Triple

T8308151
Position Surface form Disambiguated ID Type / Status
Subject Faculty of Engineering, Imperial College London E194516 entity
Predicate campus P269 FINISHED
Object South Kensington E313026 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: South Kensington | Statement: [Faculty of Engineering, Imperial College London, campus, South Kensington]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: South Kensington
Context triple: [Faculty of Engineering, Imperial College London, campus, South Kensington]
  • A. South Kensington chosen
    South Kensington is a London Underground station in West London, known for serving the museum district including the Natural History Museum, Science Museum, and Victoria and Albert Museum.
  • B. Kensington
    Kensington is a district in West London, England, known for its affluent residential areas, cultural institutions, and royal associations.
  • C. Kensington
    Kensington is a small, affluent unincorporated community in Contra Costa County, California, located in the San Francisco Bay Area.
  • D. Kensington
    Kensington is an inner-city suburb of Sydney, Australia, known for hosting the main campus of the University of New South Wales.
  • E. Kensington
    Kensington is a historic Philadelphia neighborhood known for its industrial past and ongoing urban redevelopment.
  • 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_69ca82e613e88190bf8139669bbd0d53 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb7f2c06608190bd21633af07a530b completed March 31, 2026, 8 a.m.
NED1 Entity disambiguation (via context triple) batch_69cf27cace5c8190b871c632a075cb0a completed April 3, 2026, 2:36 a.m.
Created at: March 30, 2026, 5:54 p.m.