Someone sends you a message with a name, a date, and a dollar amount buried in the middle.
You read the whole thing to find those three pieces of information.
Then you manually copy them into your system. One field at a time.
Every message. Every day. Hundreds of times.
Entity extraction does this automatically. It reads text and pulls out the structured pieces you actually need.
INTERMEDIATE - Builds on text processing. Enables knowledge graphs and data storage.
Text is messy. Messages, documents, and conversations contain valuable information buried in natural language. Names, dates, amounts, locations, organizations. The data is there, but it is not structured.
Manual extraction does not scale. Reading every message to find the relevant pieces takes time you do not have. And humans miss things, especially when tired or rushing.
Entity extraction bridges unstructured and structured. It reads natural language and outputs clean, typed data your systems can actually use.
"Please process the refund for John Smith, account #4523, for $249.99. The original transaction was on March 15th."
Entity extraction is a form of natural language processing that locates and classifies named entities in text. Names of people, organizations, locations, dates, monetary values, and custom entity types specific to your domain.
Modern entity extraction uses AI models (often LLMs) to understand context. "Apple" in a recipe is different from "Apple" in a tech article. "Jordan" could be a person, a country, or a brand. The model uses surrounding text to determine which.
The power is not just finding entities but typing them correctly. Knowing that "March 15th" is a DATE and "$249.99" is an AMOUNT lets your systems handle each appropriately.
Any time text contains structured information you need to act on, entity extraction turns reading into data. The pattern is universal: text goes in, typed entities come out, and your systems know what to do next.
Define the entity types relevant to your domain. Run text through extraction. Receive structured objects with type, value, and position. Route to appropriate handlers based on entity type.
Click "Extract Next Message" to see how entity extraction converts unstructured text into typed, structured data.
Click below to extract entities from sample messages
Most flexible, handles novel entity types
Send text to an LLM with a prompt describing what entities to find. The model returns structured JSON with entity types and values. Works for any entity type you can describe.
Handles ambiguity, custom types, context-dependent classification
Higher latency, costs per request, requires prompt engineering
Pre-trained for common entity types
Named Entity Recognition models trained on standard entity types: PERSON, ORG, LOCATION, DATE, MONEY. Fast inference, no API calls. Works offline.
Fast, cheap, consistent, no external dependencies
Limited to trained entity types, less flexible with context
Deterministic for structured formats
Regular expressions and rules for predictable formats: phone numbers, emails, account IDs, dates in known formats. Fastest option when patterns are consistent.
Instant, free, 100% predictable output
Brittle with variations, requires maintenance, no context awareness
Pattern matching when formats are predictable (account IDs, phone numbers, emails).
NER models for common entity types at scale with low latency requirements.
LLM-based when you need custom entity types or context-dependent classification.
Most production systems combine all three: pattern matching for structured formats, NER for common types, LLM for ambiguous cases.
A customer sends: "Please refund $249.99 to account #4523 for the March 15th charge." Without entity extraction, a human reads and copies each value. With entity extraction, the system instantly identifies AMOUNT, ACCOUNT, and DATE, then routes to the refund queue with all fields populated.
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You extracted "March 32nd" as a DATE because it looked like one. The downstream system crashed trying to parse an impossible date. Every extraction needs validation before use.
Instead: Validate extracted entities against type constraints. Dates must be real dates. Amounts must parse as numbers. Emails must match format.
"Apple" got extracted as a company in a grocery order. "Jordan" was classified as a country when it was a customer name. Same text, wrong interpretation.
Instead: Use context-aware extraction (LLMs) for ambiguous entities. Include surrounding text in the extraction prompt. Add domain hints when available.
You built extraction for the five entity types you needed at launch. Six months later, you need a new type. The system requires a rewrite.
Instead: Design extraction to accept entity type definitions as configuration. New types should be a schema change, not a code change.
You have learned how to pull structured data from unstructured text. The natural next step is understanding how to resolve entities across sources and link them together.