Wikidata Scraper — Entities, Labels & Statements

Search Wikidata and export structured knowledge data. Look up entities by keyword or fetch specific Q-IDs, and get one row per entity with its ID, label, description, aliases, Wikipedia link, sitelink count and full statements (claims) — in any language. Export to JSON, CSV or Excel, or pull straight from the API.

Perfect for knowledge graphs, data enrichment, entity linking, research, reference datasets, and feeding structured facts to LLMs and RAG pipelines.

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What you get

One flat, ready-to-use record per entity:

Field	Description
id	Wikidata entity ID (e.g. Q42)
type	Entity type (item / property)
label	Label in your chosen language
description	Short description
aliases	Alternative names
url	Wikidata page
wikipediaTitle / wikipediaUrl	Linked Wikipedia article
siteLinkCount	Number of linked wikis
claims	Statements as a map of property ID → values (e.g. P31 = instance of)
query	The search term that produced the row
collectedAt	When the row was scraped

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How to use

1. By search term or Q-ID

{
  "searchQueries": ["Douglas Adams", "Berlin"],
  "entityIds": ["Q42"],
  "language": "en",
  "resultsPerQuery": 5,
  "includeClaims": true,
  "maxItems": 200
}

• Search terms — each returns its top matching entities.
• Entity IDs — fetch specific Q-IDs (or Wikidata URLs) directly.
• Language — labels/descriptions/aliases in this language (falls back to English).
• Results per search term — how many entities per term.
• Include statements (claims) — add the property → values map.
• Max entities — cap the total (0 = no limit).

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Example output

{
  "id": "Q42",
  "label": "Douglas Adams",
  "description": "British science fiction writer and humourist",
  "aliases": ["Douglas Noël Adams"],
  "url": "https://www.wikidata.org/wiki/Q42",
  "wikipediaUrl": "https://en.wikipedia.org/wiki/Douglas_Adams",
  "siteLinkCount": 140,
  "claims": { "P31": ["Q5"], "P106": ["Q36180", "Q6625963"] },
  "collectedAt": "2026-09-20T16:00:00.000Z"
}

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Common use cases

• Knowledge graphs — build or enrich graphs with entities and their relationships.
• Data enrichment — resolve names to canonical IDs, labels and facts.
• Entity linking — map your data to Wikidata IDs and Wikipedia articles.
• Research & reference — pull structured facts across many entities at once.
• LLMs & RAG — feed clean, structured knowledge into models and pipelines.

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Frequently asked questions

Do I need any login, account or key? No. Just add search terms or Q-IDs and run.

Can I get data in other languages? Yes — set the Language field (it falls back to English when a label is missing).

What are claims? Wikidata statements, returned as a map of property ID (like P31 = "instance of") to its values. Turn off "Include statements" for a lighter output.

Can I fetch exact entities? Yes — put their Q-IDs (or Wikidata URLs) in Entity IDs.

What format is the data? A flat table you can export as JSON, CSV or Excel, or fetch from the Apify API.

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Related scrapers

Building a knowledge or research toolkit? Check out our other data and content scrapers on Apify Store — all with the same clean, one-row-per-item output.