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:
| contract award date   |
|:----------------------|
| q3 2017               |
| q4 2019               |
| q1 2016               |
| q4 2016               |
| q1 2017               |
| q2 2017               |
| q1 2015               |
| q1 2018               |
| q2 2015               |
| q3 2014               |
| q2 2016               |
| q3 2016               |
| q4 2017               |
| q4 2015               |
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        |
|:---------|
| cktw 003 |
| cktw 004 |
| cktw 005 |
| cktw 006 |
| cktw 007 |
| cktw 008 |
| cktw 009 |
| cktw 010 |
| cktw 011 |
| cktw 012 |
| cktw 013 |
| cktw 014 |
| cktw 015 |
| cktw 016 |
| cktw 017 |
| cktw 018 |
| cktw 019 |
| cktw 020 |
| cktw 021 |
| cktw 022 |
| cktw 023 |
| cktw 024 |
| cktw 025 |
| cktw 026 |
| cktw 027 |
| cktw 028 |
| cktw 029 |
| cktw 030 |
| cktw 031 |
| cktw 032 |
| cktw 033 |
| cktw 034 |
| cktw 035 |
| cktw 036 |
| cktw 037 |
| cktw 038 |
| cktw 039 |
| cktw 040 |
| cktw 041 |
| cktw 042 |
| cktw 043 |
| cktw 044 |
| cktw 045 |
| cktw 046 |
| cktw 047 |
| cktw 048 |
| cktw 049 |
| cktw 050 |
| cktw 051 |
| cktw 052 |
| cktw 053 |
| cktw 054 |
| cktw 055 |
| cktw 056 |
| cktw 057 |
| cktw 058 |
| cktw 059 |
| cktw 060 |
| cktw 061 |
| cktw 062 |
| cktw 063 |
| cktw 064 |
| cktw 065 |
| cktw 066 |
| cktw 067 |
| cktw 068 |
| cktw 069 |
| cktw 070 |
| cktw 071 |
| cktw 072 |
| cktw 073 |
| cktw 074 |
| cktw 075 |
| cktw 076 |
| cktw 077 |
| cktw 078 |
| cktw 079 |
| cktw 080 |
| cktw 081 |
| cktw 082 |
| cktw 083 |
| cktw 084 |
| cktw 085 |
| cktw 086 |
| cktw 087 |
| cktw 088 |
| cktw 089 |
| cktw 090 |
| cktw 091 |
| cktw 092 |
| cktw 093 |
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:
| bureau                                    |
|:------------------------------------------|
| government accountability office          |
| legislative branch boards and commissions |
| capitol police                            |
| congressional budget office               |
| united states tax court                   |
| architect of the capitol                  |
| library of congress                       |
| botanic garden                            |
| government publishing office              |
| office of compliance                      |
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:
| column4                                   |
|:------------------------------------------|
| agenda zonder plaatjes_ soho elite        |
| pen blauw_ zwart_ rood pointer (per stuk) |
| ballpoint pointer (per stuk)              |
| vulpen didaeta (per stuk)                 |
| inktpatronen penquin (6x) stuk            |
| potlood_ (per stuk)                       |
| schrift a4                                |
| bloknoot_ a4                              |
| bloknoot_ klein a5 verona                 |
| kladblok klein a5 kangaroo                |
| kladblok a4                               |
| harde cahier met indeling (gekleurd)      |
| ringband kangaroo                         |
| mapblaadjes soho (100x) stuk              |
| scheidingsblaadjes soho (5x) stuk         |
| plastic map                               |
| _insteekhoes soho 25x stuk                |
| snelhechters esselte (per stuk)           |
| snelhechters kangaroo                     |
| kaftpapier verhaak                        |
| boeklont_ 1m- 45cm verhaak                |
| etui                                      |
| gum maped                                 |
| slijper (normaal) panter                  |
| slijper (figuur)                          |
| passer                                    |
| schaar didaeta                            |
| geodriehoek                               |
| liniaal 16 cm (plastic)                   |
| liniaal 30 cm (hout) horse brand          |
| tipp-ex huang yang                        |
| tekenschrift                              |
| tekenschrift_ a4                          |
| kleurpotloden  pointer (12x) stuk         |
| kleurpotloden (24x) stuk                  |
| stiften (6x) stuk                         |
| stiften (10x) stuk faber -castell         |
| stifthouder_ elegant                      |
| penceel_ yi pin xuan 6x set               |
| plakboek                                  |
| lijm (100 ml)_ collall                    |
| stickers                                  |
| calculator_ nikko                         |
| calculator_ scientific                    |
| woordenboek_ prisma                       |
| woordenboek_ van dale                     |
| schooltas janpak                          |
| samsonite                                 |
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:
| team                     |
|:-------------------------|
| tunbridge wells harriers |
| paddock wood ac          |
| dartford road runners    |
| beckenham running club   |
| medway & maidstone ac    |
| maidstone harriers       |
| orpington road runners   |
| kent ac                  |
| dulwich runners ac       |
| ashford & district rrc   |
| deal tri                 |
| folkestone running club  |
| sittingbourne striders   |
| thanet road runners ac   |
| istead & ifield harriers |
| bexley ac                |
| cambridge harriers       |
| larkfield ac             |
| invicta east kent ac     |
| petts wood runners       |
| swanley & district ac    |
| sevenoaks ac             |
| canterbury harriers      |
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:
| plant type         |
|:-------------------|
| supercritical      |
| subcritical        |
| ultrasupercritical |
| cfb                |
|                    |
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:
|   čas epic žilina warrior |
|--------------------------:|
|                 0.0254352 |
|                 0.0310718 |
|                 0.0334468 |
|                 0.0295556 |
|                 0.0276968 |
|                 0.0313171 |
|                 0.0293854 |
|                 0.0306528 |
|                 0.0322824 |
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:
| sheet                 |
|:----------------------|
| other                 |
| d.economic inputs     |
| e. strategy           |
| vaccines              |
| +coldchain            |
| charts                |
| f.unit costs          |
| microplanning         |
| service delivery      |
| general               |
| all                   |
| plug-train            |
| plug-meeting          |
| plug-micro            |
| var_languages         |
| checklist             |
| total costs           |
| recurr costs          |
| initial investment    |
| dashboard             |
| training              |
| social mob-iec        |
| monitoring            |
| +cold chain           |
| recur_costs           |
| strategy and vaccines |
| strategy              |
| plug-micro (3)        |
| plug-ins              |
| cold chain            |
| overall strategy      |
| social mobilisation   |
| outputs               |
| owner                 |
| labels                |
| subnational info      |
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:
| wireless &_x005f_x000d_bluetooth    |
|:------------------------------------|
| no                                  |
| intel ax201 11ax, 2x2 + bt5.2       |
| intel ax201 11ax, 2x2 + bt5.1       |
| none                                |
| intel 8265 11ac, 2x2 + bt4.2        |
| intel 3165 11ac, 1x1 + bt4.2        |
| intel ax201 11ax, 2x2 + bt5.1, vpro |
| intel 9260 11ac, 2x2 + bt5.0        |
| rtl8822ce 11ac, 2x2 + bt5.0         |
| intel 9560 11ac, 2x2 + bt5.0        |
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      |
|:-------|
| 227ea  |
| 236fa  |
| 245fa  |
| 365mfc |
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                                                                  |
|:------------------------------------------------------------------------|
| contract awarded                                                        |
| pin live pqq live itt - q2 2018                                         |
| awarded                                                                 |
| itt in evaluation                                                       |
| framework awarded                                                       |
| itt live                                                                |
| pqq in evaluation                                                       |
| tenders in evaluation                                                   |
| awarded subject to standstill period                                    |
| in evaluation                                                           |
| further comp under ccs grant admin framework. itt documents in approval |
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:
|       y |
|--------:|
| 1.9e-05 |
| 1.3e-06 |
| 4.5e-07 |
| 6.7e-07 |
| 5.3e-07 |
| 1.8e-06 |
| 4.5e-06 |
| 1.2e-05 |
| 8.2e-07 |
| 7e-07   |
| 6.5e-07 |
| 7.1e-07 |
| 6.2e-07 |
| 7.2e-07 |
| 5.6e-07 |
| 5.8e-07 |
| 7.5e-07 |
| 8.3e-07 |
| 9.1e-07 |
| 3.6e-07 |
| 4.8e-07 |
| 2.7e-07 |
| 4.1e-07 |
| 4.6e-07 |
| 2.6e-07 |
| 3.9e-07 |
| 3.4e-07 |
| 3.2e-07 |
| 3.7e-07 |
| 4e-07   |
| 2.9e-07 |
| 3.5e-07 |
| 2.8e-07 |
| 2.4e-07 |
| 3.3e-07 |
| 9.7e-07 |
| 1.1e-06 |
| 7.9e-06 |
| 7.2e-06 |
| 7e-06   |
| 6.4e-06 |
| 4.4e-06 |
| 2.6e-06 |
| 4.9e-06 |
| 8.8e-07 |
| 2e-06   |
| 6.4e-07 |
| 5e-07   |
| 1.2e-06 |
| 1.5e-06 |
| 2.4e-06 |
| 1.7e-06 |
| 1e-06   |
| 2.1e-06 |
| 5e-05   |
| 2.4e-05 |
| 2.6e-05 |
| 7.6e-06 |
| 5.9e-05 |
| 1.8e-05 |
| 9.7e-05 |
| 0.0001  |
| 1.4e-05 |
| 2.7e-05 |
| 1.7e-05 |
| 6.1e-05 |
| 9.2e-06 |
| 2.5e-05 |
| 1e-05   |
| 3.7e-05 |
| 4.1e-05 |
| 1.1e-05 |
| 8.8e-05 |
| 5.9e-07 |
| 9.6e-07 |
| 6.3e-07 |
| 5.5e-07 |
| 6.1e-07 |
| 8.1e-07 |
| 8.4e-07 |
| 7.9e-07 |
| 7.8e-07 |
| 8.7e-07 |
| 6.6e-07 |
| 5.2e-07 |
| 5.7e-07 |
| 6e-07   |
| 6.9e-07 |
| 3e-07   |
| 2.5e-07 |
| 2.3e-07 |
| 2.5e-06 |
| 1.6e-06 |
| 2.9e-06 |
| 3.6e-06 |
| 6.3e-06 |
| 4.4e-07 |
| 2.2e-07 |
| 1.3e-05 |
| 3.4e-05 |
| 1.6e-05 |
| 5.5e-05 |
| 2.9e-05 |
| 7.6e-05 |
| 5.8e-05 |
| 3e-05   |
| 7.8e-06 |
| 2.3e-05 |
| 9.1e-06 |
| 8.7e-06 |
| 1.4e-06 |
| 9.9e-07 |
| 8.6e-07 |
| 4.7e-07 |
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 |
|-----------:|
| 0.0143626  |
| 0.0117174  |
| 0.0147217  |
| 0.0137632  |
| 0.0196153  |
| 0.0267337  |
| 0.0249041  |
| 0.0251363  |
| 0.0255769  |
| 0.0167732  |
| 0.0220359  |
| 0.0232747  |
| 0.021403   |
| 0.0197803  |
| 0.0205809  |
| 0.0245324  |
| 0.0217313  |
| 0.0226208  |
| 0.0215906  |
| 0.0218953  |
| 0.0193321  |
| 0.0191433  |
| 0.0193557  |
| 0.0208395  |
| 0.0183867  |
| 0.0145542  |
| 0.0159398  |
| 0.0177707  |
| 0.0137152  |
| 0.011645   |
| 0.0152716  |
| 0.0180551  |
| 0.017486   |
| 0.0129945  |
| 0.0128021  |
| 0.0186706  |
| 0.0155104  |
| 0.0150087  |
| 0.014147   |
| 0.0142428  |
| 0.0135952  |
| 0.0146738  |
| 0.0126817  |
| 0.00890533 |
| 0.00829619 |
| 0.0131388  |
| 0.0147456  |
| 0.0136912  |
| 0.0132349  |
| 0.0152238  |
| 0.0119105  |
| 0.0126576  |
| 0.0141949  |
| 0.0149131  |
| 0.0144105  |
| 0.0151043  |
| 0.0145063  |
| 0.0144584  |
| 0.0141709  |
| 0.0127299  |
| 0.0134512  |
| 0.0109924  |
| 0.0115484  |
| 0.0117657  |
| 0.0125854  |
| 0.0121035  |
| 0.0123204  |
| 0.0120311  |
| 0.0111859  |
| 0.00946478 |
| 0.00931893 |
| 0.0114518  |
| 0.00936755 |
| 0.00980485 |
| 0.0104113  |
| 0.0124409  |
| 0.0103386  |
| 0.0123927  |
| 0.0136192  |
| 0.0142668  |
| 0.0107504  |
| 0.00934324 |
| 0.0121276  |
| 0.00434658 |
| 0.00995049 |
| 0.0112584  |
| 0.0113551  |
| 0.0112101  |
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:
| value category       |
|:---------------------|
| house                |
| workshop             |
| storage/warehouse    |
| office               |
| shop                 |
| agricultural         |
| abandoned            |
| unfinished           |
| unclassifiable       |
| restaurant           |
| car park/garage      |
| outdoor sports field |
| flat                 |
| factory              |
| indoor sports field  |
| hotel                |
| sport field          |
| exception property   |
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:
| residential address: city/state/zip   |
|:--------------------------------------|
| carolina beach_ nc 28428              |
| wilmington_ nc 28411                  |
| wilmington_ nc 28403                  |
| wilmington_ nc 28412                  |
| wilmington_ nc 28409                  |
| wrightsville beach_ nc 28480          |
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 tweeters in entire graph   |
|:-------------------------------|
| metro_tv                       |
| nycking                        |
| infoll                         |
| foxnews                        |
| ktnews                         |
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                               |
|:-----------------------------------------|
| chickens and other poultry               |
| pigs                                     |
| sheep and goats                          |
| other animals                            |
| animal fats                              |
| offals                                   |
| fish, seafood and other aquatic products |
| eggs                                     |
| milk products                            |
| grains and cereals                       |
| sugar                                    |
| roots and tubers                         |
| pulses                                   |
| vegetable oil                            |
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:
| consequence category                                                                                                                                                               |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| employment; occupational and professional license and certification; business license and other property rights                                                                    |
| employment                                                                                                                                                                         |
| employment; occupational and professional license and certification                                                                                                                |
| employment; family/domestic rights                                                                                                                                                 |
| employment; occupational and professional license and certification; business license and other property rights; recreational license, including firearms; motor vehicle licensure |
| employment; occupational and professional license and certification; registration, notification, and residency restrictions                                                        |
| employment; business license and other property rights                                                                                                                             |
| employment; government contracting and program participation; political and civic participation                                                                                    |
| employment; political and civic participation                                                                                                                                      |
| employment; occupational and professional license and certification; 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:
| hdd    |
|:-------|
| 80 gb  |
| 250 gb |
| 40 gb  |
| 120 gb |
| 60 gb  |
| 160 gb |
| 30 gb  |
| 320 gb |
| 500 gb |
| 128 gb |
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:
| district   |
|:-----------|
| mty        |
| zac        |
| gto        |
| qro        |
| ver        |
| chi        |
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:
| ct6                     |
|:------------------------|
| carnival sensation      |
| carnival dream          |
| carnival ecstasy        |
| norwegian breakaway     |
| carnival pride          |
| carnival splendor       |
| event                   |
| explorer of the seas    |
| carnival sunshine       |
| carnival liberty        |
| state of port           |
| norwegian gem           |
| supervisor of elections |
| change of command       |
| reverse trade show      |
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:
| družstvo     |
|:-------------|
| osm strikers |
| pekaři       |
| šproc        |
| čajíčci      |
| chemici      |
| konfekce     |
| dráteníci    |
| rychlá rota  |
| šestá kolona |
| zákulisí     |
| hrdinové     |
| rafani       |
| sklady       |
| qualita      |
| dráteníci b  |
| náklaďáci    |
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:
| sex    |
|:-------|
| all    |
| male   |
| female |
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:
| academicyear    |
|:----------------|
| fy12: 2011/2012 |
| fy11: 2010/2011 |
| fy13: 2012/2013 |
| fy14: 2013/2014 |
| fy15: 2014/2015 |
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:
| citation               |
|:-----------------------|
| minn. r. 6400.6900     |
| minn. r. 7200.5500     |
| minn. r. 9505.0335     |
| minn. r. 9520.0860     |
| minn. r. 9525.1850     |
| minn. stat. § 144.057  |
| minn. stat. § 144.99   |
| minn. stat. § 144a.04  |
| minn. stat. § 144a.11  |
| minn. stat. § 144a.62  |
| minn. stat. § 144a.754 |
| minn. stat. § 144e.27  |
| minn. stat. § 144e.28  |
| minn. stat. § 146a.08  |
| minn. stat. § 147.091  |
| minn. stat. § 147.111  |
| minn. stat. § 147a.13  |
| minn. stat. § 147c.15  |
| minn. stat. § 147d.17  |
| minn. stat. § 147e.15  |
| minn. stat. § 148.10   |
| minn. stat. § 148.102  |
| minn. stat. § 148.6448 |
| minn. stat. § 148.75   |
| minn. stat. § 148.7813 |
| minn. stat. § 148.996  |
| minn. stat. § 148b.07  |
| minn. stat. § 150a.08  |
| minn. stat. § 150a.13  |
| minn. stat. § 153.24   |
| minn. stat. § 197.4552 |
| minn. stat. § 214.072  |
| minn. stat. § 214.075  |
| minn. stat. § 214.10   |
| minn. stat. § 214.106  |
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   |
|:-------------|
| pies         |
| decoration   |
| manager      |
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:
| ansvarig person   |
|:------------------|
| erica hult        |
| björn larsson     |
| sofia schultz     |
| per karlsson      |
| cecilia lidén     |
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:
|   среднее время обработки |
|--------------------------:|
|               0           |
|               0.000590278 |
|               0.0022338   |
|               0.00164352  |
|               0.000636574 |
|               0.0145949   |
|               0.00268519  |
|               0.0078125   |
|               0.000810185 |
|               0.00346065  |
|               0.00467593  |
|               0.00298611  |
|               0.00162037  |
|               0.00324074  |
|               0.00222222  |
|               0.00331019  |
|               0.0065625   |
|               0.0128472   |
|               0.0040162   |
|               0.0030787   |
|               0.0028125   |
|               0.00104167  |
|               0.00240741  |
|               0.00136574  |
|               0.0021875   |
|               0.00190972  |
|               0.00209491  |
|               0.00353009  |
|               0.00149306  |
|               0.00234954  |
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:
| analysis code   |
|:----------------|
| sal             |
| conductivity    |
| tn              |
| tp              |
| chl a           |
| do              |
| cu              |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| group 2   |
|:----------|
| g3        |
| g9        |
| g11       |
| g13       |
| g17       |
| g23       |
| g2        |
| g4        |
| g7        |
| g8        |
| g5        |
| g6        |
| g12       |
| g15       |
| g10       |
| g14       |
| g16       |
| g18       |
| g19       |
| g20       |
| g21       |
| g22       |
| g24       |
| g25       |
| g26       |
| g27       |
| g28       |
| g29       |
| g30       |
| g31       |
| g32       |
| g33       |
| g34       |
| g35       |
| g36       |
| g37       |
| g38       |
| g39       |
| g40       |
| g41       |
| g42       |
| g43       |
| g44       |
| g45       |
| g46       |
| g47       |
| g48       |
| g49       |
| g50       |
| g51       |
| g52       |
| g53       |
| g54       |
| g55       |
| g56       |
| g57       |
| g58       |
| g59       |
| g60       |
| g61       |
| g62       |
| g63       |
| g64       |
| g65       |
| g66       |
| g67       |
| g68       |
| g69       |
| g70       |
| g71       |
| g72       |
| g73       |
| g74       |
| g75       |
| g76       |
| g77       |
| g78       |
| g79       |
| g80       |
| g81       |
| g82       |
| g83       |
| g84       |
| g85       |
| g86       |
| g87       |
| g88       |
| g89       |
| g90       |
| g91       |
| g92       |
| g93       |
| g94       |
| g95       |
| g96       |
| g97       |
| g98       |
| g99       |
| g100      |
| g101      |
| g102      |
| g103      |
| g104      |
| g105      |
| g106      |
| g107      |
| g108      |
| g109      |
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:
| ciudad        |
|:--------------|
| veracruz      |
| coatzacoalcos |
| cosoleacaque  |