# Wikidata Entity Lookup & Knowledge Graph Scraper (Free) (`fit_melon/wikidata-entity-lookup`) Actor Search Wikidata and export entities as clean JSON: Q-ID, label, description, aliases, instance of, country, coordinates, population, dates and Wikipedia links. Free knowledge graph enrichment. - **URL**: https://apify.com/fit\_melon/wikidata-entity-lookup.md - **Developed by:** [D N](https://apify.com/fit_melon) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use 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 Lookup & Knowledge Graph Scraper (Free) Turn any name into a structured **knowledge graph record**. This actor searches [Wikidata](https://www.wikidata.org) — the free knowledge base behind Wikipedia — and returns clean JSON for each entity: **Q-ID, label, description, aliases, instance of, country, coordinates, population, inception, birth/death dates, occupation, official website, image and the Wikipedia link**. Linked items are **resolved to human-readable labels**, so you get `"country": "France"` instead of a bare `Q142`. Accepts plain search terms *or* exact Q-IDs. Available in any language. This actor is **100% free** — you only pay your own Apify platform usage. ### Input | Field | Type | Default | Description | |---|---|---|---| | `queries` | array of strings | *(required)* | Search terms (`Paris`) or exact Q-IDs (`Q90`). One item per entry. | | `language` | string | `en` | Language for labels, descriptions and the Wikipedia sitelink. | | `resolveLabels` | boolean | `true` | Resolve referenced Q-IDs to readable labels. | ### Output One dataset item per query, always with an explicit `status` field (`found`, `not_found` or `error`) — the actor never crashes on an unknown name. ```json { "status": "found", "query": "Paris", "qid": "Q90", "label": "Paris", "description": "capital and most populous city in France", "aliases": ["City of Light", "City of Love", "Lutetia"], "sitelinkCount": 312, "wikipediaTitle": "Paris", "wikipediaUrl": "https://en.wikipedia.org/wiki/Paris", "wikidataUrl": "https://www.wikidata.org/wiki/Q90", "instanceOf": [{ "type": "item", "id": "Q484170", "label": "commune of France" }], "country": [{ "type": "item", "id": "Q142", "label": "France" }], "coordinates": [{ "type": "coordinates", "latitude": 48.8566, "longitude": 2.3522 }], "population": [{ "type": "quantity", "amount": 2145906, "unit": null }], "inception": [{ "type": "time", "value": "-300 BCE" }], "officialWebsite": [{ "type": "string", "value": "https://www.paris.fr/" }], "error": null } ```` Mapped properties: `instanceOf` (P31), `subclassOf` (P279), `country` (P17), `locatedIn` (P131), `coordinates` (P625), `inception` (P571), `dateOfBirth` (P569), `dateOfDeath` (P570), `population` (P1082), `officialWebsite` (P856), `image` (P18), `occupation` (P106), `countryOfCitizenship` (P27). ### Use cases - **Entity resolution & enrichment** — map messy names (cities, companies, people) to stable Q-IDs. - **Feed a RAG / LLM pipeline** with trustworthy, citable facts and Wikipedia links. - **Build a gazetteer or dataset** with coordinates, populations and countries in one run. - **Cross-language lookup** — get the same entity's label and Wikipedia article in any language. ### Limitations & fair use - Uses the public Wikidata `wbsearchentities` / `wbgetentities` API. Search returns the **best single match**; pass an exact Q-ID when precision matters. - Only the properties listed above are surfaced as fields — Wikidata entities can have hundreds of properties. Deprecated-rank statements are dropped. - Dates use Wikidata's precision model and can be partial (year only, or BCE) — the `value` string reflects that faithfully. - Wikidata content is **CC0** (public domain), so it is free to reuse; attribution is appreciated. - A polite delay is applied between requests; label lookups are batched (50 per request). Calls retry up to 3 times with backoff; failures become an `error` item rather than crashing the run. ### More actors by this developer Browse the full collection of free data and enrichment actors: **[apify.com/fit\_melon](https://apify.com/fit_melon)** # Actor input Schema ## `queries` (type: `array`): Names to search on Wikidata (e.g. Paris, Marie Curie) or exact Q-IDs (e.g. Q90). Q-IDs skip the search step and are fetched directly. One dataset item per entry. ## `language` (type: `string`): Language code for labels, descriptions and the Wikipedia sitelink (e.g. en, fr, de, es). Falls back to English when a label is missing. ## `resolveLabels` (type: `boolean`): If enabled, referenced items (country, instance of, occupation...) are resolved from bare Q-IDs to human-readable labels. Adds a batched extra request per entity. ## Actor input object example ```json { "queries": [ "Paris", "Q7186" ], "language": "en", "resolveLabels": true } ``` # 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 '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "queries": [ "Paris", "Q7186" ] }; // Run the Actor and wait for it to finish const run = await client.actor("fit_melon/wikidata-entity-lookup").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 '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "queries": [ "Paris", "Q7186", ] } # Run the Actor and wait for it to finish run = client.actor("fit_melon/wikidata-entity-lookup").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 '{ "queries": [ "Paris", "Q7186" ] }' | apify call fit_melon/wikidata-entity-lookup --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=fit_melon/wikidata-entity-lookup", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Wikidata Entity Lookup & Knowledge Graph Scraper (Free)", "description": "Search Wikidata and export entities as clean JSON: Q-ID, label, description, aliases, instance of, country, coordinates, population, dates and Wikipedia links. Free knowledge graph enrichment.", "version": "0.0", "x-build-id": "tA6fNfFPvnfOqUaEH" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/fit_melon~wikidata-entity-lookup/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-fit_melon-wikidata-entity-lookup", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/fit_melon~wikidata-entity-lookup/runs": { "post": { "operationId": "runs-sync-fit_melon-wikidata-entity-lookup", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/fit_melon~wikidata-entity-lookup/run-sync": { "post": { "operationId": "run-sync-fit_melon-wikidata-entity-lookup", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "queries" ], "properties": { "queries": { "title": "Search terms or Q-IDs", "type": "array", "description": "Names to search on Wikidata (e.g. Paris, Marie Curie) or exact Q-IDs (e.g. Q90). Q-IDs skip the search step and are fetched directly. One dataset item per entry.", "items": { "type": "string" } }, "language": { "title": "Language", "type": "string", "description": "Language code for labels, descriptions and the Wikipedia sitelink (e.g. en, fr, de, es). Falls back to English when a label is missing.", "default": "en" }, "resolveLabels": { "title": "Resolve linked item labels", "type": "boolean", "description": "If enabled, referenced items (country, instance of, occupation...) are resolved from bare Q-IDs to human-readable labels. Adds a batched extra request per entity.", "default": true } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```