
| dennie    |
| martin    |
| jayne     |
| rhodri    |
| michelle  |
| juliet    |
| janet     |
| carl      |
| rachel    |
| gareth    |
| mike      |
| joanne    |
| stephane  |
| geraint   |
| heidi     |
| alison    |
| david     |
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:
|   supply and demand / revenue / capacity values - 10% increase |
|---------------------------------------------------------------:|
|                                                         0.0081 |
|                                                         0.0001 |
|                                                         0.0107 |
|                                                         0.008  |
|                                                         0.0115 |
|                                                         0.0021 |
|                                                         0.0125 |
|                                                         0.0112 |
|                                                         0.003  |
|                                                         0.0111 |
|                                                         0.007  |
|                                                         0.0207 |
|                                                         0.0441 |
|                                                         0.0088 |
|                                                         0.012  |
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          |
|:--------------|
| agency        |
| community     |
| caa           |
| roving        |
| corp          |
| church        |
| closed        |
| fsa           |
| my free taxes |
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     |
|:---------------|
| des moines vsc |
| buffalo vsc    |
| boise vsc      |
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:
| extra description       |
|:------------------------|
| 美 国 作品一号副牌-2020 |
| 美 国 作品一号          |
| 智利活靈魂              |
| 活靈魂-1.5 l            |
| 活靈魂                  |
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                                                                                                                                                                                                                                                     |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| apply host-based firewalls or port filtering tools on end systems, with a default-deny rule that drops all traffic except those services and ports that are explicitly allowed.                                                                       |
| perform automated port scans on a regular basis against all key servers and compared to a known effective baseline.  if a change that is not listed on the organization’s approved baseline is discovered, an alert should be generated and reviewed. |
| keep all services up to date and uninstall and remove any unnecessary components from the system.                                                                                                                                                     |
| verify any server that is visible from the internet or an untrusted network, and if it is not required for business purposes, move it to an internal vlan and give it a private address.                                                              |
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    |
|:--------------|
| asfalt        |
| betong        |
| buskage       |
| fogbruk       |
| fötter        |
| gatsten       |
| grus & sand   |
| gräs          |
| kerammaterial |
| löv           |
| metall        |
| naglar        |
| plast         |
| plåt          |
| sand          |
| sly           |
| sten          |
| stål          |
| tandproteser  |
| tegel         |
| trä           |
| tänder        |
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   |
|:----------|
| g3        |
| g1        |
| g2        |
| g22       |
| g4        |
| g5        |
| g6        |
| g7        |
| g8        |
| g9        |
| g10       |
| g11       |
| g12       |
| g13       |
| g14       |
| g15       |
| g16       |
| g17       |
| g18       |
| g19       |
| g20       |
| g21       |
| g23       |
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               |
|:----------------|
| lbs             |
| ton             |
| gallon          |
| 330-gallon tote |
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:
| region    |
|:----------|
| midwest   |
| northeast |
| south     |
| 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:
| category                        |
|:--------------------------------|
| official statistic              |
| national statistic              |
| experimental official statistic |
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:
| current 4 w reported list   |
|:----------------------------|
| q4_2016                     |
| q1_2017                     |
| q2_2018                     |
| q3_2017                     |
| q4_2018                     |
| q3_2018                     |
| q2_2017                     |
| q1_2018                     |
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                                  |
|:-----------------------------------|
| state ppo plan                     |
| state ppo plan (dep)               |
| state hdhp ppo plan                |
| state hdhp ppo plan (dep)          |
| avmed hmo                          |
| avmed hmo (dep)                    |
| avmed hdhp hmo                     |
| avmed hdhp hmo (dep)               |
| united healthcare hmo              |
| united healthcare hmo (dep)        |
| united healthcare hdhp hmo         |
| united hdhp hmo (dep)              |
| capital health plan hmo            |
| capital health plan hmo (dep)      |
| capital health plan hdhp hmo       |
| capital health plan hdhp hmo (dep) |
| aetna health plan hmo              |
| aetna health plan hmo (dep)        |
| aetna hdhp hmo                     |
| aetna hdhp hmo (dep)               |
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:
| mois   |
|:-------|
| dec    |
| mars   |
| mai    |
| juin   |