A named entity is a "real-world object" that's assigned a name – for example, a person, a country, a product or a book title. spaCy can **recognize various types of named entities in a document, by asking the model for a prediction**. Because models are statistical and strongly depend on the examples they were trained on, this doesn't always work _perfectly_ and might need some tuning later, depending on your use case. Named entities are available as the `ents` property of a `Doc`: ```python {executable="true"} import spacy nlp = spacy.load("en_core_web_sm") doc = nlp("Apple is looking at buying U.K. startup for $1 billion") for ent in doc.ents: print(ent.text, ent.start_char, ent.end_char, ent.label_) ``` > - **Text:** The original entity text. > - **Start:** Index of start of entity in the `Doc`. > - **End:** Index of end of entity in the `Doc`. > - **Label:** Entity label, i.e. type. | Text | Start | End | Label | Description | | ----------- | :---: | :-: | ------- | ---------------------------------------------------- | | Apple | 0 | 5 | `ORG` | Companies, agencies, institutions. | | U.K. | 27 | 31 | `GPE` | Geopolitical entity, i.e. countries, cities, states. | | \$1 billion | 44 | 54 | `MONEY` | Monetary values, including unit. | Using spaCy's built-in [displaCy visualizer](/usage/visualizers), here's what our example sentence and its named entities look like:
Apple ORG is looking at buying U.K. GPE startup for $1 billion MONEY