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:
| fruit: ribbing      |
|:--------------------|
| absent or very weak |
| weak                |
| absent or weak      |
| very weak to weak   |
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:
| d    |
|:-----|
| i->v |
| d->y |
| s->l |
| e->d |
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:
| atividade                                              |
|:-------------------------------------------------------|
| filme/competição                                       |
| visita técnica                                         |
| curso de curta/média/longa duração                     |
| exposição em outra ies                                 |
| palestra em outra ies                                  |
| congresso/simpósio/colóquio/fórum                      |
| atividade técnico acadêmica voluntária                 |
| trabalho voluntário / ação social                      |
| oficina / mini-curso em outra ies                      |
| teatro                                                 |
| produção científica                                    |
| monitoria voluntária                                   |
| apresentação do trabalho em evento científico cultural |
| grupo de pesquisa                                      |
| monitoria contratual                                   |
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:
| dla przedsiębiorców? tak/nie                                                                                                                                                                                                         |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| nie                                                                                                                                                                                                                                  |
| tak                                                                                                                                                                                                                                  |
| tak udział przedsiębiorców jest możliwy_ o ile są to podmioty działające w celu zaspokajania potrzeb w interesie ogólnym_ nie mające charakteru przemysłowego lub komercyjnego. podmioty te nie mogą pełnić roli partnera wiodącego. |
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 full name                        |
|:---------------------------------------|
| seattle_ wa                            |
| rio de janeiro_ brazil                 |
| austin_ tx                             |
| medford_ or                            |
| brooklyn_ ny                           |
| washington_ dc                         |
| chicago_ il                            |
| los angeles_ ca                        |
| westmont_ il                           |
| garden grove_ ca                       |
| ballwin_ mo                            |
| st honoré boulangerie                  |
| portland_ or                           |
| melbourne_ victoria                    |
| new orleans_ la                        |
| shinjuku-ku_ tokyo                     |
| camas_ wa                              |
| kelowna_ british columbia              |
| plateau-mont-royal_ montréal           |
| la paz_ baja california sur            |
| atlanta_ ga                            |
| sydney_ new south wales                |
| sharjah_ united arab emirates          |
| paris_ france                          |
| canberra_ australian capital territory |
| houston_ tx                            |
| campbell_ ca                           |
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:
|   2021/22 entry interruptible price |
|------------------------------------:|
|                          0.0314288  |
|                          0.0357179  |
|                          0.0393504  |
|                          0.0052542  |
|                          0.0400739  |
|                          0.00420733 |
|                          0.00432791 |
|                          0.00560407 |
|                          0.031628   |
|                          0.00550905 |
|                          0.00561035 |
|                          0.00425603 |
|                          0.0309137  |
|                          0.0302088  |
|                          0.0400291  |
|                          0.0437592  |
|                          0.0759139  |
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:
| m                  |
|:-------------------|
| atnb500dau451l     |
| atnb500dau454b     |
| asn500avua373m     |
| as009fusg          |
| asn500avua374m     |
| asn500avuf573l     |
| asn500avuf574l     |
| atnb500dau456      |
| atnb500ldau456     |
| at520dau2d1b       |
| at520dau2d6b       |
| atn520dau2d1b      |
| atn520dau2d6b      |
| afhxx180701xx      |
| afhxx180702xx      |
| atnb500dau457m     |
| atnb500dau458a     |
| atnb500ldau451l    |
| atnb500ldau454b    |
| atnb500ldau457m    |
| atnb500ldau458a    |
| atn302dau831       |
| atn302dau836       |
| at509huww          |
| at509hubk          |
| ak301fua373m       |
| ak301fua374m       |
| ak301fua377l       |
| ak301fcc1mic3      |
| ak301fcc2min3      |
| ak301fcc3poh3      |
| ak301fcc4pig3      |
| at800hldau481      |
| at800hldacc1iro3   |
| asn509acvuf573l    |
| asn509acvuf567     |
| asn509acvcc13mpri3 |
| asn509acvcc54mcap4 |
| asn509acvcc67airo3 |
| atn800hldau451an   |
| asn509acvuf574l    |
| atn800hldacccap4   |
| at529dau453a       |
| at529dau456        |
| at500hldacc1bla6   |
| at520hldacc1spi4   |
| tn200hf896         |
| asna509acvua373m   |
| asna509acvua374m   |
| asna509acvua377a   |
| x001w6mkybxx       |
| x001w6fsjfxx       |
| x002fe2h3dxx       |
| x002fe2h33xx       |
| x001yaelpbxx       |
| x0022a1e3jxx       |
| x002i1o3zdxx       |
| x002i1vg5nxx       |
| x001v8z1ufxx       |
| x002fe1or3xx       |
| x002fdq87pxx       |
| x0026a3mifxx       |
| x001v8z1upxx       |
| x002ls4e43xx       |
| x002nno261xx       |
| x002nno4gtxx       |
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:
| sub-branch                                 |
|:-------------------------------------------|
| pensions                                   |
| survivor pensions                          |
| early retirement for labour market reasons |
| disability pensions                        |
| unemployment compensations                 |
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:
| tafs_acct                                                                   |
|:----------------------------------------------------------------------------|
| 73-4149 /x - business guaranteed loan financing account                     |
| 73-1154 /16 - business loans program account                                |
| 73-4150 /x - disaster direct loan financing account                         |
| 73-0100 /16 - salaries and expenses                                         |
| 73-1152 /x - disaster loans program account                                 |
| 73-0100 /x - salaries and expenses                                          |
| 73-0400 15/16 - entrepreneurial development programs                        |
| 73-4280 /x - business loan and investment guaranteed loan financing account |
| 73-1156 /16 - business loans program account_ recovery act                  |
| 73-0400 16/17 - entrepreneurial development programs                        |
| 73-4148 /x - business direct loan financing account                         |
| 73-1154 /x - business loans program account                                 |
| 73-4156 /x - surety bond guarantees revolving fund                          |
| 73-0200 /16 - office of inspector general                                   |
| 73-1152 /16 - disaster loans program account                                |
| 73-0300 /x - office of advocacy                                             |
| 73-4154 /x - business loan fund liquidating account                         |
| 73-4279 /x - business loan and investment direct loan financing account     |
| 73-0100 15/16 - salaries and expenses                                       |
| 73-4268 /x - surety bond guarantees revolving fund - recovery act           |
| 73-0400 /16 - entrepreneurial development programs                          |
| 73-0200 /x - office of inspector general                                    |
| 73-4153 /x - disaster loan fund liquidating account                         |
| 73-0100 16/17 - salaries and expenses                                       |
| 73-4293 /x - disaster loans guaranteed loan financing account               |
| 73-8466 /x - business assistance trust fund                                 |
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:
| latitud    |
|:-----------|
| 05 05 48.7 |
| 05 09 31_1 |
| 05 03 23_4 |
| 05 06 8_5  |
| 05 03 7_7  |
| 04 01 30_2 |
| 05 03 1_9  |
| 05 21 24_6 |
| 05 07 32_3 |
| 05 07 0_2  |
| 05 09 0_5  |
| 05 19 12_2 |
| 05 07 7_15 |
| 05 02 00   |
| 04 59 47_9 |
| 05 11 49_8 |
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     |
|:---------|
| friendly |
| league   |
| cup      |
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:
| addressline1     |
|:-----------------|
| 7 reed st        |
| 225 gardiner rd  |
| 7 bird st        |
| 18 maplewood ave |
| 177 alewive rd   |
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             |
|:--------------|
| 399-9879-1-nd |
| 399-3652-nd   |
| 478-5120-nd   |
| 399-4206-nd   |
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:
| intervention category                        |
|:---------------------------------------------|
| automated alerts                             |
| dispensing limits                            |
| information sharing                          |
| laws/policies                                |
| multifaceted intervention                    |
| nsaid substitution (or multimodal analgesia) |
| opioid tapering                              |
| patient education                            |
| pdmp                                         |
| pharmacist intervention                      |
| predictive modeling                          |
| prescription guidelines                      |
| provider education                           |
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:
| glcategory                               |
|:-----------------------------------------|
| z non procurement                        |
| business services                        |
| labour hire                              |
| medical                                  |
| information and communication technology |
| construction                             |
| energy                                   |
| facilities management                    |
| logistics                                |
| vehicles                                 |
| travel                                   |
| materials                                |
| machinery_ plant and equipment           |
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:
| status      |
|:------------|
| closed      |
| in progress |
| open        |
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:
| twitter                             |
|:------------------------------------|
| https://twitter.com/jgaz3           |
| https://twitter.com/yvonnefern      |
| https://twitter.com/gallowaykelly   |
| https://twitter.com/paulsinghward6  |
| https://twitter.com/bilioannidis    |
| https://twitter.com/marsh_ward10    |
| https://twitter.com/redman4region   |
| https://twitter.com/tomjgalloway14  |
| https://twitter.com/waynewettlaufer |
| https://twitter.com/berryonline     |
| https://twitter.com/trustee_ramsay  |
| https://twitter.com/waddell_kw      |
| https://twitter.com/voteamyfee      |
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:
| auction                |
|:-----------------------|
| adesa boston           |
| adesa golden gate      |
| adesa phoenix          |
| adesa washington d. c. |
| greensboro a.a.        |
| iaa hartford           |
| iaa phoenix            |
| kci auto auction       |
| manheim atlanta        |
| manheim hawaii         |
| manheim milwaukee      |
| manheim nashville      |
| manheim new jersey     |
| manheim ohio           |
| manheim orlando        |
| manheim pennsylvania   |
| manheim portland       |
| manheim riverside      |
| manheim seattle        |
| southern a.a.          |
| tra - dallas metro     |
| tra - new jersey       |
| tra - ohio             |
| tra - riverside        |
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                                 |
|:----------------------------------|
| site security                     |
| insurance                         |
| mobilization fees                 |
| demolition costs                  |
| exterior                          |
| site work                         |
| sewer line/septic tank            |
| termite letter                    |
| foundation                        |
| slab                              |
| framing: walls, floors, roof deck |
| sheathing                         |
| roof (shingles)                   |
| windows                           |
| façade                            |
| finish grade                      |
| front porch                       |
| deck/patio                        |
| permit sign off(s)                |
| paint ext                         |
| driveway                          |
| cleanup/trash out/dumpster        |
| garage door(s)                    |
| gutters                           |
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:
| city                |
|:--------------------|
| anaheim             |
| san francisco       |
| fresno              |
| san jose            |
| los angeles         |
| templeton           |
| salinas             |
| portland            |
| eugene              |
| tigard              |
| hillsboro           |
| beaverton           |
| orem                |
| city of commerce    |
| north hollywood     |
| san luis obispo     |
| roseville           |
| irvine              |
| san diego           |
| point reyes station |
| downey              |
| mountain view       |
| bakersfield         |
| sherman oaks        |
| las vegas           |
| north las vegas     |
| topaz lake          |
| springfield         |
| west valley city    |
| fremont             |
| sisters             |
| vernal              |
| escondido           |
| coos bay            |
| salt lake city      |
| redmond             |
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 |
|------------:|
| 0.00193287  |
| 0.00153935  |
| 0.00133102  |
| 0.00204861  |
| 0.00164352  |
| 0.00282407  |
| 0.00125     |
| 0.00291667  |
| 0.0019213   |
| 0.00175926  |
| 0.00289352  |
| 0.0416667   |
| 0.0833333   |
| 0.00200231  |
| 0.00261574  |
| 0.000914352 |
| 0.00145833  |
| 0.125       |
| 0.00230324  |
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                  |
|:----------------------------|
| oral surgery                |
| prosthodontics, removable   |
| periodontics                |
| adjunctive general services |
| endodontics                 |
| restorative                 |
| implant services            |
| preventive                  |
| diagnostic                  |
| prosthodontics, fixed       |
| orthodontics                |
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:
| item id                                |
|:---------------------------------------|
| {23acafcb-2dcc-4fb6-9905-b8e95b4756ac} |
| {8db716df-8a0d-4bba-8ce0-2b27eec0e6cd} |
| {f0458ebf-2283-4a72-93ca-c32c03d72ef4} |
| {59256094-60c9-421c-b696-9b1da4cd4036} |
| {efa4f54c-7c35-4903-9144-ac228a93deb9} |
| {03b37021-bae4-42a2-8bc4-9c6b54ac151d} |
| {8a3247a4-cb0e-40c2-8442-73596470dfd7} |
| {3918d176-3535-47df-8547-714e173a37a0} |
| {9b52be70-b065-4f99-a89e-8b1ab8a83969} |
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:
| geography9   |
|:-------------|
| washington   |
| marion       |
| tillamook    |
| union        |
| malheur      |
| lake         |
| polk         |
| wallowa      |
| multnomah    |
| lane         |
| umatilla     |
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:
|   pmc12_auth_clm_moe |
|---------------------:|
|            0.016384  |
|            0.0376785 |
|            0.023364  |
|            0.0177274 |
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:
| gains benefit rate tables summary for the guaranteed annual income system   |
|:----------------------------------------------------------------------------|
| single pensioners                                                           |
| qualified couples_ per person                                               |
| summary of maximum monthly benefits                                         |
| benefit program                                                             |
| oas - old age security                                                      |
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   |
|:-------------|
| consumer     |
|              |
| retailer     |
| distributor  |
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 replied-to in g3   |
|:-----------------------|
| prem_k                 |
| maureenb2b             |
| brunogebarski          |
| sameerpatel            |
| admv0rl0n              |
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:
| subject             |
|:--------------------|
| science (biology)   |
| science (chemistry) |
| science (physics)   |
| spanish             |
| statistics          |
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:
| definition                                                                                                                                                                                                                           |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| value of retail sales of new building materials and supplies.                                                                                                                                                                        |
| existing single-family home sales based on sample of mls.                                                                                                                                                                            |
| new privately-owned single-family housing starts.                                                                                                                                                                                    |
| repeat-sales index of single-family homes. composite of indices for the nine us census divisions based on observed changes in market value of real estate in 20 defined msas and three price tiers (low, middle, high). jan-2000=100 |
| median sales price of existing single-family homes.                                                                                                                                                                                  |
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:
| groupes thématiques (par regroupement d'objectifs opérationnels du pbe)   |
|:--------------------------------------------------------------------------|
| assainissement collectif_ anc                                             |
| suivi qualité eau & observatoire des pressions                            |
| réduire les pollutions diffuses                                           |
| milieux aquatiques                                                        |
| gouvernance_ animation                                                    |
| quantité & inondation                                                     |
| littoral                                                                  |
| eau potable                                                               |
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:
| practice name   |
|:----------------|
| irrigated       |
| non-irrigated   |
| other           |
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                                                       |
|:--------------------------------------------------------|
| business support organisation                           |
| patient client council                                  |
| ni practice & education council for nursing & midwifery |
| ni social care council                                  |
| regulation & quality improvement authority              |
| ni medical & dental training agency                     |
| ni guardian ad litem agency                             |