In IBM Watson, what is 'entity' typically used for?

In IBM Watson, 'entity' is typically used to identify keywords within user inputs. Entities serve as the pivotal components that capture and represent specific pieces of information extracted from the user's text. For instance, if a user mentions a location, a date, or a product name, these are recognized as entities. This enables the system to understand the context and meaning behind user queries more effectively.

Entities enhance the natural language understanding capabilities of Watson by providing a structured way to address the various pieces of information that can be important for processing user requests. For example, if a user asks for restaurant recommendations in New York City, recognizing "New York City" as an entity allows Watson to apply that specific geographical information when retrieving data related to the user's request.

The other options focus on different functionalities: categorizing user intents relates more to understanding what action or response the user is seeking; storing user preferences deals with personalization aspects; and managing dialogue flow pertains to how conversations are structured. While all these are integral to creating a robust conversational agent, the primary role of entities specifically emphasizes the recognition and categorization of important terms within user inputs.