Triple

T5146020
Position Surface form Disambiguated ID Type / Status
Subject Royal Borough of Kensington and Chelsea E116071 entity
Predicate containsNeighbourhood P4813 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: [Royal Borough of Kensington and Chelsea, containsNeighbourhood, Kensington]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kensington
Context triple: [Royal Borough of Kensington and Chelsea, containsNeighbourhood, 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_69bd4446c0e08190a7c29dc74976bf03 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd78ac677881909ce8632f1a8af880 completed March 20, 2026, 4:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf21a42d208190832d00fb62ad683e completed March 21, 2026, 10:54 p.m.
Created at: March 20, 2026, 1:43 p.m.