Triple

T8557992
Position Surface form Disambiguated ID Type / Status
Subject Kensington (Calgary) E202621 entity
Predicate hasNameOrigin P3325 FINISHED
Object Kensington (London) 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 (London) | Statement: [Kensington (Calgary), hasNameOrigin, Kensington (London)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kensington (London)
Context triple: [Kensington (Calgary), hasNameOrigin, Kensington (London)]
  • 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 an inner-city suburb of Sydney, Australia, known for hosting the main campus of the University of New South Wales.
  • D. Kensington
    Kensington is a historic Philadelphia neighborhood known for its industrial past and ongoing urban redevelopment.
  • E. Kensington
    Kensington is a residential neighborhood in central Brooklyn, New York City, known for its diverse population and mix of apartment buildings and single-family homes.
  • 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_69ca8326e6c881908ff720d6abaebdc5 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe9485dd88190bc2cf2adf39d48ee completed March 31, 2026, 3:33 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf6ea4fadc8190ade74e4fdf890056 completed April 3, 2026, 7:39 a.m.
Created at: March 30, 2026, 6:20 p.m.