query
stringlengths 1.02k
96.3k
| response
stringlengths 42
25.2k
| rejected_response
stringlengths 39
25.6k
| error_category
stringclasses 3
values | format
stringclasses 2
values | source
stringclasses 9
values |
|---|---|---|---|---|---|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
EU rejects German call to boycott British lamb .
|
{"entities": [{"text": "EU", "type": "organization"}, {"text": "German", "type": "miscellaneous"}, {"text": "British", "type": "miscellaneous"}]}
|
{"entities": [{"text": "EU rejects", "type": "organization"}, {"text": "German call", "type": "miscellaneous"}, {"text": "boycott British", "type": "miscellaneous"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
Peter Blackburn
|
{"entities": [{"text": "Peter Blackburn", "type": "person"}]}
|
{"entities": [{"text": "Peter", "type": "person"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
BRUSSELS 1996-08-22
|
{"entities": [{"text": "BRUSSELS", "type": "location"}]}
|
{"entities": [{"text": "BRUSSELS", "type": "person"}]}
|
wrong_entity_types
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
The European Commission said on Thursday it disagreed with German advice to consumers to shun British lamb until scientists determine whether mad cow disease can be transmitted to sheep .
|
<response>The <organization> European Commission </organization> said on Thursday it disagreed with <miscellaneous> German </miscellaneous> advice to consumers to shun <miscellaneous> British </miscellaneous> lamb until scientists determine whether mad cow disease can be transmitted to sheep .</response>
|
<response>The <organization> European Commission said </organization> on Thursday it disagreed <miscellaneous> with German </miscellaneous> advice to consumers to <miscellaneous> shun British </miscellaneous> lamb until scientists determine whether mad cow disease can be transmitted to sheep .</response>
|
incorrect_spans
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
Germany 's representative to the European Union 's veterinary committee Werner Zwingmann said on Wednesday consumers should buy sheepmeat from countries other than Britain until the scientific advice was clearer .
|
<response><location> Germany </location> 's representative to the <organization> European Union </organization> 's veterinary committee <person> Werner Zwingmann </person> said on Wednesday consumers should buy sheepmeat from countries other than <location> Britain </location> until the scientific advice was clearer .</response>
|
<response><miscellaneous> Germany </miscellaneous> 's representative to the <location> European Union </location> 's veterinary committee <person> Werner Zwingmann </person> said on Wednesday consumers should buy sheepmeat from countries other than <miscellaneous> Britain </miscellaneous> until the scientific advice was clearer .</response>
|
wrong_entity_types
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
" We do n't support any such recommendation because we do n't see any grounds for it , " the Commission 's chief spokesman Nikolaus van der Pas told a news briefing .
|
{"entities": [{"text": "Commission", "type": "organization"}, {"text": "Nikolaus van der Pas", "type": "person"}]}
|
{"entities": [{"text": "Nikolaus van der Pas", "type": "person"}]}
|
missing_entities
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
He said further scientific study was required and if it was found that action was needed it should be taken by the European Union .
|
{"entities": [{"text": "European Union", "type": "organization"}]}
|
{"entities": [{"text": "the European Union", "type": "organization"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
He said a proposal last month by EU Farm Commissioner Franz Fischler to ban sheep brains , spleens and spinal cords from the human and animal food chains was a highly specific and precautionary move to protect human health .
|
<response>He said a proposal last month by <organization> EU </organization> Farm Commissioner <person> Franz Fischler </person> to ban sheep brains , spleens and spinal cords from the human and animal food chains was a highly specific and precautionary move to protect human health .</response>
|
<response>He said a proposal last month by <organization> EU </organization> Farm Commissioner Franz Fischler to ban sheep brains , spleens and spinal cords from the human and animal food chains was a highly specific and precautionary move to protect human health .</response>
|
missing_entities
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
Fischler proposed EU-wide measures after reports from Britain and France that under laboratory conditions sheep could contract Bovine Spongiform Encephalopathy -LPR- BSE -RPR- -- mad cow disease .
|
<response><person> Fischler </person> proposed <miscellaneous> EU-wide </miscellaneous> measures after reports from <location> Britain </location> and <location> France </location> that under laboratory conditions sheep could contract <miscellaneous> Bovine Spongiform Encephalopathy </miscellaneous> -LPR- <miscellaneous> BSE </miscellaneous> -RPR- -- mad cow disease .</response>
|
<response><person> Fischler </person> proposed <organization> EU-wide </organization> measures after reports from <person> Britain </person> and <organization> France </organization> that under laboratory conditions sheep could contract <miscellaneous> Bovine Spongiform Encephalopathy </miscellaneous> -LPR- <person> BSE </person> -RPR- -- mad cow disease .</response>
|
wrong_entity_types
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
But Fischler agreed to review his proposal after the EU 's standing veterinary committee , mational animal health officials , questioned if such action was justified as there was only a slight risk to human health .
|
{"entities": [{"text": "Fischler", "type": "person"}, {"text": "EU", "type": "organization"}]}
|
{"entities": [{"text": "Fischler", "type": "miscellaneous"}, {"text": "EU", "type": "organization"}]}
|
wrong_entity_types
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
Spanish Farm Minister Loyola de Palacio had earlier accused Fischler at an EU farm ministers ' meeting of causing unjustified alarm through " dangerous generalisation . "
|
<response><miscellaneous> Spanish </miscellaneous> Farm Minister <person> Loyola de Palacio </person> had earlier accused <person> Fischler </person> at an <organization> EU </organization> farm ministers ' meeting of causing unjustified alarm through " dangerous generalisation . "</response>
|
<response><miscellaneous> Spanish Farm </miscellaneous> <person> Minister Loyola de Palacio </person> had earlier accused <person> Fischler at </person> an <organization> EU farm </organization> ministers ' meeting of causing unjustified alarm through " dangerous generalisation . "</response>
|
incorrect_spans
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
Only France and Britain backed Fischler 's proposal .
|
{"entities": [{"text": "France", "type": "location"}, {"text": "Britain", "type": "location"}, {"text": "Fischler", "type": "person"}]}
|
{"entities": [{"text": "Only France", "type": "location"}, {"text": "and Britain", "type": "location"}, {"text": "backed Fischler", "type": "person"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
The EU 's scientific veterinary and multidisciplinary committees are due to re-examine the issue early next month and make recommendations to the senior veterinary officials .
|
{"entities": [{"text": "EU", "type": "organization"}]}
|
{"entities": [{"text": "EU", "type": "location"}]}
|
wrong_entity_types
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
Sheep have long been known to contract scrapie , a brain-wasting disease similar to BSE which is believed to have been transferred to cattle through feed containing animal waste .
|
{"entities": [{"text": "BSE", "type": "miscellaneous"}]}
|
{"entities": [{"text": "BSE which", "type": "miscellaneous"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
British farmers denied on Thursday there was any danger to human health from their sheep , but expressed concern that German government advice to consumers to avoid British lamb might influence consumers across Europe .
|
{"entities": [{"text": "British", "type": "miscellaneous"}, {"text": "German", "type": "miscellaneous"}, {"text": "British", "type": "miscellaneous"}, {"text": "Europe", "type": "location"}]}
|
{"entities": [{"text": "British farmers", "type": "miscellaneous"}, {"text": "that German", "type": "miscellaneous"}, {"text": "British lamb", "type": "miscellaneous"}, {"text": "Europe .", "type": "location"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
" What we have to be extremely careful of is how other countries are going to take Germany 's lead , " Welsh National Farmers ' Union -LPR- NFU -RPR- chairman John Lloyd Jones said on BBC radio .
|
{"entities": [{"text": "Germany", "type": "location"}, {"text": "Welsh National Farmers ' Union", "type": "organization"}, {"text": "NFU", "type": "organization"}, {"text": "John Lloyd Jones", "type": "person"}, {"text": "BBC radio", "type": "organization"}]}
|
{"entities": [{"text": "Germany 's", "type": "location"}, {"text": "\" Welsh National Farmers ' Union", "type": "organization"}, {"text": "NFU -RPR-", "type": "organization"}, {"text": "John Lloyd", "type": "person"}, {"text": "on BBC radio", "type": "organization"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
Bonn has led efforts to protect public health after consumer confidence collapsed in March after a British report suggested humans could contract an illness similar to mad cow disease by eating contaminated beef .
|
<response><location> Bonn </location> has led efforts to protect public health after consumer confidence collapsed in March after a <miscellaneous> British </miscellaneous> report suggested humans could contract an illness similar to mad cow disease by eating contaminated beef .</response>
|
<response>Bonn has led efforts to protect public health after consumer confidence collapsed in March after a <miscellaneous> British </miscellaneous> report suggested humans could contract an illness similar to mad cow disease by eating contaminated beef .</response>
|
missing_entities
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
Germany imported 47,600 sheep from Britain last year , nearly half of total imports .
|
<response><location> Germany </location> imported 47,600 sheep from <location> Britain </location> last year , nearly half of total imports .</response>
|
<response><location> Germany imported </location> 47,600 sheep <location> from Britain </location> last year , nearly half of total imports .</response>
|
incorrect_spans
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
It brought in 4,275 tonnes of British mutton , some 10 percent of overall imports .
|
<response>It brought in 4,275 tonnes of <miscellaneous> British </miscellaneous> mutton , some 10 percent of overall imports .</response>
|
<response>It brought in 4,275 tonnes of <miscellaneous> British mutton </miscellaneous> , some 10 percent of overall imports .</response>
|
incorrect_spans
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
Rare Hendrix song draft sells for almost $ 17,000 .
|
{"entities": [{"text": "Hendrix", "type": "person"}]}
|
{"entities": [{"text": "Rare Hendrix", "type": "person"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
LONDON 1996-08-22
|
<response><location> LONDON </location> 1996-08-22</response>
|
<response><location> LONDON 1996-08-22 </location></response>
|
incorrect_spans
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
A rare early handwritten draft of a song by U.S. guitar legend Jimi Hendrix was sold for almost $ 17,000 on Thursday at an auction of some of the late musician 's favourite possessions .
|
<response>A rare early handwritten draft of a song by <location> U.S. </location> guitar legend <person> Jimi Hendrix </person> was sold for almost $ 17,000 on Thursday at an auction of some of the late musician 's favourite possessions .</response>
|
<response>A rare early handwritten draft of a song by <organization> U.S. </organization> guitar legend <person> Jimi Hendrix </person> was sold for almost $ 17,000 on Thursday at an auction of some of the late musician 's favourite possessions .</response>
|
wrong_entity_types
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
A Florida restaurant paid 10,925 pounds -LPR- $ 16,935 -RPR- for the draft of " Ai n't no telling " , which Hendrix penned on a piece of London hotel stationery in late 1966 .
|
<response>A <location> Florida </location> restaurant paid 10,925 pounds -LPR- $ 16,935 -RPR- for the draft of " <miscellaneous> Ai n't no telling </miscellaneous> " , which <person> Hendrix </person> penned on a piece of <location> London </location> hotel stationery in late 1966 .</response>
|
<response>A <miscellaneous> Florida </miscellaneous> restaurant paid 10,925 pounds -LPR- $ 16,935 -RPR- for the draft of " <person> Ai n't no telling </person> " , which <organization> Hendrix </organization> penned on a piece of <miscellaneous> London </miscellaneous> hotel stationery in late 1966 .</response>
|
wrong_entity_types
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
At the end of a January 1967 concert in the English city of Nottingham he threw the sheet of paper into the audience , where it was retrieved by a fan .
|
{"entities": [{"text": "English", "type": "miscellaneous"}, {"text": "Nottingham", "type": "location"}]}
|
{"entities": [{"text": "English city", "type": "miscellaneous"}, {"text": "of Nottingham", "type": "location"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
Buyers also snapped up 16 other items that were put up for auction by Hendrix 's former girlfriend Kathy Etchingham , who lived with him from 1966 to 1969 .
|
{"entities": [{"text": "Hendrix", "type": "person"}, {"text": "Kathy Etchingham", "type": "person"}]}
|
{"entities": [{"text": "Hendrix", "type": "organization"}, {"text": "Kathy Etchingham", "type": "location"}]}
|
wrong_entity_types
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
They included a black lacquer and mother of pearl inlaid box used by Hendrix to store his drugs , which an anonymous Australian purchaser bought for 5,060 pounds -LPR- $ 7,845 -RPR- .
|
<response>They included a black lacquer and mother of pearl inlaid box used by <person> Hendrix </person> to store his drugs , which an anonymous <miscellaneous> Australian </miscellaneous> purchaser bought for 5,060 pounds -LPR- $ 7,845 -RPR- .</response>
|
<response>They included a black lacquer and mother of pearl inlaid box used by <location> Hendrix </location> to store his drugs , which an anonymous <person> Australian </person> purchaser bought for 5,060 pounds -LPR- $ 7,845 -RPR- .</response>
|
wrong_entity_types
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
China says Taiwan spoils atmosphere for talks .
|
{"entities": [{"text": "China", "type": "location"}, {"text": "Taiwan", "type": "location"}]}
|
{"entities": [{"text": "China says", "type": "location"}, {"text": "says Taiwan", "type": "location"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
BEIJING 1996-08-22
|
{"entities": [{"text": "BEIJING", "type": "location"}]}
|
{"entities": [{"text": "BEIJING 1996-08-22", "type": "location"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
China on Thursday accused Taipei of spoiling the atmosphere for a resumption of talks across the Taiwan Strait with a visit to Ukraine by Taiwanese Vice President Lien Chan this week that infuriated Beijing .
|
{"entities": [{"text": "China", "type": "location"}, {"text": "Taipei", "type": "location"}, {"text": "Taiwan Strait", "type": "location"}, {"text": "Ukraine", "type": "location"}, {"text": "Taiwanese", "type": "miscellaneous"}, {"text": "Lien Chan", "type": "person"}, {"text": "Beijing", "type": "location"}]}
|
{"entities": [{"text": "China on", "type": "location"}, {"text": "accused Taipei", "type": "location"}, {"text": "Taiwan", "type": "location"}, {"text": "Ukraine by", "type": "location"}, {"text": "by Taiwanese", "type": "miscellaneous"}, {"text": "Chan", "type": "person"}, {"text": "infuriated Beijing", "type": "location"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
Speaking only hours after Chinese state media said the time was right to engage in political talks with Taiwan , Foreign Ministry spokesman Shen Guofang told Reuters : " The necessary atmosphere for the opening of the talks has been disrupted by the Taiwan authorities . "
|
{"entities": [{"text": "Chinese", "type": "miscellaneous"}, {"text": "Taiwan", "type": "location"}, {"text": "Foreign Ministry", "type": "organization"}, {"text": "Shen Guofang", "type": "person"}, {"text": "Reuters", "type": "organization"}, {"text": "Taiwan", "type": "location"}]}
|
{"entities": [{"text": "Chinese", "type": "miscellaneous"}, {"text": "Taiwan", "type": "miscellaneous"}, {"text": "Foreign Ministry", "type": "location"}, {"text": "Shen Guofang", "type": "person"}, {"text": "Reuters", "type": "person"}, {"text": "Taiwan", "type": "organization"}]}
|
wrong_entity_types
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
State media quoted China 's top negotiator with Taipei , Tang Shubei , as telling a visiting group from Taiwan on Wednesday that it was time for the rivals to hold political talks .
|
{"entities": [{"text": "China", "type": "location"}, {"text": "Taipei", "type": "location"}, {"text": "Tang Shubei", "type": "person"}, {"text": "Taiwan", "type": "location"}]}
|
{"entities": [{"text": "Taipei", "type": "location"}, {"text": "Taiwan", "type": "location"}, {"text": "China", "type": "location"}]}
|
missing_entities
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
that is to end the state of hostility , " Thursday 's overseas edition of the People 's Daily quoted Tang as saying .
|
{"entities": [{"text": "People 's Daily", "type": "organization"}, {"text": "Tang", "type": "person"}]}
|
{"entities": [{"text": "Tang", "type": "person"}]}
|
missing_entities
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
The foreign ministry 's Shen told Reuters Television in an interview he had read reports of Tang 's comments but gave no details of why the negotiator had considered the time right for talks with Taiwan , which Beijing considers a renegade province .
|
{"entities": [{"text": "Shen", "type": "organization"}, {"text": "Reuters Television", "type": "organization"}, {"text": "Tang", "type": "person"}, {"text": "Taiwan", "type": "location"}, {"text": "Beijing", "type": "location"}]}
|
{"entities": [{"text": "'s Shen", "type": "organization"}, {"text": "Television", "type": "organization"}, {"text": "Tang 's", "type": "person"}, {"text": "with Taiwan", "type": "location"}, {"text": "Beijing considers", "type": "location"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
China , which has long opposed all Taipei efforts to gain greater international recognition , was infuriated by a visit to Ukraine this week by Taiwanese Vice President Lien .
|
<response><location> China </location> , which has long opposed all <location> Taipei </location> efforts to gain greater international recognition , was infuriated by a visit to <location> Ukraine </location> this week by <miscellaneous> Taiwanese </miscellaneous> Vice President <person> Lien </person> .</response>
|
<response><location> China , </location> which has long opposed <location> all Taipei </location> efforts to gain greater international recognition , was infuriated by a visit to <location> Ukraine this </location> week <miscellaneous> by Taiwanese </miscellaneous> Vice <person> President Lien </person> .</response>
|
incorrect_spans
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
China says time right for Taiwan talks .
|
{"entities": [{"text": "China", "type": "location"}, {"text": "Taiwan", "type": "location"}]}
|
{"entities": [{"text": "China", "type": "location"}]}
|
missing_entities
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
BEIJING 1996-08-22
|
{"entities": [{"text": "BEIJING", "type": "location"}]}
|
{"entities": [{"text": "BEIJING 1996-08-22", "type": "location"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
China has said it was time for political talks with Taiwan and that the rival island should take practical steps towards that goal .
|
{"entities": [{"text": "China", "type": "location"}, {"text": "Taiwan", "type": "location"}]}
|
{"entities": [{"text": "China", "type": "organization"}, {"text": "Taiwan", "type": "miscellaneous"}]}
|
wrong_entity_types
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
Consultations should be held to set the time and format of the talks , the official Xinhua news agency quoted Tang Shubei , executive vice chairman of the Association for Relations Across the Taiwan Straits , as saying late on Wednesday .
|
<response>Consultations should be held to set the time and format of the talks , the official <organization> Xinhua </organization> news agency quoted <person> Tang Shubei </person> , executive vice chairman of the <organization> Association for Relations Across the Taiwan Straits </organization> , as saying late on Wednesday .</response>
|
<response>Consultations should be held to set the time and format of the talks , the official <person> Xinhua </person> news agency quoted <miscellaneous> Tang Shubei </miscellaneous> , executive vice chairman of the <organization> Association for Relations Across the Taiwan Straits </organization> , as saying late on Wednesday .</response>
|
wrong_entity_types
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
German July car registrations up 14.2 pct yr / yr .
|
{"entities": [{"text": "German", "type": "miscellaneous"}]}
|
{"entities": [{"text": "German", "type": "organization"}]}
|
wrong_entity_types
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
FRANKFURT 1996-08-22
|
{"entities": [{"text": "FRANKFURT", "type": "location"}]}
|
{"entities": [{"text": "FRANKFURT", "type": "person"}]}
|
wrong_entity_types
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
German first-time registrations of motor vehicles jumped 14.2 percent in July this year from the year-earlier period , the Federal office for motor vehicles said on Thursday .
|
<response><miscellaneous> German </miscellaneous> first-time registrations of motor vehicles jumped 14.2 percent in July this year from the year-earlier period , the <organization> Federal office for motor vehicles </organization> said on Thursday .</response>
|
<response><miscellaneous> German </miscellaneous> first-time registrations of motor vehicles jumped 14.2 percent in July this year from the year-earlier period , the <location> Federal office for motor vehicles </location> said on Thursday .</response>
|
wrong_entity_types
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
The growth was partly due to an increased number of Germans buying German cars abroad , while manufacturers said that domestic demand was weak , the federal office said .
|
<response>The growth was partly due to an increased number of <miscellaneous> Germans </miscellaneous> buying <miscellaneous> German </miscellaneous> cars abroad , while manufacturers said that domestic demand was weak , the federal office said .</response>
|
<response>The growth was partly due to an increased number of Germans buying <miscellaneous> German </miscellaneous> cars abroad , while manufacturers said that domestic demand was weak , the federal office said .</response>
|
missing_entities
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
Almost all German car manufacturers posted gains in registration numbers in the period .
|
{"entities": [{"text": "German", "type": "miscellaneous"}]}
|
{"entities": [{"text": "German car", "type": "miscellaneous"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
Volkswagen AG won 77,719 registrations , slightly more than a quarter of the total .
|
<response><organization> Volkswagen AG </organization> won 77,719 registrations , slightly more than a quarter of the total .</response>
|
<response><organization> Volkswagen </organization> AG won 77,719 registrations , slightly more than a quarter of the total .</response>
|
incorrect_spans
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
Opel AG together with General Motors came in second place with 49,269 registrations , 16.4 percent of the overall figure .
|
<response><organization> Opel AG </organization> together with <organization> General Motors </organization> came in second place with 49,269 registrations , 16.4 percent of the overall figure .</response>
|
<response><organization> Opel AG </organization> together with General Motors came in second place with 49,269 registrations , 16.4 percent of the overall figure .</response>
|
missing_entities
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
Third was Ford with 35,563 registrations , or 11.7 percent .
|
<response>Third was <organization> Ford </organization> with 35,563 registrations , or 11.7 percent .</response>
|
<response>Third <organization> was Ford </organization> with 35,563 registrations , or 11.7 percent .</response>
|
incorrect_spans
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
Only Seat and Porsche had fewer registrations in July 1996 compared to last year 's July .
|
<response>Only <organization> Seat </organization> and <organization> Porsche </organization> had fewer registrations in July 1996 compared to last year 's July .</response>
|
<response>Only <organization> Seat </organization> and Porsche had fewer registrations in July 1996 compared to last year 's July .</response>
|
missing_entities
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
Seat posted 3,420 registrations compared with 5522 registrations in July a year earlier .
|
{"entities": [{"text": "Seat", "type": "organization"}]}
|
{"entities": [{"text": "Seat", "type": "location"}]}
|
wrong_entity_types
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
Porsche 's registrations fell to 554 from 643 .
|
{"entities": [{"text": "Porsche", "type": "organization"}]}
|
{"entities": [{"text": "Porsche", "type": "location"}]}
|
wrong_entity_types
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
GREEK SOCIALISTS GIVE GREEN LIGHT TO PM FOR ELECTIONS .
|
<response><miscellaneous> GREEK </miscellaneous> SOCIALISTS GIVE GREEN LIGHT TO PM FOR ELECTIONS .</response>
|
<response><miscellaneous> GREEK SOCIALISTS </miscellaneous> GIVE GREEN LIGHT TO PM FOR ELECTIONS .</response>
|
incorrect_spans
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
ATHENS 1996-08-22
|
<response><location> ATHENS </location> 1996-08-22</response>
|
<response><miscellaneous> ATHENS </miscellaneous> 1996-08-22</response>
|
wrong_entity_types
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
The Greek socialist party 's executive bureau gave the green light to Prime Minister Costas Simitis to call snap elections , its general secretary Costas Skandalidis told reporters .
|
<response>The <miscellaneous> Greek </miscellaneous> socialist party 's executive bureau gave the green light to Prime Minister <person> Costas Simitis </person> to call snap elections , its general secretary <person> Costas Skandalidis </person> told reporters .</response>
|
<response>The <location> Greek </location> socialist party 's executive bureau gave the green light to Prime Minister <person> Costas Simitis </person> to call snap elections , its general secretary <organization> Costas Skandalidis </organization> told reporters .</response>
|
wrong_entity_types
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
Prime Minister Costas Simitis is going to make an official announcement after a cabinet meeting later on Thursday , said Skandalidis .
|
<response>Prime Minister <person> Costas Simitis </person> is going to make an official announcement after a cabinet meeting later on Thursday , said <person> Skandalidis </person> .</response>
|
<response>Prime Minister <person> Costas Simitis </person> is going to make an official announcement after a cabinet meeting later on Thursday , said Skandalidis .</response>
|
missing_entities
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
-- Dimitris Kontogiannis , Athens Newsroom +301 3311812-4
|
{"entities": [{"text": "Dimitris Kontogiannis", "type": "person"}, {"text": "Athens Newsroom", "type": "organization"}]}
|
{"entities": [{"text": "Dimitris Kontogiannis", "type": "person"}]}
|
missing_entities
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
BayerVB sets C$ 100 million six-year bond .
|
<response><organization> BayerVB </organization> sets <miscellaneous> C$ </miscellaneous> 100 million six-year bond .</response>
|
<response><organization> BayerVB <miscellaneous> </organization> sets C$ </miscellaneous> 100 million six-year bond .</response>
|
incorrect_spans
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
LONDON 1996-08-22
|
<response><location> LONDON </location> 1996-08-22</response>
|
<response><location> LONDON 1996-08-22 </location></response>
|
incorrect_spans
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
The following bond was announced by lead manager Toronto Dominion .
|
{"entities": [{"text": "Toronto Dominion", "type": "person"}]}
|
{"entities": [{"text": "Toronto", "type": "person"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
BORROWER BAYERISCHE VEREINSBANK
|
{"entities": [{"text": "BAYERISCHE VEREINSBANK", "type": "organization"}]}
|
{"entities": [{"text": "BAYERISCHE", "type": "organization"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
AMT C$ 100 MLN COUPON 6.625 MATURITY 24.SEP.02
|
{"entities": [{"text": "C$", "type": "miscellaneous"}]}
|
{"entities": [{"text": "C$", "type": "organization"}]}
|
wrong_entity_types
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
S&P = DENOMS -LPR- K -RPR- 1-10-100 SALE LIMITS US / UK / CA
|
{"entities": [{"text": "S&P", "type": "organization"}, {"text": "US", "type": "location"}, {"text": "UK", "type": "location"}, {"text": "CA", "type": "location"}]}
|
{"entities": [{"text": "S&P", "type": "organization"}, {"text": "UK", "type": "location"}, {"text": "CA", "type": "location"}]}
|
missing_entities
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
GOV LAW GERMAN HOME CTRY = TAX PROVS STANDARD
|
{"entities": [{"text": "GERMAN", "type": "miscellaneous"}]}
|
{"entities": [{"text": "LAW GERMAN", "type": "miscellaneous"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
NOTES BAYERISCHE VEREINSBANK IS JOINT LEAD MANAGER
|
{"entities": [{"text": "BAYERISCHE VEREINSBANK", "type": "organization"}]}
|
{"entities": [{"text": "BAYERISCHE", "type": "organization"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
-- London Newsroom +44 171 542 7658
|
<response>-- <organization> London Newsroom </organization> +44 171 542 7658</response>
|
<response>-- <organization> London Newsroom +44 </organization> 171 542 7658</response>
|
incorrect_spans
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
Venantius sets $ 300 million January 1999 FRN .
|
<response><organization> Venantius </organization> sets $ 300 million January 1999 FRN .</response>
|
<response><miscellaneous> Venantius </miscellaneous> sets $ 300 million January 1999 FRN .</response>
|
wrong_entity_types
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
LONDON 1996-08-22
|
{"entities": [{"text": "LONDON", "type": "location"}]}
|
{"entities": [{"text": "LONDON", "type": "organization"}]}
|
wrong_entity_types
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
The following floating-rate issue was announced by lead manager Lehman Brothers International .
|
<response>The following floating-rate issue was announced by lead manager <organization> Lehman Brothers International </organization> .</response>
|
<response>The following floating-rate issue was announced by lead manager Lehman <organization> Brothers International </organization> .</response>
|
incorrect_spans
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
TYPE FRN BASE 3M LIBOR PAY DATE S23.SEP.96
|
<response>TYPE FRN BASE <organization> 3M </organization> LIBOR PAY DATE S23.SEP.96</response>
|
<response>TYPE FRN <organization> BASE 3M </organization> LIBOR PAY DATE S23.SEP.96</response>
|
incorrect_spans
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
LAST S&P AA+ REOFFER =
|
{"entities": [{"text": "S&P", "type": "organization"}]}
|
{"entities": [{"text": "LAST S&P", "type": "organization"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
GOV LAW ENGLISH HOME CTRY SWEDEN TAX PROVS STANDARD
|
<response>GOV LAW <miscellaneous> ENGLISH </miscellaneous> HOME CTRY <location> SWEDEN </location> TAX PROVS STANDARD</response>
|
<response>GOV LAW <miscellaneous> ENGLISH </miscellaneous> HOME CTRY <person> SWEDEN </person> TAX PROVS STANDARD</response>
|
wrong_entity_types
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
-- London Newsroom +44 171 542 8863
|
{"entities": [{"text": "London Newsroom", "type": "organization"}]}
|
{"entities": [{"text": "London Newsroom", "type": "person"}]}
|
wrong_entity_types
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
Port conditions update - Syria - Lloyds Shipping .
|
<response>Port conditions update - <location> Syria </location> - <organization> Lloyds Shipping </organization> .</response>
|
<response>Port conditions update - <location> Syria </location> - Lloyds Shipping .</response>
|
missing_entities
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
Port conditions from Lloyds Shipping Intelligence Service --
|
<response>Port conditions from <organization> Lloyds Shipping Intelligence Service </organization> --</response>
|
<response>Port conditions from <miscellaneous> Lloyds Shipping Intelligence Service </miscellaneous> --</response>
|
wrong_entity_types
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
LATTAKIA , Aug 10 - waiting time at Lattakia and Tartous presently 24 hours .
|
{"entities": [{"text": "LATTAKIA", "type": "location"}, {"text": "Lattakia", "type": "location"}, {"text": "Tartous", "type": "location"}]}
|
{"entities": [{"text": "LATTAKIA", "type": "person"}, {"text": "Lattakia", "type": "person"}, {"text": "Tartous", "type": "person"}]}
|
wrong_entity_types
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
Israel plays down fears of war with Syria .
|
<response><location> Israel </location> plays down fears of war with <location> Syria </location> .</response>
|
<response><location> Israel plays </location> down fears of war <location> with Syria </location> .</response>
|
incorrect_spans
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
Colleen Siegel
|
<response><person> Colleen Siegel </person></response>
|
<response>Colleen <person> Siegel </person></response>
|
incorrect_spans
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
JERUSALEM 1996-08-22
|
{"entities": [{"text": "JERUSALEM", "type": "location"}]}
|
{"entities": [{"text": "JERUSALEM", "type": "organization"}]}
|
wrong_entity_types
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
Israel 's outgoing peace negotiator with Syria said on Thursday current tensions between the two countries appeared to be a storm in a teacup .
|
{"entities": [{"text": "Israel", "type": "location"}, {"text": "Syria", "type": "location"}]}
|
{"entities": [{"text": "Israel", "type": "location"}, {"text": "Syria", "type": "miscellaneous"}]}
|
wrong_entity_types
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
Itamar Rabinovich , who as Israel 's ambassador to Washington conducted unfruitful negotiations with Syria , told Israel Radio it looked like Damascus wanted to talk rather than fight .
|
<response><person> Itamar Rabinovich </person> , who as <location> Israel </location> 's ambassador to <location> Washington </location> conducted unfruitful negotiations with <location> Syria </location> , told <organization> Israel Radio </organization> it looked like <location> Damascus </location> wanted to talk rather than fight .</response>
|
<response><person> Itamar Rabinovich , </person> who as <location> Israel 's </location> ambassador to <location> Washington conducted </location> unfruitful negotiations with <location> Syria , </location> told <organization> Israel Radio it </organization> looked <location> like Damascus </location> wanted to talk rather than fight .</response>
|
incorrect_spans
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
" It appears to me the Syrian priority is still to negotiate .
|
{"entities": [{"text": "Syrian", "type": "miscellaneous"}]}
|
{"entities": [{"text": "the Syrian", "type": "miscellaneous"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
The Syrians are confused , they are definitely tense , but the general assessment here in Washington is that this is essentially a storm in a teacup , " he said .
|
{"entities": [{"text": "Syrians", "type": "miscellaneous"}, {"text": "Washington", "type": "location"}]}
|
{"entities": [{"text": "The Syrians", "type": "miscellaneous"}, {"text": "in Washington", "type": "location"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
Rabinovich is winding up his term as ambassador .
|
{"entities": [{"text": "Rabinovich", "type": "person"}]}
|
{"entities": [{"text": "Rabinovich is", "type": "person"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
He will be replaced by Eliahu Ben-Elissar , a former Israeli envoy to Egypt and right-wing Likud party politician .
|
{"entities": [{"text": "Eliahu Ben-Elissar", "type": "person"}, {"text": "Israeli", "type": "miscellaneous"}, {"text": "Egypt", "type": "location"}, {"text": "Likud", "type": "organization"}]}
|
{"entities": [{"text": "Ben-Elissar", "type": "person"}, {"text": "Israeli envoy", "type": "miscellaneous"}, {"text": "to Egypt", "type": "location"}, {"text": "right-wing Likud", "type": "organization"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
Israel on Wednesday sent Syria a message , via Washington , saying it was committed to peace and wanted to open negotiations without preconditions .
|
{"entities": [{"text": "Israel", "type": "location"}, {"text": "Syria", "type": "location"}, {"text": "Washington", "type": "location"}]}
|
{"entities": [{"text": "Israel on", "type": "location"}, {"text": "sent Syria", "type": "location"}, {"text": "Washington ,", "type": "location"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
But it slammed Damascus for creating what it called a dangerous atmosphere .
|
{"entities": [{"text": "Damascus", "type": "location"}]}
|
{"entities": [{"text": "slammed Damascus", "type": "location"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
Syria accused Israel on Wednesday of launching a hysterical campaign against it after Israeli television reported that Damascus had recently test fired a missile .
|
{"entities": [{"text": "Syria", "type": "location"}, {"text": "Israel", "type": "location"}, {"text": "Israeli", "type": "miscellaneous"}, {"text": "Damascus", "type": "location"}]}
|
{"entities": [{"text": "Syria", "type": "location"}, {"text": "Israel", "type": "location"}, {"text": "Israeli", "type": "organization"}, {"text": "Damascus", "type": "location"}]}
|
wrong_entity_types
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
" The message that we sent to -LPR- Syrian President Hafez al- -RPR- Assad is that Israel is ready at any time without preconditions to enter peace negotiations , " Israeli Foreign Minister David Levy told Israel Radio in an interview .
|
{"entities": [{"text": "Syrian", "type": "miscellaneous"}, {"text": "Hafez al-", "type": "person"}, {"text": "Assad", "type": "person"}, {"text": "Israel", "type": "location"}, {"text": "Israeli", "type": "miscellaneous"}, {"text": "David Levy", "type": "person"}, {"text": "Israel Radio", "type": "organization"}]}
|
{"entities": [{"text": "Syrian President", "type": "miscellaneous"}, {"text": "President Hafez al-", "type": "person"}, {"text": "Assad is", "type": "person"}, {"text": "that Israel", "type": "location"}, {"text": "Israeli Foreign", "type": "miscellaneous"}, {"text": "Minister David Levy", "type": "person"}, {"text": "Israel", "type": "organization"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
Tension has mounted since Israeli Prime Minister Benjamin Netanyahu took office in June vowing to retain the Golan Heights Israel captured from Syria in the 1967 Middle East war .
|
{"entities": [{"text": "Israeli", "type": "miscellaneous"}, {"text": "Benjamin Netanyahu", "type": "person"}, {"text": "Golan Heights", "type": "location"}, {"text": "Israel", "type": "location"}, {"text": "Syria", "type": "location"}, {"text": "Middle East", "type": "location"}]}
|
{"entities": [{"text": "Israel", "type": "location"}, {"text": "Golan Heights", "type": "location"}]}
|
missing_entities
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
Israeli-Syrian peace talks have been deadlocked over the Golan since 1991 despite the previous government 's willingness to make Golan concessions .
|
<response><miscellaneous> Israeli-Syrian </miscellaneous> peace talks have been deadlocked over the <location> Golan </location> since 1991 despite the previous government 's willingness to make <location> Golan </location> concessions .</response>
|
<response>Israeli-Syrian peace talks have been deadlocked over the <location> Golan </location> since 1991 despite the previous government 's willingness to make Golan concessions .</response>
|
missing_entities
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
" The voices coming out of Damascus are bad , not good .
|
{"entities": [{"text": "Damascus", "type": "location"}]}
|
{"entities": [{"text": "of Damascus", "type": "location"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
this artificial atmosphere is very dangerous because those who spread it could become its prisoners , " Levy said .
|
<response>this artificial atmosphere is very dangerous because those who spread it could become its prisoners , " <person> Levy </person> said .</response>
|
<response>this artificial atmosphere is very dangerous because those who spread it could become its prisoners , " <person> Levy said </person> .</response>
|
incorrect_spans
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
" We expect from Syria , if its face is to peace , that it will answer Israel 's message to enter peace negotiations because that is our goal , " he said . "
|
<response>" We expect from <location> Syria </location> , if its face is to peace , that it will answer <location> Israel </location> 's message to enter peace negotiations because that is our goal , " he said . "</response>
|
<response>" We expect from <location> Syria , </location> if its face is to peace , that it will <location> answer Israel </location> 's message to enter peace negotiations because that is our goal , " he said . "</response>
|
incorrect_spans
|
inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
We do not want a war , God forbid .
|
{"entities": [{"text": "God", "type": "person"}]}
|
{"entities": [{"text": "God forbid", "type": "person"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
Israel 's Channel Two television said Damascus had sent a " calming signal " to Israel .
|
{"entities": [{"text": "Israel", "type": "location"}, {"text": "Channel Two", "type": "organization"}, {"text": "Damascus", "type": "location"}, {"text": "Israel", "type": "location"}]}
|
{"entities": [{"text": "Israel 's", "type": "location"}, {"text": "Channel", "type": "organization"}, {"text": "Damascus had", "type": "location"}, {"text": "to Israel", "type": "location"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
Netanyahu and Levy 's spokesmen said they could not confirm it .
|
{"entities": [{"text": "Netanyahu", "type": "person"}, {"text": "Levy", "type": "person"}]}
|
{"entities": [{"text": "Netanyahu and", "type": "person"}, {"text": "and Levy", "type": "person"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
The television also said that Netanyahu had sent messages to reassure Syria via Cairo , the United States and Moscow .
|
{"entities": [{"text": "Netanyahu", "type": "person"}, {"text": "Syria", "type": "location"}, {"text": "Cairo", "type": "location"}, {"text": "United States", "type": "location"}, {"text": "Moscow", "type": "location"}]}
|
{"entities": [{"text": "Moscow", "type": "location"}, {"text": "Cairo", "type": "location"}]}
|
missing_entities
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
Polish diplomat denies nurses stranded in Libya .
|
{"entities": [{"text": "Polish", "type": "miscellaneous"}, {"text": "Libya", "type": "location"}]}
|
{"entities": [{"text": "Polish diplomat", "type": "miscellaneous"}, {"text": "Libya .", "type": "location"}]}
|
incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
TUNIS 1996-08-22
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<response><location> TUNIS </location> 1996-08-22</response>
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<response><location> TUNIS 1996-08-22 </location></response>
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incorrect_spans
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inline
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extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
A Polish diplomat on Thursday denied a Polish tabloid report this week that Libya was refusing exit visas to 100 Polish nurses trying to return home after working in the North African country .
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<response>A <miscellaneous> Polish </miscellaneous> diplomat on Thursday denied a <miscellaneous> Polish </miscellaneous> tabloid report this week that <location> Libya </location> was refusing exit visas to 100 <miscellaneous> Polish </miscellaneous> nurses trying to return home after working in the <miscellaneous> North African </miscellaneous> country .</response>
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<response>A Polish diplomat on Thursday denied a <miscellaneous> Polish </miscellaneous> tabloid report this week that <location> Libya </location> was refusing exit visas to 100 Polish nurses trying to return home after working in the <miscellaneous> North African </miscellaneous> country .</response>
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missing_entities
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inline
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and identify all named entities in JSON format.
For example:
Text: "John Smith works at Google in Mountain View."
JSON output:
{
"entities": [
{"text": "John Smith", "type": "person"},
{"text": "Google", "type": "organization"},
{"text": "Mountain View", "type": "location"}
]
}
Available entity types: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Return the entities in valid JSON format as shown in the example.
2. Include ALL entities you can identify with their exact text and type.
3. Do not include explanations, just the JSON object.
4. If entity spans overlap, choose the most specific entity type.
5. Ensure the JSON is properly formatted and valid.
Text to process:
Up to today , we have no knowledge of any nurse stranded or kept in Libya without her will , and we have not received any complaint , " the Polish embassy 's charge d'affaires in Tripoli , Tadeusz Awdankiewicz , told Reuters by telephone .
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{"entities": [{"text": "Libya", "type": "location"}, {"text": "Polish", "type": "miscellaneous"}, {"text": "Tripoli", "type": "location"}, {"text": "Tadeusz Awdankiewicz", "type": "person"}, {"text": "Reuters", "type": "organization"}]}
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{"entities": [{"text": "in Libya", "type": "location"}, {"text": "Polish embassy", "type": "miscellaneous"}, {"text": "Tripoli ,", "type": "location"}, {"text": "Tadeusz Awdankiewicz ,", "type": "person"}, {"text": "told Reuters", "type": "organization"}]}
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incorrect_spans
|
json
|
extraordinarylab/conll2003
|
You are a named entity recognition system that identifies the following entity types:
- person: Names of people, including first and last names
- organization: Names of companies, institutions, organizations, etc.
- location: Names of locations, cities, states, countries, etc.
- miscellaneous: Miscellaneous entities not in the above categories
Process the provided text and mark all named entities with XML-style tags.
For example:
<person>John Smith</person> works at <organization>Google</organization> in <location>Mountain View</location>.
Available entity tags: person, organization, location, miscellaneous
INSTRUCTIONS:
1. Wrap your entire response in <response>...</response> tags.
2. Inside these tags, include the original text with entity tags inserted.
3. Do not change the original text in any way (preserve spacing, punctuation, case, etc.).
4. Tag ALL entities you can identify using the exact tag names provided.
5. Do not include explanations, just the tagged text.
6. If entity spans overlap, choose the most specific entity type.
7. Ensure every opening tag has a matching closing tag.
Text to process:
Poland 's labour ministry said this week it would send a team to Libya to investigate , but Awdankiewicz said the probe was prompted by some nurses complaining about their work conditions such as non-payment of their salaries .
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<response><location> Poland </location> 's labour ministry said this week it would send a team to <location> Libya </location> to investigate , but <person> Awdankiewicz </person> said the probe was prompted by some nurses complaining about their work conditions such as non-payment of their salaries .</response>
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<response><location> Poland 's </location> labour ministry said this week it would send a team <location> to Libya </location> to investigate , <person> but Awdankiewicz </person> said the probe was prompted by some nurses complaining about their work conditions such as non-payment of their salaries .</response>
|
incorrect_spans
|
inline
|
extraordinarylab/conll2003
|
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