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:
| b                                                            |
|:-------------------------------------------------------------|
| provide and install 24-inch diameter steel surface casing    |
| drill 12-inch diameter pilot hole                            |
| conduct geophysical logging prior to setting casing          |
| ream hole to 24-inch dia. and conduct geophysical logging    |
| provide and install 16-inch diameter sdr 17 certa-lok casing |
| grout 16-inch diameter casing and conduct temperature logs   |
| drill 14-inch diameter hole by reverse air rotary            |
| well development (air and pump)                              |
| geophysical and video logging at total depth                 |
| specific capacity pumping tests                              |
| acidization of wells, as necessary                           |
| repeat specific capacity pumping tests                       |
| constant-rate pumping tests                                  |
| disinfection and 2-point bacteriological clearance           |
| extra work                                                   |
| standby time with rig and drilling crew on site              |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| group 1   |
|:----------|
| g2        |
| g3        |
| g4        |
| g5        |
| g6        |
| g7        |
| g8        |
| g9        |
| g10       |
| g11       |
| g12       |
| g13       |
| g14       |
| g15       |
| g16       |
| g17       |
| g18       |
| g19       |
| g20       |
| g21       |
| g22       |
| g23       |
| g24       |
| g25       |
| g26       |
| g27       |
| g28       |
| g29       |
| g30       |
| g31       |
| g32       |
| g33       |
| g34       |
| g35       |
| g36       |
| g37       |
| g38       |
| 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       |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| d             |
|:--------------|
| 50102010   01 |
| 50102020   00 |
| 50102030   01 |
| 50102040   01 |
| 50102050   03 |
| 50102050   04 |
| 50102060   01 |
| 50102060   04 |
| 50102060   05 |
| 50102070   01 |
| 50102070   04 |
| 50102070   05 |
| 50102080   01 |
| 50102090   01 |
| 50102100   01 |
| 50102100   05 |
| 50102110   01 |
| 50102110   06 |
| 50102120   01 |
| 50102120   05 |
| 50102130   01 |
| 50102130   02 |
| 50102990   36 |
| 50102140   01 |
| 50102150   01 |
| 50102990   11 |
| 50102990   12 |
| 50102990   14 |
| 50103010   00 |
| 50103020   01 |
| 50103030   01 |
| 50103040   01 |
| 50104010   01 |
| 50104020   01 |
| 50104030   01 |
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:
| previous   |
|:-----------|
| aug-18     |
| sep-18     |
| oct-18     |
| nov-18     |
| dec-18     |
| jan-19     |
| feb-19     |
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   |
|:----|
| rbs |
| rtk |
| rf4 |
| rff |
| r1h |
| rdd |
| rc1 |
| rq3 |
| rxl |
| rmc |
| rae |
| ry2 |
| rxh |
| rxq |
| rwy |
| rgt |
| rqm |
| rfs |
| rln |
| rjr |
| rxp |
| rj6 |
| rn7 |
| rp5 |
| rbd |
| rwh |
| rjn |
| rvv |
| rxr |
| rde |
| rxc |
| rdu |
| rr7 |
| rlt |
| rte |
| rp4 |
| rn3 |
| rj1 |
| rn5 |
| rcd |
| rqx |
| rwa |
| ryj |
| r1f |
| rgp |
| rnq |
| rjz |
| rax |
| rxn |
| rr8 |
| rj2 |
| rbq |
| rep |
| r1k |
| rc9 |
| rwf |
| r0a |
| rpa |
| rbt |
| rq8 |
| rxf |
| rd8 |
| rp6 |
| rm1 |
| rvj |
| rnl |
| rap |
| rvw |
| rgn |
| rns |
| rbz |
| rjl |
| rtf |
| rx1 |
| rth |
| rw6 |
| rd3 |
| rhu |
| rpc |
| rhw |
| rt3 |
| ref |
| rh8 |
| ral |
| rq6 |
| ran |
| rgm |
| ra2 |
| rd1 |
| rm3 |
| rnz |
| rxk |
| rcu |
| rhq |
| rk5 |
| rxw |
| rtr |
| re9 |
| rjc |
| raj |
| rvy |
| rj7 |
| rbn |
| rwj |
| rtp |
| rmp |
| rba |
| rbv |
| rna |
| ras |
| rtd |
| rqw |
| rcx |
| rl1 |
| rfr |
| rdz |
| rpy |
| rrj |
| rl4 |
| ret |
| ra9 |
| rwd |
| rrv |
| rhm |
| rrk |
| ra7 |
| rkb |
| rtg |
| rwe |
| rtx |
| rje |
| rk9 |
| rbk |
| rww |
| rwg |
| rgr |
| ryr |
| ra3 |
| rke |
| rbl |
| rwp |
| rrf |
| rlq |
| ra4 |
| rcb |
| nv3 |
| nht |
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:
| q       |
|:--------|
| hyp,hyp |
| hyp,eqm |
| eqm,hyp |
| hyp     |
| lie,hyp |
| eqm     |
| eqm,eqm |
| plg,eqm |
| plg     |
| 0       |
| eqm,plg |
| lie,lie |
| lie     |
| hyp,lie |
| eqm,lie |
| plg,hyp |
| hyp,plg |


label: 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:
| 2014  letter grade   |
|:---------------------|
| d                    |
| f                    |
| c                    |
| t                    |
| b                    |
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:
| select   |
|:---------|
| d        |
| f        |
| e        |
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:
| new code   |
|:-----------|
| no change  |
| aur30514   |
| aur31114   |
| aur40514   |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| e                              |
|:-------------------------------|
| complete engine                |
| stroker kit                    |
| piston                         |
| drive train                    |
| connecting rod                 |
| crankshaft                     |
| competition bearing            |
| camshaft                       |
| cam gear                       |
| oversize valve                 |
| valve spring                   |
| valve spring sheet             |
| titanium valve spring retainer |
| valve lifter                   |
| solid pivot                    |
| valve guide                    |
| valve sheet ring               |
| cylinder head others           |
| strenghtened belt              |
| gasket                         |
| reinforced bolt                |
| oil lubrication                |
| intake                         |
| turbo charger                  |
| fuel line                      |
| exhaust                        |


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

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

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

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

Column:
| d   |
|:----|
| ar  |
| ca  |
| tn  |
| mo  |
| az  |
| or  |
| il  |
| mi  |
| wa  |
| tx  |
| nm  |
| ia  |
| fl  |
| ak  |
| ga  |
| wi  |
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:
| tipologia delle risorse                 |
|:----------------------------------------|
| mutuo                                   |
| altro                                   |
| finanziamento regionale                 |
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 |
|------------:|
| 0.000211794 |
| 3.79848e-05 |
| 9.78395e-09 |
| 0.000173809 |
| 1.15105e-05 |
| 0.000131796 |
| 7.94227e-05 |
| 0.000694661 |
| 0.000958252 |
| 0.0010348   |
| 0.00109638  |
| 0.0201434   |
| 5.75527e-06 |
| 0.00402639  |
| 5.75527e-05 |
| 3.51071e-05 |
| 0.000512219 |
| 1.43882e-05 |
| 0.0086329   |
| 0.00296339  |
| 0.0575527   |
| 0.00485054  |
| 0.0032218   |
| 0.00300943  |
| 0.000841996 |
| 9.26598e-05 |
| 0.00061236  |
| 6.90632e-05 |
| 0.00920843  |
| 3.74092e-05 |
| 2.24455e-05 |
| 0.00153435  |
| 1.20861e-05 |
| 0.00108544  |
| 0.000189924 |
| 0.0115105   |
| 0.00075394  |
| 0.00748185  |
| 0.000656101 |
| 3.22295e-05 |
| 0.00318957  |
| 0.000513945 |
| 0.00111249  |
| 0.000277979 |
| 0.00156658  |
| 0.447265    |
| 0.0102815   |
| 0.026989    |
| 2.47476e-05 |
| 0.00219161  |
| 0.000191075 |
| 0.000896208 |
| 0.00631801  |
| 9.16771e-05 |
| 0.000196206 |
| 0.107099    |
| 0.037699    |
| 0.0222767   |
| 0.00104957  |
| 5.4675e-05  |
| 0.00503586  |
| 0.0642596   |
| 0.00104358  |
| 0.00095961  |
| 0.000944439 |
| 0.00013122  |
| 0.00985314  |
| 0.00263361  |
| 0.00741642  |
| 0.00830234  |
| 0.000268195 |
| 0.000417259 |
| 0.000209915 |
| 0.0197063   |
| 0.00856795  |
| 0.00171219  |
| 0.0929623   |
| 0.0634691   |
| 0.00633079  |
| 0.00172658  |
| 0.00188773  |
| 0.0333806   |
| 0.0668949   |
| 1.72658e-05 |
| 8.86311e-05 |
| 0.00552045  |
| 2.187e-05   |
| 7.48185e-05 |
| 0.00297547  |
| 0.000169205 |
| 0.0235966   |
| 0.000771781 |
| 0.00241664  |
| 0.00250527  |
| 0.000122587 |
| 5.43988e-06 |
| 0.000310784 |
| 0.000872499 |
| 0.0049478   |
| 0.00111595  |
| 0.000335532 |
| 0.000509917 |
| 0.00011971  |
| 0.000215823 |
| 0.0103595   |
| 0.170992    |
| 0.000538118 |
| 0.0402869   |
| 0.00269059  |
| 0.00159076  |
| 0.000482291 |
| 0.000197406 |
| 0.00114185  |
| 0.000507615 |
| 0.000181291 |
| 0.000135824 |
| 0.0126616   |
| 0.000264742 |
| 0.000812644 |
| 0.00415703  |
| 0.085178    |
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:
| clasificacion   |
|:----------------|
| camioneta       |
| camion          |
| automovil       |
| maquinaria      |
| pipa            |
| motocicleta     |
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                                |
|:---------------------------------|
| meridian-n wh rimless + s&c slim |
| gap  wh + s&c                    |
| gap rimless  wh + s&c            |
| nexo  wh + s&c                   |
| nexo rimless  wh + s&c           |
| debba wh + s&c                   |
| debba wh rimless + s&c           |
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:
| 氏名シメイ        |
|:------------------|
| 森 さとみモリ     |
| 小田島 美咲       |
| 今泉 賢一ケンイチ |
| 小松 徹           |
| 吉田 達士         |
| 富岡 隆           |
| 北条 翔子         |
| 長谷川 隆ハセガワ |
| 森 寛治           |
| 吉田 宏ヨシダ     |
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:
| artikelnummer     |
|:------------------|
| 1-15-80-00-30-spp |
| 1-20-10-60-00-pek |
| 1-20-10-62-00-pek |
| 1-25-50-10-00-kvs |
| 1-25-50-32-00-ppv |
| 1-25-50-32-00-wiv |
| 1-30-50-10-00-kst |
| 1-30-51-00-00-ksv |
| 1-40-10-02-00-ggs |
| 1-40-12-04-00-ggb |
| 1-45-12-04-00-gvr |
| 1-55-22-27-00-sg3 |
| 1-55-22-58-00-sg3 |
| 1-60-10-11-00-gza |
| 1-90-00-00-03-psc |
| 1-90-00-00-22-pgs |
| 1-90-00-00-26-pgs |
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        |
| g4        |
| g5        |
| g6        |
| g9        |
| g12       |
| g2        |
| g8        |
| g14       |
| g18       |
| g19       |
| g7        |
| g10       |
| g11       |
| g13       |
| g15       |
| g16       |
| g17       |
| g20       |
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:
| log   |
|:------|
| f5    |
| f4    |
| f0    |
| f3    |
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:
| building          |
|:------------------|
| gerberding hall   |
| mackenzie hall    |
| harborview maleng |
| mag h.s.c./t      |
| graves hall       |
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:
| 销售id   |
|:---------|
| sn01002  |
| sn01003  |
| sn01004  |
| sn01001  |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| group 1   |
|:----------|
| g2        |
| g3        |
| g4        |
| g5        |
| g6        |
| g7        |
| g8        |
| g9        |
| g10       |
| g11       |
| g12       |
| g13       |
| g14       |
| g15       |
| g16       |
| g17       |
| g18       |
| g19       |
| g20       |
| g21       |
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                    |
|:---------------------|
| equipment            |
| supplies             |
| contractual          |
| other                |
| total direct charges |
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:
| activity                                                                           |
|:-----------------------------------------------------------------------------------|
| provide psychosocial activities for children in child friendly spaces /child clubs |
| provide mine risk education to girls and boys                                      |
| refer conflict affected children to specialist support services where required     |
| identify separated children                                                        |
| identify unaccompanied children                                                    |
| support children at risk and survivors through inter-agency case management system |
| placement of separated children in alternative care arrangements and follow-up     |
| placement of unaccompanied children in alternative care arrangements and follow-up |
| reunification of separated children                                                |
| reunification of unaccompanied children                                            |
| establish cfs / child clubs                                                        |
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:
| event     |
|:----------|
| view      |
| search    |
| update    |
| check out |
| check 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:
| place url                                                |
|:---------------------------------------------------------|
| https://api.twitter.com/1.1/geo/id/35189a8fcb82b788.json |
| https://api.twitter.com/1.1/geo/id/55b4f9e5c516e0b6.json |
| https://api.twitter.com/1.1/geo/id/7d51c8dfbb08a9de.json |
| https://api.twitter.com/1.1/geo/id/4ec01c9dbc693497.json |
| https://api.twitter.com/1.1/geo/id/3877d6c867447819.json |
| https://api.twitter.com/1.1/geo/id/76c1bb41d3de7ff1.json |
| https://api.twitter.com/1.1/geo/id/09f6a7707f18e0b1.json |
| https://api.twitter.com/1.1/geo/id/0074f28e263ac8db.json |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| city                   |
|:-----------------------|
| ardrossan (fultonvale) |
| barrhead               |
| bashaw                 |
| beaumont               |
| bon accord             |
| bruderheim             |
| calahoo                |
| calling lake           |
| calmar                 |
| camrose                |
| devon                  |
| drayton valley         |
| edmonton               |
| evansburg              |
| forestburg             |
| fort saskatchewan      |
| gibbons                |
| gunn                   |
| holden                 |
| josephburg             |
| lamont                 |
| leduc                  |
| legal                  |
| mannville              |
| mayerthorpe            |
| millet                 |
| morinville             |
| mundare                |
| new sarepta            |
| onoway                 |
| redwater               |
| riviere qui barre      |
| sherwood park          |
| smoky lake             |
| spruce grove           |
| st. albert             |
| stony plain            |
| thorhild               |
| thorsby                |
| tofield                |
| vegreville             |
| wabamun                |
| warburg                |
| westlock               |
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:
| neighborhood   |
|:---------------|
| aetna          |
| pulaski        |
| miller         |
| east glen park |
| emerson        |
| west side      |
| black oak      |
| brunswick      |
| west glen park |
| central        |
| small farms    |
| tolleston      |
| horace mann    |
| ambridge mann  |
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:
| coordinador de área         |
|:----------------------------|
| natisma julieta lópez salas |
| joaquin hernández sánchez   |
| sergio ernesto moreno soto  |
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                    |
|:---------------------|
| from other relatives |
| savings              |
| from scholarships    |
| other                |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| column1                                                       |
|:--------------------------------------------------------------|
| wrap text around pictures with harmony                        |
| change page with new chapter.                                 |
| headings_ toc_ page #_ footnotes                              |
| url (web links) must always go in footnotes unless in biblio. |
| use theme_ style_ cover page_ business                        |
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:
| units_vol   |
|:------------|
| gal/day     |
| yd^3        |
| tons/d      |
| gal/yr      |
| gal         |
| yd^3/d      |
| gal/y       |
| gal/min     |
| ft^3        |
| ft/d        |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| a                                |
|:---------------------------------|
| cookie program                   |
| troop money earning project      |
| troop/group sponsorhips          |
| funds for insignia/recognition   |
| funds for programs/trips/camping |
| troop dues                       |
| other                            |


label: troop/group sponsorhips
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         |
|:-------------|
| james dunbar |
| bob turn     |
| paul smith   |
| steve dozza  |
| stu hawk     |
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:
| idea   |
|:-------|
| idea 3 |
| idea 4 |
| idea 5 |
| idea 6 |
| idea 7 |
| idea 8 |
| idea 9 |
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                                                                                      |
|:------------------------------------------------------------------------------------------------|
| date is not valid or lacks required precision.                                                  |
| the value conflicts with other data in the message.                                             |
| the value is not valid. this applies for fields that are not associated with a table of values. |