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:
| groupname                           |
|:------------------------------------|
| arlington afg                       |
| arlington afg alateen               |
| que empiece por mi gfa              |
| south arlington afg                 |
| azle afg                            |
| serenity seekers afg                |
| burleson new beginning afg  alateen |
| burleson new beginnings afg         |
| olde towne afg                      |
| cleburne unity afg                  |
| circle of hope afg                  |
| heart to heart afg                  |
| neighbor to neighbor afg            |
| new beginnings decatur afg          |
| step-by-step afg                    |
| anchor afg                          |
| camino a la felicidad gfa           |
| downtown afg                        |
| fe y recuperacion gfa               |
| dar y recibir alateen group         |
| friday morning afg                  |
| glass house afg                     |
| grupo serenidad gfa                 |
| hope afg                            |
| legacy afg                          |
| one day at a time afg               |
| pathway afg                         |
| river crest afg                     |
| stepping stones afg                 |
| acton 12 step afg                   |
| the promises afg                    |
| choices afg                         |
| grapevine unity afg                 |
| hillsboro afg                       |
| the last stop afg                   |
| keller afg                          |
| focus on solutions afg              |
| others like me afg                  |
| northeast afg                       |
| saginaw afg                         |
| southlake afg                       |
| cross timbers afg                   |
| serenity afg                        |
| our little room afg                 |
| lake whitney afg                    |
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:
| storage and/or  capture   |
|:--------------------------|
| capture                   |
| capture and storage       |
| storage                   |
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:
| tuesday             |
|:--------------------|
| quesadilla          |
| southwest corn      |
| pears               |
| waffle              |
| chicken fried steak |
| with cream gravy    |
| mashed potato       |
| roll                |
| pineapple           |
| omelet-sausage      |
| hamburger on a bun  |
| baked beans         |
| tomato-onion-pickle |
| tropical fruit      |
| biscuit and gravy   |
| chicken and noodles |
| mixed fruit         |
| breakfast pizza     |
| chicken soft taco   |
| pinto beans         |
| hot sauce           |
| peaches             |
| lunch               |
| salad and fruit bar |
| choice of milk      |
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:
| servicio                                                   |
|:-----------------------------------------------------------|
| direccion general                                          |
| enlace y cooperacion institucional                         |
| comite de insumos y servicios externos                     |
| subdireccion general administrativa                        |
| coordinacion general de adquisiciones                      |
| coordinacion general de finanzas                           |
| coordinacion general de informatica                        |
| coordinacion general de recursos humanos                   |
| coordinacion general de servicios generales                |
| coordinacion general de gestion y procuracion de recursos  |
| coordinacion general de mejora regulatoria y transparencia |
| subdireccion general de enseñanza e investigacion          |
| departamento de capacitacion                               |
| coordinacion de asuntos juridicos                          |
| comunicacion social                                        |
| relaciones publicas                                        |
| coordinacion de planeacion y desarrollo                    |
| contraloria                                                |
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       |
|:-----------|
| section    |
| field      |
| title      |
| subsection |
| subtitle   |
| item       |
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:
| section                           |
|:----------------------------------|
| change in obligated balance       |
| budget authority and outlays_ net |
| budgetary resources               |
| status of budgetary resources     |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| f        |
|:---------|
| ngp05162 |
| ngp05241 |
| ngp05211 |
| ngp02131 |
| ngp03151 |
| ngp01141 |
| ngp03232 |
| ngp05111 |
| ngp01121 |
| ngp03181 |
| ngp03211 |
| ngp01241 |
| ngp03171 |
| ngp05242 |
| ngp05222 |
| ngp05271 |
| ngp05232 |
| ngp01211 |
| ngp05281 |
| ngp01131 |
| ngp02141 |
| ngp05261 |
| ngp05272 |
| ngp02151 |
| ngp03161 |
| ngp05122 |
| ngp03221 |
| ngp01111 |
| ngp03264 |
| ngp05221 |
| ngp03131 |
| ngp02111 |
| ngp03121 |
| ngp05212 |
| ngp01221 |
| ngp03261 |
| ngp01213 |
| ngp05142 |
| ngp05151 |
| ngp03212 |
| ngp05132 |
| ngp03141 |
| ngp03111 |
| ngp02121 |
| ngp02181 |
| ngp05163 |
| ngp05152 |
| ngp03262 |
| ngp05172 |
| ngp05133 |
| ngp02243 |
| ngpnwg   |


label: ngpnwg
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 entire graph   |
|:---------------------------------|
| phampants                        |
| heydayfootwear                   |
| bradbogus                        |
| atomiusa                         |
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                |
|:-----------------|
| hsa_circ_0132250 |
| hsa_circ_0001445 |
| hsa_circ_0112637 |
| hsa_circ_0035796 |
| hsa_circ_0099634 |
| hsa_circ_0131202 |
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:
| id elemento                            |
|:---------------------------------------|
| {5e6ae036-76f8-4971-87bf-e865a8832d09} |
| {1472bf8f-ea7f-4bb6-8d4b-312c8f724a76} |
| {891f7466-6a42-44a0-8aa5-df4a4a9ddb22} |
| {e00bb0d1-aaf2-4b38-9b57-77cb931ff0ac} |
| {dda12484-82a1-407d-9815-15937c7dc1c0} |
| {d558160f-b5d6-4edd-9fb1-73388848973f} |
| {a80c133d-1e9c-4c88-a40a-df700bbee724} |
| {1708dedc-4b5f-40bb-ba87-4990695dbbdd} |
| {0e6a8e0b-28e8-4af9-a60f-412a19de3e67} |
| {077a3789-b650-4f49-a743-8e9e74913482} |
| {57d101b2-a785-406e-bea9-dad46004a4b4} |
| {1bac2d1f-b1af-4336-a268-29944cfee354} |
| {71e1626c-2529-4057-902d-909fab3c8524} |
| {0fdd8c80-5b73-4e25-bc60-d7d699e9ac16} |
| {7d7c3311-b383-4921-863f-8b1bda037255} |
| {117e2806-97f0-4ea7-b696-47e05c69d2da} |
| {fa381ae1-0903-4165-98b1-48eb368707dc} |
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:
| businesstype                          |
|:--------------------------------------|
| hospitals/childcare/caring premises   |
| restaurant/cafe/canteen               |
| pub/bar/nightclub                     |
| takeaway/sandwich shop                |
| retailers - other                     |
| distributors/transporters             |
| farmers/growers                       |
| manufacturers/packers                 |
| hotel/bed & breakfast/guest house     |
| school/college/university             |
| mobile caterer                        |
| other catering premises               |
| retailers - supermarkets/hypermarkets |
| importers/exporters                   |
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:
| 4 frazionista        |
|:---------------------|
| mathevon valerie     |
| rita piero           |
| mela malika          |
| citernesi lorenzo    |
| andreoni alberto     |
| trombi stefania      |
| marroncini simone    |
| tarchiani alessandro |
| valenti valentino    |
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             |
|:-----------------------|
| personal entertainment |
| personal goods         |
| restaurants            |
| child care             |
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         |
| ar         |
| es         |
| fr         |
| no         |
| ja         |
| de         |
| it         |
| und        |
| pt         |
| ru         |
| tl         |
| pl         |
| zh         |
| el         |
| ro         |
| et         |
| ko         |
| in         |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| product    |
|:-----------|
| ded-987-9  |
| hht-700-3  |
| hed-800-14 |
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:
| hr                                                     |
|:-------------------------------------------------------|
| djelomično                                             |
| nedostaje                                              |
| nije primjenjivo                                       |
| nije dostupno                                          |
| konsolidirana                                          |
| pojedinačna                                            |
| potkonsolidirana                                       |
| identifikacijska oznaka polja                          |
| polje                                                  |
| format (maksimalni broj znakova)                       |
| vrijednost                                             |
| poveznica na definicije i upute koje treba primijeniti |
| tekst                                                  |
| alfanumerički                                          |
| broj                                                   |
| prazno (ako nije primjenjivo)                          |
| iznos                                                  |
| format                                                 |
| radni list                                             |
| definicije                                             |
| upute                                                  |
| polje koje treba popuniti institucija? (da/ne)         |
| upozorenje                                             |
| prilog                                                 |
| broj obrasca                                           |
| oznaka obrasca                                         |
| id                                                     |
| stupac                                                 |
| redak                                                  |
| #                                                      |
| razdoblje doprinosa                                    |
| dostava podataka                                       |
| id pravila                                             |
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:
| j           |
|:------------|
| lsa47.2vs2  |
| lsa47.2s4   |
| lsa47.2s5   |
| lsa47.2m8   |
| lsa49.1s4   |
| lsa49.1m7   |
| lsa49.1l9   |
| lsa49.1l11  |
| lsa50.2m6   |
| lsa50.2l7   |
| lsa50.2vl10 |
| ls641s55    |
| ls641l70    |
| ls641vl90   |
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:
| error code   |
|:-------------|
| e1.003       |
| e2.001       |
| e2.002       |
| e2.003       |
| e3.001       |
| e3.002       |
| e3.003       |
| e3.004       |
| e4.001       |
| e4.002       |
| w5.001       |
| w5.002       |
| e6.001       |
| e6.002       |
| e6.003       |
| e7.001       |
| e7.002       |
| e7.003       |
| e8.001       |
| e8.002       |
| w8.003       |
| e9.001       |
| e9.002       |
| e10.001      |
| e10.002      |
| e11.001      |
| e11.002      |
| e12.001      |
| e12.002      |
| e13.001      |
| e13.002      |
| e14.001      |
| e14.002      |
| e15.001      |
| e15.002      |
| w16.001      |
| w16.002      |
| w16.003      |
| w17.001      |
| w17.002      |
| w17.003      |
| w18.001      |
| w18.002      |
| w18.003      |
| w19.001      |
| w19.002      |
| w19.003      |
| w20.001      |
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:
| unit title                                        |
|:--------------------------------------------------|
| numeracy skills                                   |
| digital literacy                                  |
| entrepreneurial skills                            |
| employability skills                              |
| environmental literacy                            |
| occupational safety and health practices          |
| horticultural nursery structure setting up        |
| mother block establishment                        |
| nursery planting media preparation                |
| potted horticultural seedlings production         |
| grafted/budded horticultural seedlings production |
| vegetable seedlings production                    |
| tissue culture seedlings hardening                |
| horticultural nursery operations supervision      |
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   |
|:----------|
| g4        |
| g5        |
| g6        |
| g7        |
| g10       |
| g11       |
| g12       |
| g17       |
| g21       |
| g1        |
| g2        |
| g8        |
| g3        |
| g9        |
| g13       |
| g14       |
| g15       |
| g16       |
| g18       |
| g19       |
| g20       |
| g22       |
| g23       |
| g24       |
| g25       |
| g26       |
| g27       |
| g28       |
| g29       |
| g30       |
| g31       |
| g32       |
| g33       |
| g34       |
| g35       |
| g36       |
| g37       |
| g38       |
| g39       |
| g40       |
| g41       |
| g42       |
| g43       |
| g44       |
| g45       |
| g46       |
| g47       |
| g48       |
| g49       |
| g50       |
| g51       |
| g52       |
| g53       |
| g54       |
| g55       |
| g56       |
| g57       |
| g58       |
| g59       |
| g60       |
| g61       |
| g62       |
| g63       |
| g64       |
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 android |
| ifttt               |
| paper.li            |
| twitter for iphone  |
| twitter web client  |
| mobile web (m5)     |
| tweetdeck           |
| mlbot1              |
| flipboard           |
| buffer              |
| hootsuite           |
| mlbot2              |
| mailchimp           |
| mlbot4              |
| roundteam           |
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               |
|:----------------|
| oocl yokohama   |
| cosco singapore |
| oocl brisbane   |
| cosco antwerp   |
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           |
|:---------------------|
| corbran, rose        |
| crist, logan         |
| daughenbaugh, jim    |
| daughenbaugh, kaden  |
| delong, jim          |
| delong, matt         |
| ecker, steve         |
| foster, rich         |
| gens, jeff           |
| kahle, ethan         |
| kahle, jim           |
| kingsley, dave       |
| knopp, kenton        |
| lucas, mason         |
| martin, jerry        |
| morgan, al           |
| nagurney, greg       |
| nelson, owen         |
| nicklas, kevin       |
| peterson, jeff       |
| rater, rex           |
| ruch, dakota         |
| ruch, deven          |
| rumsey, cliff        |
| rumsey, derek        |
| south, trevor        |
| spoon, dennis        |
| studley, luke        |
| sturzenbecker, kevin |
| summerford, shylee   |
| turfitt, bill        |
| tutmaher, matt       |
| tutmaher, stephanie  |
| tutmaher, sydnie     |
| tutmaher, tom        |
| wassman, greg        |
| whalen, bonnie       |
| willett, jerry       |
| wingard, russ        |
| young, tim           |
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                  |
|:-------------------|
| 3rd district …………… |
| 4th district …………… |
| 5th district …………… |
| 6th district …………… |
| 7th district …………… |
| 8th district …………… |
| 9th district …………… |
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        |
|:---------|
| gold     |
| tantalum |
| tin      |
| tungsten |
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                                          |
|:-------------------------------------------|
| college of central florida                 |
| chipola college                            |
| daytona state college                      |
| florida southwestern state college         |
| florida state college at jacksonville      |
| college of the florida keys                |
| gulf coast state college                   |
| hillsborough community college             |
| indian river state college                 |
| florida gateway college                    |
| lake-sumter state college                  |
| state college of florida, manatee-sarasota |
| miami dade college                         |
| north florida college                      |
| northwest florida state college            |
| palm beach state college                   |
| pasco-hernando state college               |
| pensacola state college                    |
| polk state college                         |
| st. johns river state college              |
| st. petersburg college                     |
| santa fe college                           |
| seminole state college of florida          |
| south florida state college                |
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            |
|:--------------------|
| accounting services |
| insurance           |
| office supplies     |
| deposit-fee income  |
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 mentioned in g4   |
|:----------------------|
| autosport_web         |
| sunoco_japan          |
| より                  |
| tomcoronel            |
| madoka0507love1       |
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:
| lcc   |
|:------|
| 2c    |
| 4w    |
| 2e    |
| 6s    |
| 4s    |
| 5w    |
| 8w    |
| --    |
| 3s    |
| 2s    |
| 3e    |
| 7s    |
| 4e    |
| 6e    |
| 7e    |
| 6w    |
| 8s    |
| 2w    |
| 3w    |
| 5s    |


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

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

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

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

Column:
| tab set                      |
|:-----------------------------|
| none (quick access toolbar)  |
| none (core tab)              |
| none (not in the ribbon)     |
| tabsetappointment            |
| tabsetreccurringappointment  |
| tabsettodobar                |
| tabsetdailytasklist          |
| tabsetsearch                 |
| tabsetattachments            |
| tabcomposetools              |
| conversationcomposetoolsmcrp |
| tabsetgrouptools             |
| none (backstage view)        |
| none (context menu)          |
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:
| h             |
|:--------------|
| wenatchee     |
| anacortes     |
| hoquiam       |
| sedro woolley |
| walla walla   |
| yakima        |
| bremerton     |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| name from ncbi blast june 2016                                 |
|:---------------------------------------------------------------|
| [clostridium] sordellii                                        |
| erysipelatoclostridium ramosum (same microbe_ additional name) |
| not confirmed                                                  |
| lactobacillus murinus                                          |
| peptoniphilus asaccharolyticus (same microbe_new name)         |
| propionibacterium avidum                                       |
| finegoldia magna (same microbe_new name)                       |
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:
| series      |
|:------------|
| plus        |
| lx          |
| base        |
| ex          |
| luxury      |
| premium     |
| limited     |
| sx          |
| exclaim     |
| l           |
| touring nav |
| hybrid ex   |
| g80 3.8 rwd |
| ex-l        |
| se          |
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   |
|:----|
| 2c  |
| 2d  |
| 2e  |
| 2f  |
| 2g  |
| 2h  |
| 2i  |
| 2j  |
| 2k  |
| 2l  |
| 2m  |
| 2n  |
| 2o  |
| 2p  |
| 2q  |
| 2r  |
| 2s  |