What Are the Types of Entity in NLP?

Entities in NLP are structured, real-world concepts extracted via Named Entity Recognition (NER), commonly categorized into predefined types like people, organizations, locations, and temporal expressions. Standard types include PERSON (names), ORG (companies/institutions), LOC (geographical places), DATE/TIME, MONEY, PERCENT, and PRODUCT. 

In this blog, you will understand what are the types of entity in NLP, how they are categorized, and why they matter in tasks like Named Entity Recognition. 

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What Are the Types of Entity in NLP? 

To directly answer the types of entity in NLP, entities are commonly grouped into predefined categories that represent real-world objects or concepts mentioned in text. 

The most widely used entity types include: 

• Person (PER) 
• Organization (ORG) 
• Location (LOC) 
• Date and Time (DATE/TIME) 
• Money (MONEY) 
• Percent (PERCENT) 
• Product (PRODUCT) 
• Event (EVENT) 

These categories allow NLP systems to identify and label important information inside sentences. This process is known as Named Entity Recognition (NER). 

Also Read: NLP in Artificial Intelligence: Complete Beginner Guide 

Here is a quick overview: 

Entity Type	Example
Person	“Elon Musk”
Organization	“Google”
Location	“New York”
Date	“January 2026”
Money	“$500”
Product	“iPhone 15”

For example, in the sentence: 

“Apple launched the iPhone 15 in California in 2026.” 

An NLP model can detect: 

• Apple → Organization 
• iPhone 15 → Product 
• California → Location 
• 2026 → Date 

Understanding what are the types of entity in NLP helps developers build systems that automatically extract, classify, and organize key information from large volumes of text. 

Also Read: Named Entity Recognition(NER) Model with BiLSTM and Deep Learning in NLP

Person, Organization, and Location Entities 

When learning what are the types of entity in NLP, the most fundamental categories are Person, Organization, and Location. These are the core building blocks of Named Entity Recognition systems. 

1. Person (PER): Names of individuals 

Example: “Virat Kohli,” “Marie Curie” 

2. Organization (ORG): Companies, institutions, agencies 

Example: “Microsoft,” “World Health Organization” 

3. Location (LOC): Cities, countries, landmarks 

Example: “London,” “India,” “Eiffel Tower” 

These entities help NLP systems understand who is involved, which organization is mentioned, and where events take place. They are widely used in news analysis, resume parsing, question answering, and knowledge graph construction. 

Also Read: NLP Models in Machine Learning and Deep Learning   

Numerical and Temporal Entities 

Another important group when understanding what are the types of entity in NLP includes numerical and time-based entities. These categories help systems interpret quantities, dates, and financial information accurately. 

Common examples include: 

• Date: “15 March 2026” 
• Time: “10:30 AM” 
• Money: “₹50,000” 
• Percent: “12% growth” 
• Quantity: “20 kilograms” 

Also Read: Types of Natural Language Processing with Examples   

For example, in the sentence: 

“The company earned $2 million in 2025.” 

An NLP model identifies: 

• $2 million → Money 
• 2025 → Date 

These entities are widely used in financial analysis, business reports, and data-driven applications where numbers and timelines matter. 

Also Read: Which NLP Model Is Best for Sentiment Analysis in 2026?   

Domain-Specific Entities 

Beyond common categories, understanding what are the types of entity in NLP also includes domain-specific entities. These are customized entity types designed for a particular industry or application. 

Examples include: 

1. Medical Entities: Diseases, symptoms, medications 

Example: “Diabetes,” “Paracetamol” 

2. Legal Entities: Case numbers, laws, court names 

Example: “Section 420,” “Supreme Court” 

3. Technical Entities: Programming languages, software tools 

Example: “Python,” “TensorFlow” 

In specialized systems, developers train models to recognize these entities accurately. Domain-specific entities improve precision in healthcare analytics, legal document processing, and technical knowledge extraction. 

Also Read: NLP in Deep Learning: Models, Methods, and Applications   

Conclusion 

Knowing what are the types of entity in NLP helps you understand how machines extract meaningful information from text. From people and organizations to dates and products, entity classification enables structured analysis. These categories power search engines, chatbots, and intelligent data systems that rely on accurate information extraction. 

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