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:
| a               |
|:----------------|
| dn65 low flow   |
| dn65 high flow  |
| dn80 low flow   |
| dn80 high flow  |
| dn100 low flow  |
| dn100 high flow |
| dn125 low flow  |
| dn125 high flow |
| dn150 low flow  |
| dn150 high flow |
| dn200 low flow  |
| dn200 high flow |
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           |
|:---------------------|
| joseph schulte       |
| cma cgm tanya        |
| cos tbn 37           |
| cma cgm volga        |
| cosco shipping seine |
| tbn 34               |
| cma cgm lisa marie   |
| judith schulte       |
| cma cgm columbia     |
| johanna schulte      |
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      |
|:----------------|
| alameda         |
| alpine          |
| amador          |
| butte           |
| calaveras       |
| colusa          |
| contra costa    |
| del norte       |
| el dorado       |
| fresno          |
| glenn           |
| humboldt        |
| imperial        |
| inyo            |
| kern            |
| kings           |
| lake            |
| lassen          |
| los angeles     |
| madera          |
| marin           |
| mariposa        |
| mendocino       |
| merced          |
| modoc           |
| mono            |
| monterey        |
| napa            |
| nevada          |
| orange          |
| placer          |
| plumas          |
| riverside       |
| sacramento      |
| san benito      |
| san bernardino  |
| san diego       |
| san francisco   |
| san joaquin     |
| san luis obispo |
| san mateo       |
| santa barbara   |
| santa clara     |
| santa cruz      |
| shasta          |
| sierra          |
| siskiyou        |
| solano          |
| sonoma          |
| stanislaus      |
| sutter          |
| tehama          |
| trinity         |
| tulare          |
| tuolumne        |
| ventura         |
| yolo            |
| yuba            |
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:
| existing sdo                                                                 |
|:-----------------------------------------------------------------------------|
| astm international_ institute of electrical and electronics engineers (ieee) |
| nij_ astm                                                                    |
| nfpa_ fema                                                                   |
| astm e2520-07                                                                |
| astm                                                                         |
| nist                                                                         |
| nij_ astm e54 or f23                                                         |
| nij                                                                          |
| nist_ nfpa                                                                   |
| nfpa_ niosh                                                                  |
| nfpa                                                                         |
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:
| fuel         |
|:-------------|
| coal         |
| oil          |
| biomass      |
| biogas       |
| landfill gas |
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                                        |
|:-----------------------------------------|
| general requirements and provisions      |
| protection, removal, realignment and     |
| replacement of services:                 |
| (a)                                      |
| (i)                                      |
| (ii)                                     |
| (b)                                      |
| excavation:                              |
| (1)                                      |
| (2)                                      |
| backfilling:                             |
| dispute adjudication board (dab)         |
| repairing of bridge joints               |
| overhaul on material hauled in excess of |
| 1,0km (ordinary overhaul)                |
| service ducts:                           |
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:
| l    |
|:-----|
| nfmt |
| bkgt |
| nfjt |
| mept |
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:
| mea program: program name                      |
|:-----------------------------------------------|
| residential renewable grants                   |
| commercial renewable grants                    |
| cegp - commercial renewables                   |
| cegp - residential renewables                  |
| residential sustainable energy contracts pilot |
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:
| employer's state/country   |
|:---------------------------|
| ma                         |
| ny                         |
| ga                         |
| az                         |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| type of truck     |
|:------------------|
| tipper            |
| flat bed          |
| drop side         |
| volume van        |
| furniture removal |
| tanker            |
| fridge            |
| container         |
| lowbed            |
| car carrier       |
| cement mixer      |
| abnormal          |
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:
| year   |
|:-------|
| y2015  |
| y2016  |
| y2017  |
| y2018  |
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:
| objective                                                                                             |
|:------------------------------------------------------------------------------------------------------|
| increase number of staff members actively engaged in energy management-related activities             |
| continually improve our energy performance, and performance of the enms                               |
| increase number of student members actively engaged in energy management-related activities           |
| minimise energy demand, use, and consumption                                                          |
| increase number of staff and student members actively engaged in energy management-related activities |
| improve the energy performance of existing buildings and facilities                                   |
| optimise the energy performance of new buildings and facilities                                       |
| ensure energy performance considerations are made for high value purchasing activities                |
| increase self-sufficiency from on-site renewables                                                     |
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:
| vehicle #   |
|:------------|
| p23         |
| pw52        |
| pw36        |
| p16         |
| pw65        |
| p101        |
| p10         |
| p17         |
| c89         |
| p95         |
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                        |
|:----------------------------------|
| sheila.gage@use.salvationarmy.org |
| cindygomez@cityofpassaicnj.gov    |
| dbuchholtz@paterson.k12.nj.us     |
| csarullo@passaicschools.org       |
| aaya.rasheed@icpcnj.org           |
| jserapiglia@mrhs.net              |
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   |
|:----|
| dza |
| and |
| ago |
| aia |
| atg |
| arg |
| arm |
| aus |
| aut |
| aze |
| bhs |
| bhr |
| bgd |
| brb |
| blr |
| bel |
| blz |
| ben |
| btn |
| bol |
| bih |
| bwa |
| bra |
| vgb |
| brn |
| bgr |
| bfa |
| bdi |
| cpv |
| khm |
| cmr |
| can |
| caf |
| tcd |
| chl |
| chn |
| col |
| com |
| cog |
| cok |
| cri |
| civ |
| hrv |
| cub |
| cyp |
| cze |
| prk |
| cod |
| dnk |
| dji |
| dma |
| dom |
| ecu |
| egy |
| slv |
| gnq |
| eri |
| est |
| swz |
| eth |
| fji |
| fin |
| fra |
| gab |
| gmb |
| geo |
| deu |
| gha |
| grc |
| grd |
| gtm |
| gin |
| gnb |
| guy |
| hti |
| vat |
| hnd |
| hun |
| isl |
| ind |
| idn |
| irn |
| irq |
| irl |
| isr |
| ita |
| jam |
| jpn |
| jor |
| kaz |
| ken |
| kir |
| kwt |
| kgz |
| lao |
| lva |
| lbn |
| lso |
| lbr |
| lby |
| lie |
| ltu |
| lux |
| mdg |
| mwi |
| mys |
| mdv |
| mli |
| mlt |
| mhl |
| mrt |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| product       |
|:--------------|
| darnell       |
| bellen        |
| crested beaut |
| aspen         |
| tri fly       |
| carlota       |
| quad          |
| fast catch    |
| yanaki        |
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        |
|:---------|
| russo    |
| evans    |
| wood     |
| allsopp  |
| tarling  |
| allen    |
| young    |
| marley   |
| page     |
| truelove |
| marsden  |
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:
| trans_partner   |
|:----------------|
| f               |
| x               |
| 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:
| winter wheat (ww)                 |
|:----------------------------------|
| dark northern spring wheat (dnsw) |
| oats (o)                          |
| peas (p)                          |
| austrian winter peas (awp)        |
| lentils (l)                       |
| garbanzos (g)                     |
| spring canola (sc)                |
| camelina (cml)                    |
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:
|   white british |
|----------------:|
|      0.0784439  |
|      0.0300069  |
|      0.00798277 |
|      0.0297626  |
|      0.0324222  |
|      0.00440681 |
|      0.17934    |
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:
| descripción                                                      |
|:-----------------------------------------------------------------|
| justicia                                                         |
| coordinación de la política de gobierno                          |
| relaciones exteriores                                            |
| asuntos financieros y hacendarios                                |
| defensa                                                          |
| asuntos de orden público y de seguridad                          |
| investigación fundamental (básica)                               |
| otros servicios generales                                        |
| desarrollo social                                                |
| protección ambiental                                             |
| vivienda y servicios a la comunidad                              |
| salud                                                            |
| recreación_ cultura y otras manifestaciones sociales             |
| educación                                                        |
| protección social                                                |
| otros asuntos sociales                                           |
| desarrollo económico                                             |
| asuntos económicos_ comerciales y laborales en general           |
| agropecuaria_ silvicultura_ pesca y caza                         |
| combustibles y energía                                           |
| minería_ manufacturas y construcción                             |
| transporte                                                       |
| comunicación                                                     |
| turismo                                                          |
| investigación y desarrollo relacionados con asuntos económicos   |
| otras industrias y oros asuntos económicos                       |
| otras                                                            |
| transacciones de la deuda pública / costo financiero de la deuda |
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:
| u            |
|:-------------|
| results!b591 |
| results!b592 |
| results!b593 |
| results!b594 |
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             |
|:--------------|
| péauéãj       |
| daiàä£àuàgà   |
| péæãgàªàäauà® |
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:
| sec_name       |
|:---------------|
| crim-33-1169   |
| crim-30-6244   |
| crim-33-6084   |
| crim-33-8274   |
| corr-30-6109   |
| corr-30-7131   |
| crim-30-6433   |
| crim-30-7131   |
| crim-49aw-9165 |
| crim-49ay-9113 |
Task: Error-Detect (source dataset: Spreadsheet-Tables)

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

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

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

Column:
| state   |
|:--------|
| nv      |
| ut      |
| az      |
| ca      |
| oh      |
| co      |
| il      |
| id      |
| nj      |
| mn      |
| me      |
| fl      |
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:
| země            |
|:----------------|
| česká republika |
| usa             |
| rakousko        |
| německo         |
| polsko          |
| kanada          |
| austrálie       |
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:
| processstrand   |
|:----------------|
| c               |
| k               |
| a               |
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:
| collection resource id   |
|:-------------------------|
| cpf036                   |
| cpf037                   |
| cpf038                   |
| cpf030                   |
| cpf028                   |
| cpf034                   |
| cpf033                   |
| cpf032                   |
| cpf027                   |
| cpf029                   |
| sup002                   |
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:
|          ax |
|------------:|
| 1.78968e-06 |
| 6.35338e-05 |
| 4.08981e-06 |
| 6.3017e-09  |
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                                         |
|:------------------------------------------|
| rab value—previous disclosure year        |
| regulatory depreciation                   |
| indexed revaluations                      |
| periodic land revaluations                |
| total revaluations                        |
| assets commissioned (other than below)    |
| assets acquired from a regulated supplier |
| assets acquired from a related party      |
| assets commissioned                       |
| asset disposals (other)                   |
| asset disposals to a regulated supplier   |
| asset disposals to a related party        |
| asset disposals                           |
| lost and found assets adjustment          |
| adjustment resulting from cost allocation |
| standard depreciation                     |
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   |
|:-------------|
| level 2      |
| level 3      |
| level 4      |
| level 5      |
| level 1      |
| level 6      |
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 word pairs in tweet in g10   |
|:---------------------------------|
| liga1_romania_soffeactive        |
| soffeactive_nflcanada            |
| nflcanada_yogagloww              |
| yogagloww_carvelicecream         |
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                     |
|:----------------------|
| bald mountain east #3 |
| bald mountain east #4 |
| bald mountain east #5 |
| baldmtnwest#1         |
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:
|   % of dollar vol. |
|-------------------:|
|         0.0421467  |
|         0.0445773  |
|         0.0338494  |
|         0.0275134  |
|         0.0333973  |
|         0.0204276  |
|         0.0167229  |
|         0.0140484  |
|         0.0230384  |
|         0.0160776  |
|         0.014059   |
|         0.0120587  |
|         0.028742   |
|         0.0140697  |
|         0.0122391  |
|         0.00933046 |
|         0.00952522 |
|         0.0237168  |
|         0.0206245  |
|         0.0194598  |
|         0.0156516  |
|         0.0113314  |
|         0.00980748 |
|         0.00876746 |
|         0.0212909  |
|         0.0111129  |
|         0.0102502  |
|         0.00968556 |
|         0.00933908 |
|         0.00803936 |
|         0.00750953 |
|         0.0070774  |
|         0.00689116 |
|         0.00657719 |
|         0.00616482 |
|         0.00507591 |
|         0.0039378  |
|         0.013819   |
|         0.0135695  |
|         0.00881716 |
|         0.00754574 |
|         0.00706676 |
|         0.00654472 |
|         0.00468279 |
|         0.00458723 |
|         0.00425708 |
|         0.00414112 |
|         0.0040868  |
|         0.00389277 |
|         0.00373027 |
|         0.00372495 |
|         0.00363131 |
|         0.00356424 |
|         0.00353263 |
|         0.00335245 |
|         0.00334075 |
|         0.00331713 |
|         0.0033075  |
|         0.0031396  |
|         0.00311282 |
|         0.00272358 |
|         0.00257594 |
|         0.00251168 |
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:
| credential description   |
|:-------------------------|
| license                  |
| certification & degree   |
| certification & license  |
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       |
|:-----------------|
| african-american |
| hispanic         |
| native american  |
| other            |