Wikidata Entity Scraper 🌐

Scrape Wikidata at scale — search the world's largest free knowledge graph or fetch structured entity data (labels, descriptions, aliases, "instance of" types and claim counts) for any Q-ID. Clean JSON output, no API key, ready for AI training pipelines, data enrichment and research.

Wikidata is the structured-data backbone behind Wikipedia, Google's Knowledge Graph, Siri, Alexa and thousands of apps. This actor turns it into a simple, paginated, machine-readable feed.

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✨ What it does

Two modes, one actor:

Mode	Input	Output
search	A text query (e.g. albert einstein)	Matching entities: id, label, description, url
entities	A list of Q-IDs (e.g. ["Q42","Q64"])	Full records: label, description, aliases, instance_of (P31), claim_count, url

Every item is timestamped (scraped_at) and links straight to wikidata.org.

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🚀 Example input

Search mode:

{
  "mode": "search",
  "query": "albert einstein",
  "language": "en",
  "maxItems": 50
}

Entities mode:

{
  "mode": "entities",
  "entityIds": ["Q42", "Q64", "Q937"],
  "language": "en",
  "maxItems": 100
}

📦 Example output

Search result:

{
  "id": "Q937",
  "label": "Albert Einstein",
  "description": "German-born theoretical physicist (1879–1955)",
  "url": "http://www.wikidata.org/entity/Q937",
  "scraped_at": "2026-07-03T00:00:00Z"
}

Entity result:

{
  "id": "Q42",
  "label": "Douglas Adams",
  "description": "English writer and humorist (1952–2001)",
  "aliases": ["Douglas Noel Adams", "Douglas Noël Adams"],
  "instance_of": ["Q5"],
  "claim_count": 305,
  "url": "https://www.wikidata.org/wiki/Q42",
  "scraped_at": "2026-07-03T00:00:00Z"
}

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🎯 Use cases

• Knowledge graphs — build or enrich your own graph with canonical entity IDs, types and relationships.
• AI / LLM training — harvest clean, licensed, structured facts for grounding, RAG and fine-tuning datasets.
• Data enrichment — resolve messy names to stable Q-IDs, then attach descriptions, aliases and categories.
• Research & journalism — pull structured facts about people, places, organisations and works at scale.

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💸 Pricing & cost

This actor is lightweight and runs on 512 MB. Wikidata's API is free, so your only cost is compute.

• ~$1 per 1,000 items (search results or entities) as a rough guide.
• Fetching entities is batched 50 per request, keeping runs fast and cheap.
• No proxies required — the Wikidata API is datacenter-friendly.

Actual cost depends on maxItems and the Apify plan you run on.

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⚙️ Input reference

Field	Type	Default	Description
mode	select	search	search or entities
query	string	albert einstein	Text query (search mode)
language	string	en	Language code for labels/descriptions/aliases
entityIds	array	–	List of Q-IDs (entities mode)
maxItems	integer	100	Max items to return (1–500)

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🆚 How it compares

• vs. raw SPARQL / Query Service — no query language to learn, no timeouts on big result sets, and output lands directly in an Apify dataset you can export to JSON/CSV/Excel.
• vs. writing your own client — pagination, batching, retries, curl_cffi impersonation and schema-validated output are handled for you.
• vs. generic scrapers — purpose-built for Wikidata's entity model (Q-IDs, P31, claims), not brittle HTML parsing.

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🔗 Related actors

• Wikipedia Scraper
• Dictionary Scraper
• Geocoding Scraper
• MusicBrainz Scraper

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🛟 Support

Questions, feature requests or a bug? Open an issue on the actor page and we'll respond quickly. Custom fields, extra properties or bulk plans are available on request.

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Keywords: wikidata scraper, knowledge graph api, wikidata entities, q-id lookup, structured data, entity extraction, ai training data, semantic web, wikibase, linked data