Wikipedia Scraper: Wikidata Facts, Summaries and Images

Turn a list of names into clean, structured records for companies, people, places and any other topic on Wikipedia. Give it article titles, Wikipedia links, Wikidata IDs or a search term, and get the article summary, image, categories and the key facts from Wikidata in plain, ready-to-use fields. All data comes from the official Wikipedia and Wikidata APIs. $1 per 1,000 entities.

What can this Wikipedia scraper do?

• Look up anything by title (Apple Inc.), Wikipedia URL in any language, Wikidata ID (Q312) or search term.
• Return readable facts, not codes: a CEO or headquarters city comes back as a name.
• Pick the right value: preferred values first, the most recent dated value for employees, revenue and population, and current office holders for CEO and chair.
• Work in any language edition, with labels falling back to English.
• Add any extra Wikidata property you need, such as P1448 (official name) or P2002 (X username).

What data do you get?

Group	Fields
Identity	label, description, aliases, entityType, instanceOf, wikidataId, wikidataUrl, title, pageUrl, language
Article	summary, thumbnail, originalImage, categories, lastModified
Organizations	founded, foundedBy, headquarters, industry, officialWebsite, ceo, chairperson, parentOrganization, employees, revenue, netIncome, stockExchange, isin, country, logo
People	birthDate, deathDate, birthPlace, deathPlace, occupation, nationality, educatedAt, awards
Places	country, locatedIn, capital, coordinates, population, area
Extras	extraProperties, license

Who uses it?

• Sales and market research teams enriching company lists with founding year, HQ, industry, CEO, website, employees and revenue.
• Data and AI teams building knowledge bases, entity-linking tables or training data with stable Wikidata IDs.
• Publishers, educators and researchers who need short, reliable summaries and images.
• Analysts joining their data to Wikidata IDs, ISINs, coordinates or population figures.

How to use it

1. Click Try for free (or Start).
2. Paste Articles or entities, one per line, and/or a Search term.
3. Choose the Wikipedia language (default en).
4. Run it and export to Excel, CSV or JSON.

Input example

{
  "items": [
    "Apple Inc.",
    "Marie Curie",
    "Q90",
    "https://de.wikipedia.org/wiki/Siemens"
  ],
  "searchTerm": "electric vehicle manufacturer",
  "maxSearchResults": 5,
  "language": "en"
}

Output example

{
  "input": "Apple Inc.",
  "language": "en",
  "title": "Apple Inc.",
  "pageUrl": "https://en.wikipedia.org/wiki/Apple_Inc.",
  "wikidataId": "Q312",
  "wikidataUrl": "https://www.wikidata.org/wiki/Q312",
  "label": "Apple Inc.",
  "description": "American multinational technology company based in Cupertino, California",
  "aliases": [
    "Apple (company)"
  ],
  "entityType": "organization",
  "instanceOf": [
    "enterprise",
    "business",
    "public company"
  ],
  "summary": "Apple Inc. is an American multinational technology company headquartered in Cupertino, California, in Silicon Valley, and known for consumer electronics, software and online services. Founded in 1976 as Apple Computer Co...",
  "thumbnail": "https://thumb.wikimedia.org/wikipedia/commons/thumb/f/fa/Apple_logo_black.svg/500px-Apple_logo_black.svg.png?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail",
  "originalImage": "https://upload.wikimedia.org/wikipedia/commons/f/fa/Apple_logo_black.svg?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=original",
  "founded": "1976-04-01",
  "foundedBy": [
    "Steve Wozniak",
    "Ronald Wayne",
    "Steve Jobs"
  ],
  "headquarters": [
    "Cupertino"
  ],
  "industry": [
    "software industry",
    "consumer electronics industry",
    "digital distribution"
  ],
  "officialWebsite": "https://apple.com/",
  "ceo": [
    "John Ternus"
  ],
  "chairperson": [
    "Arthur D. Levinson"
  ],
  "employees": 164000,
  "revenue": 416161000000,
  "netIncome": 112010000000,
  "stockExchange": [
    "Nasdaq",
    "Tokyo Stock Exchange"
  ],
  "isin": [
    "US0378331005"
  ],
  "awards": [
    "National Design Awards"
  ],
  "country": [
    "United States"
  ],
  "image": "https://commons.wikimedia.org/wiki/Special:FilePath/Apple_park_cupertino_2019.jpg",
  "logo": "https://commons.wikimedia.org/wiki/Special:FilePath/Apple_logo_black.svg",
  "employeesYear": "2022",
  "revenueCurrency": "United States dollar",
  "revenueYear": "2025",
  "netIncomeCurrency": "United States dollar",
  "netIncomeYear": "2025",
  "lastModified": "2026-10-28T12:58:11Z",
  "license": "Wikipedia text: CC BY-SA 4.0 (attribute Wikipedia contributors, link pageUrl). Wikidata facts: CC0."
}

How much does it cost?

$1 per 1,000 entities ($0.001 each), plus a $0.00005 start fee per run.

You look up	You pay
500 company names	$0.50
10,000 Wikidata IDs refreshed monthly	$10.00 a month

Inputs that do not exist are logged, skipped and not charged.

Volume discounts on higher Apify plans. The prices above are for the Apify Free plan. On paid plans the per-result price drops automatically: about 15% lower on Bronze, 25% lower on Silver and 38% lower on Gold and above.

Use it via API, Make, Zapier, n8n or an AI agent

Apify API. Start a run and get the results back in one HTTP request (replace YOUR_API_TOKEN with the token from Apify Console > Settings > Integrations):

curl -X POST "https://api.apify.com/v2/acts/plotpointtech~wikipedia-wikidata-entity-extractor/run-sync-get-dataset-items?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"items": ["Apple Inc.", "Marie Curie", "Q90", "https://de.wikipedia.org/wiki/Siemens"], "searchTerm": "electric vehicle manufacturer", "maxSearchResults": 5, "language": "en"}'

For longer jobs, start the run asynchronously and read the dataset when it finishes. Official apify-client libraries exist for Python and JavaScript; the API tab on this page has ready-made code.

No-code automation. Connect the tool to Make, Zapier, n8n, Google Sheets, Slack, Airtable or any webhook with Apify integrations, and use Schedules in Apify Console to run it hourly, daily or weekly.

AI agents (MCP). Give Claude, ChatGPT, Cursor, VS Code or any MCP-compatible agent direct access to this tool through the hosted Apify MCP server. Add this server URL to your client:

https://mcp.apify.com?tools=plotpointtech/wikipedia-wikidata-entity-extractor

The agent can then call the tool with the same input fields shown above and read the results.

FAQ

Is it legal to use Wikipedia and Wikidata data? Yes, under their licences. Wikidata facts are CC0 (public domain). Wikipedia summaries are CC BY-SA 4.0: if you republish them, credit "Wikipedia contributors", link to the article (pageUrl) and share adaptations under the same licence. Images have their own licences on Wikimedia Commons. The tool follows Wikimedia's API etiquette.

Why are some fields empty? Fields appear only when Wikidata has a value. Coverage is excellent for well-known entities and thinner for small companies.

Can I use other languages? Yes. Set the Wikipedia language (de, fr, es, ja...), or paste URLs from any language edition.

What if a title is ambiguous? Redirects are followed automatically. For exact matches, use a Wikidata ID or the full Wikipedia URL.

Is this good for AI and RAG? Yes. Each record has a stable wikidataId, a clean summary and a source URL for citations.

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Plot Point Technologies is not affiliated with the Wikimedia Foundation, Wikipedia or Wikidata.

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If this tool saved you time, we would be grateful for a short review on this page; it helps other people find it. If something did not work as expected, please open an issue on the Issues tab and we will look into it quickly.