nentsentities

Nentsentities are a class of abstract referents used in knowledge representation and natural language understanding to model participants in discourse that are not named entities. They function as placeholders for entities described or implied by context, and they persist across events even when a proper name is unknown or withheld. Nentsentities are distinct from named entities, which carry explicit labels such as person, organization, or location.

A nentsentity typically has attributes such as an identifier, a semantic type (for example, person, agent, object, or role), a referential anchor that links it to other mentions, and a persistence constraint that governs how it evolves as discourse unfolds. They may participate in events, hold properties, and acquire descriptors through description. They can be resolved to named entities later in the text when sufficient information becomes available.

In practical terms, nentsentities are used in tasks like coreference resolution, event extraction, and narrative understanding. They are discovered through discourse analysis and clustering of non-named mentions, and they can be grounded to knowledge graphs or ontologies to support reasoning. They help maintain coherent references in long texts and enable models to represent hypothetical or generic participants.

Example: "A mysterious traveler entered the hall. He looked around" makes "the traveler" a nentsentity until the traveler is identified or named later. In AI systems, treating such entities explicitly supports more robust discourse tracking and description grounding. The concept is discussed in theoretical work on discourse representation and in experimental systems that simulate human-like understanding of stories.