Triple

T4817834
Position Surface form Disambiguated ID Type / Status
Subject Bishop of Kensington E107631 entity
Predicate hasTerritorialDesignation P974 FINISHED
Object Kensington E14419 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Kensington | Statement: [Bishop of Kensington, hasTerritorialDesignation, Kensington]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kensington
Context triple: [Bishop of Kensington, hasTerritorialDesignation, Kensington]
  • A. Kensington chosen
    Kensington is a district in West London, England, known for its affluent residential areas, cultural institutions, and royal associations.
  • B. Kensington
    Kensington is a small, affluent unincorporated community in Contra Costa County, California, located in the San Francisco Bay Area.
  • C. Kensington
    Kensington is a popular inner-city district in Calgary known for its vibrant mix of shops, restaurants, and cultural venues.
  • D. Kensington
    Kensington is a historic Philadelphia neighborhood known for its industrial past and ongoing urban redevelopment.
  • E. Kensington
    Kensington is an inner-city suburb of Sydney, Australia, known for hosting the main campus of the University of New South Wales.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69bd43f9efa081908314cb3e94fa1695 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6c95b0ec8190a562541b0d7417cb completed March 20, 2026, 3:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69be6fa2f3e0819084ca792c1b08e3b9 completed March 21, 2026, 10:14 a.m.
Created at: March 20, 2026, 1:24 p.m.