# Entity Extractor — emails, URLs, phones, dates (regex, no LLM) (`shoebill-dev27/entity-extractor`) Actor Extract structured entities from free text: email addresses, URLs, phone numbers (incl. Japanese formats and full-width digits), dates (ISO, slash, Japanese 年月日) and IP addresses. Deterministic regex extraction with per-kind counts — fast, cheap, no LLM. - **URL**: https://apify.com/shoebill-dev27/entity-extractor.md - **Developed by:** [Shinobu Otani](https://apify.com/shoebill-dev27) (community) - **Categories:** Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## 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.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.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/platform/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 ## Entity Extractor Extract structured entities from free text with deterministic regexes — fast, cheap, no LLM. ### What it does - **Emails**, **URLs** (http/https, trailing punctuation stripped), **phone numbers** (international `+XX…` and Japanese `0X-XXXX-XXXX` formats), **dates** (`YYYY-MM-DD`, `YYYY/M/D`, `YYYY年M月D日`) and **IPv4 addresses**. - Input is NFKC-normalized first, so full-width Japanese digits and symbols (`０９０－１２３４－５６７８`) extract cleanly. - Each entity kind can be toggled; values are deduplicated by default (first occurrence kept, order preserved). ### Input ```json { "texts": ["Contact info@example.com or ０３－１２３４－５６７８, see https://example.com on 2026-06-13."], "emails": true, "urls": true, "phone_numbers": true, "dates": true, "ip_addresses": true, "unique": true } ```` ### Output (one dataset item per text) ```json { "emails": ["info@example.com"], "urls": ["https://example.com"], "phone_numbers": ["03-1234-5678"], "dates": ["2026-06-13"], "ip_addresses": [], "counts": {"emails": 1, "urls": 1, "phone_numbers": 1, "dates": 1, "ip_addresses": 0}, "total": 4, "index": 0 } ``` ### Usage Point it at scraped pages, support tickets, or listings to pull out contact details and dates for CRM enrichment, lead lists, or monitoring pipelines. # Actor input Schema ## `texts` (type: `array`): Texts to extract entities from; one dataset item is produced per text ## `emails` (type: `boolean`): Extract email addresses ## `urls` (type: `boolean`): Extract http(s) URLs ## `phone_numbers` (type: `boolean`): Extract phone numbers (international +XX and Japanese 0X-XXXX-XXXX formats; full-width digits supported) ## `dates` (type: `boolean`): Extract dates (YYYY-MM-DD, YYYY/M/D, YYYY年M月D日) ## `ip_addresses` (type: `boolean`): Extract IPv4 addresses ## `unique` (type: `boolean`): Drop repeated occurrences of the same value (first occurrence kept, order preserved) ## Actor input object example ```json { "texts": [ "Contact john.doe@example.com or call +1-202-555-0142. See https://example.com, updated 2026-01-15, served from 192.168.0.1." ], "emails": true, "urls": true, "phone_numbers": true, "dates": true, "ip_addresses": true, "unique": 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 = { "texts": [ "Contact john.doe@example.com or call +1-202-555-0142. See https://example.com, updated 2026-01-15, served from 192.168.0.1." ] }; // Run the Actor and wait for it to finish const run = await client.actor("shoebill-dev27/entity-extractor").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 = { "texts": ["Contact john.doe@example.com or call +1-202-555-0142. See https://example.com, updated 2026-01-15, served from 192.168.0.1."] } # Run the Actor and wait for it to finish run = client.actor("shoebill-dev27/entity-extractor").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 '{ "texts": [ "Contact john.doe@example.com or call +1-202-555-0142. See https://example.com, updated 2026-01-15, served from 192.168.0.1." ] }' | apify call shoebill-dev27/entity-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=shoebill-dev27/entity-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Entity Extractor — emails, URLs, phones, dates (regex, no LLM)", "description": "Extract structured entities from free text: email addresses, URLs, phone numbers (incl. Japanese formats and full-width digits), dates (ISO, slash, Japanese 年月日) and IP addresses. Deterministic regex extraction with per-kind counts — fast, cheap, no LLM.", "version": "0.1", "x-build-id": "FJ5PgZvqHGmAruzlj" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/shoebill-dev27~entity-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-shoebill-dev27-entity-extractor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/shoebill-dev27~entity-extractor/runs": { "post": { "operationId": "runs-sync-shoebill-dev27-entity-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/shoebill-dev27~entity-extractor/run-sync": { "post": { "operationId": "run-sync-shoebill-dev27-entity-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "texts" ], "properties": { "texts": { "title": "Texts", "type": "array", "description": "Texts to extract entities from; one dataset item is produced per text", "items": { "type": "string" } }, "emails": { "title": "Emails", "type": "boolean", "description": "Extract email addresses", "default": true }, "urls": { "title": "URLs", "type": "boolean", "description": "Extract http(s) URLs", "default": true }, "phone_numbers": { "title": "Phone numbers", "type": "boolean", "description": "Extract phone numbers (international +XX and Japanese 0X-XXXX-XXXX formats; full-width digits supported)", "default": true }, "dates": { "title": "Dates", "type": "boolean", "description": "Extract dates (YYYY-MM-DD, YYYY/M/D, YYYY年M月D日)", "default": true }, "ip_addresses": { "title": "IP addresses", "type": "boolean", "description": "Extract IPv4 addresses", "default": true }, "unique": { "title": "Unique values only", "type": "boolean", "description": "Drop repeated occurrences of the same value (first occurrence kept, order preserved)", "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 } } } } } } } } } } ```