# Wikidata Entity Scraper (`bgfc97/wikidata-entity-scraper`) Actor

Extract structured knowledge-graph data from Wikidata — labels, descriptions, aliases, statements/claims (properties and values) and Wikipedia sitelinks — for any entity, by search or by Q-ID. Official API, no key, no proxy.

- **URL**: https://apify.com/bgfc97/wikidata-entity-scraper.md
- **Developed by:** [Bruno](https://apify.com/bgfc97) (community)
- **Categories:** Developer tools, AI, Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.75 / 1,000 entity scrapeds

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Wikidata Entity Scraper 🧩

Extract **structured knowledge-graph data** from **Wikidata** — the free database behind Wikipedia. Get labels, descriptions, aliases, statements (properties → values) and Wikipedia sitelinks for any entity, by search or by Q-ID.

Perfect for **data enrichment, entity resolution, knowledge graphs, RAG / AI training data, and building structured datasets** about people, companies, places and concepts.

### What it extracts (one row per entity)

- `id`, `label`, `description`, `aliases`
- `claims` — property IDs (P-numbers) mapped to their values (`claim_count`)
- `sitelinks` — Wikipedia pages in every language (`sitelink_count`)
- `wikipedia_url`, `url`

### Input

```json
{
  "searchQueries": ["Apple Inc", "Albert Einstein"],
  "entityIds": ["Q312", "Q42"],
  "language": "en",
  "includeClaims": true,
  "maxResultsPerQuery": 5
}
```

- **searchQueries** — find entities by name.
- **entityIds** — exact Q-IDs (e.g. `Q312` = Apple Inc.).
- **includeClaims** — include the entity's statements (properties → values).

### Output example

```json
{
  "type": "entity",
  "id": "Q312",
  "label": "Apple Inc.",
  "description": "American multinational technology company",
  "aliases": ["Apple", "Apple Computer"],
  "claim_count": 256,
  "claims": { "P159": ["Q16552"], "P452": ["Q880071"], "P1128": ["164000"] },
  "sitelink_count": 183,
  "wikipedia_url": "https://en.wikipedia.org/wiki/Apple_Inc.",
  "url": "https://www.wikidata.org/wiki/Q312"
}
```

### Notes

- Uses the **official Wikidata API** — free, no key.
- Property IDs (P-numbers) and entity values (Q-numbers) follow the Wikidata data model.
- No proxy required.

# Actor input Schema

## `searchQueries` (type: `array`):

Names/terms to find matching Wikidata entities (people, companies, places, concepts...).

## `entityIds` (type: `array`):

Specific Wikidata entity IDs to fetch, e.g. 'Q312' (Apple Inc.), 'Q42' (Douglas Adams).

## `language` (type: `string`):

Language code for labels/descriptions, e.g. 'en', 'pt', 'es', 'de'.

## `includeClaims` (type: `boolean`):

If ON, include the entity's statements (property IDs mapped to their values).

## `maxResultsPerQuery` (type: `integer`):

How many matching entities to fetch per search query.

## Actor input object example

```json
{
  "searchQueries": [
    "Apple Inc"
  ],
  "entityIds": [],
  "language": "en",
  "includeClaims": true,
  "maxResultsPerQuery": 5
}
```

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "searchQueries": [
        "Apple Inc"
    ],
    "entityIds": []
};

// Run the Actor and wait for it to finish
const run = await client.actor("bgfc97/wikidata-entity-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "searchQueries": ["Apple Inc"],
    "entityIds": [],
}

# Run the Actor and wait for it to finish
run = client.actor("bgfc97/wikidata-entity-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "searchQueries": [
    "Apple Inc"
  ],
  "entityIds": []
}' |
apify call bgfc97/wikidata-entity-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=bgfc97/wikidata-entity-scraper",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/bBRwXLSMjgs0KN5zz/builds/PomeU11n1Wx1NkBd3/openapi.json