agentlans/wikipedia-first-paragraph-ner

Wikipedia First Paragraph NER

This dataset contains named entities extracted from the first paragraph of every article in the agentlans/wikipedia-first-paragraph Wikipedia dataset. Entity recognition was performed using spaCy’s en_core_web_sm, zh_core_web_sm, ja_core_news_sm models.

Each record corresponds to one Wikipedia article and includes the article title along with lists of entities grouped by their type.

Example:

{
  "title": "Moot Hall, Brampton",
  "CARDINAL": [],
  "DATE": [],
  "EVENT": [],
  "FAC": [],
  "GPE": ["Brampton", "England", "Cumbria"],
  "LANGUAGE": [],
  "LAW": [],
  "LOC": [],
  "MONEY": [],
  "NORP": [],
  "ORDINAL": [],
  "ORG": ["Brampton Parish Council", "The Market Place"],
  "PERCENT": [],
  "PERSON": ["The Moot Hall"],
  "PRODUCT": [],
  "QUANTITY": [],
  "TIME": [],
  "WORK_OF_ART": []
}

Field Descriptions

Field	Description
title	Title of the Wikipedia article.
CARDINAL	Numerals that do not fall under another type.
DATE	Absolute or relative dates or time periods.
EVENT	Named events such as wars, battles, or sports competitions.
FAC	Facilities such as buildings, airports, highways, or bridges.
GPE	Geopolitical entities: countries, cities, states.
LANGUAGE	Named languages.
LAW	Named legal documents or statutes.
LOC	Non-GPE locations, including mountains and bodies of water.
MONEY	Monetary values including currency units.
NORP	Nationalities, religious groups, or political groups.
ORDINAL	Ordinal numbers (e.g., “first”, “second”).
ORG	Organizations, companies, institutions, or agencies.
PERCENT	Percent expressions including “%”.
PERSON	Individual people, including fictional characters.
PRODUCT	Physical products such as objects, vehicles, or foods.
QUANTITY	Measured quantities (e.g., weight, distance).
TIME	Specific times smaller than a day.
WORK_OF_ART	Creative works such as book, song, or film titles.

Limitations

• Many Wikipedia articles are short or stubs, resulting in limited content for entity extraction.
• spaCy’s models may produce both false positives and false negatives.
• Misclassifications are possible (for example, botanical names detected as PERSON, or person names as ORG).
• Data originates from a static Wikipedia dump and may not reflect the latest content.

Licence

Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)