# Text Entity Extractor — Emails, Phones, URLs & More (`hipersoft/text-entity-extractor`) Actor

Extract structured entities from any text: emails, phone numbers, URLs, dates, times, money, percentages, IP addresses, hashtags, @mentions, ZIP codes and emojis. Choose which types to pull. Process one text or many at once. One row per entity as JSON, CSV or Excel.

- **URL**: https://apify.com/hipersoft/text-entity-extractor.md
- **Developed by:** [hiper soft](https://apify.com/hipersoft) (community)
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.00015 / entity extracted

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

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

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Text Entity Extractor — Emails, Phones, URLs & More

Pull **structured entities out of messy, unstructured text** with the **Text Entity Extractor**. Point it at an article, email, chat log, transcript or scraped page and get back every **email, phone number, URL, date, time, money amount, percentage, IP address, hashtag, @mention, ZIP code and emoji** — one clean row per entity, exported as **JSON, CSV or Excel**.

Ideal for **lead extraction, data mining, log parsing, content moderation, redaction prep and dataset enrichment**, it turns free text into a tidy, filterable table with zero setup.

### What it does

- 📧 **Many entity types** — emails, URLs, phone numbers, dates, times, money, percentages, IPv4 addresses, hashtags, @mentions, US ZIP codes, card-number candidates and emojis.
- 🎛️ **Pick what you want** — extract everything, or select just the types you need (e.g. only emails and phones).
- 🧹 **Smart matching** — phone detection avoids false positives from dates, IPs and ZIP codes.
- 🧾 **De-duplication** — keep only the first occurrence of each identical entity, or keep them all.
- 📚 **Batch texts** — scan many documents in one run, each labelled by document.
- 📍 **Positions included** — every entity comes with its character offset in the source text.
- 📤 **Export anywhere** — JSON, CSV or Excel, or straight into a CRM, spreadsheet or workflow.

### Example output

```json
{ "documentIndex": 0, "type": "email", "value": "jane.doe@example.com", "start": 8 }
{ "documentIndex": 0, "type": "phone", "value": "+1 (650) 555-1234", "start": 32 }
{ "documentIndex": 0, "type": "money", "value": "$1,250.50", "start": 96 }
```

### How to use it

1. Paste your **text** (or add several texts in the list).
2. Optionally choose which **entity types** to extract — leave empty for all.
3. Toggle **Remove duplicates** if you want unique values only.
4. Run, and download the entities as **JSON, CSV or Excel**.

### Input fields

| Field | Description |
|-------|-------------|
| **Text** | The text to scan. |
| **Multiple texts** | Optional list of separate texts to scan in one run. |
| **Entity types** | Which types to extract; empty = all. |
| **Remove duplicates** | Keep only the first occurrence of each identical entity. |
| **Max results** | Maximum total entities across all texts (0 = no limit). |

### Output fields

`documentIndex`, `type`, `value`, `start`, `collectedAt`.

### Popular use cases

- **Lead extraction** — pull emails and phone numbers out of scraped pages or documents.
- **Data mining** — turn free-text notes, reviews or tickets into structured fields.
- **Log & security parsing** — extract IPs, URLs and timestamps from logs.
- **Content moderation & redaction** — locate personal data (emails, phones, cards) to review or mask.
- **Social analysis** — collect hashtags and @mentions across posts.
- **Dataset enrichment** — add structured entity columns to an existing text dataset.

### FAQ

**Do I need an account or key?**
No. Paste your text, pick the types, and run.

**How accurate is the extraction?**
It uses well-tuned pattern matching with guards to reduce false positives (for example, dates and IP addresses aren't mistaken for phone numbers). As with any pattern-based extractor, unusual formats may vary.

**Can I extract only certain types?**
Yes — select just the entity types you need (e.g. only emails and phones) and the rest are ignored.

**Does it work on many documents at once?**
Yes — add a list of texts and each is scanned separately, with a document index on every result.

**Can I export to Excel or Google Sheets?**
Yes — results download as JSON, CSV or Excel and integrate with Sheets, CRMs and automation tools.

***

Turn unstructured text into a clean table of **emails, phones, URLs, dates, money and more** — filterable, deduplicated and export-ready.

# Actor input Schema

## `text` (type: `string`):

The text to scan for entities. Paste an article, email, transcript, log or document. For multiple texts, use the list below.

## `texts` (type: `array`):

Optional — a list of separate texts to scan in one run. Each is processed independently and labelled by document.

## `types` (type: `array`):

Which entity types to extract. Leave empty to extract them all.

## `dedupe` (type: `boolean`):

Keep only the first occurrence of each identical entity within a text.

## `maxItems` (type: `integer`):

Maximum total entities to output across all texts (0 = no limit).

## Actor input object example

```json
{
  "texts": [],
  "types": [],
  "dedupe": true,
  "maxItems": 0
}
```

# Actor output Schema

## `results` (type: `string`):

The results as dataset items.

# 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 = {
    "text": "",
    "texts": []
};

// Run the Actor and wait for it to finish
const run = await client.actor("hipersoft/text-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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "text": "",
    "texts": [],
}

# Run the Actor and wait for it to finish
run = client.actor("hipersoft/text-entity-extractor").call(run_input=run_input)

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

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

```

## CLI example

```bash
echo '{
  "text": "",
  "texts": []
}' |
apify call hipersoft/text-entity-extractor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,hipersoft/text-entity-extractor"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/0Ru052XwYHc8OqeIQ/builds/1s5x4OoSFhjeNf1P2/openapi.json