Triple

T19437432
Position Surface form Disambiguated ID Type / Status
Subject Francis Urquhart E486258 entity
Predicate setting P1957 FINISHED
Object Westminster NE NERFINISHED

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: Westminster | Statement: [Francis Urquhart, setting, Westminster]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Westminster
Context triple: [Francis Urquhart, setting, Westminster]
  • A. Westminster
    Westminster is a small New England town in northern Massachusetts known for its rural character, historic charm, and proximity to the Wachusett Mountain area.
  • B. Westminster
    Westminster is a suburban city in the Denver metropolitan area of Colorado, known for its residential communities, parks, and proximity to the Rocky Mountains.
  • C. Westminster
    Westminster is a city in Orange County, California, known for its large Vietnamese-American community and vibrant Little Saigon district.
  • D. Westminster
    Westminster is a city in northern Maryland that serves as the county seat of Carroll County and a regional hub for the surrounding communities.
  • E. City of Westminster chosen
    The City of Westminster is a central London borough that serves as the political and ceremonial heart of the United Kingdom, encompassing landmarks such as the Houses of Parliament, Buckingham Palace, and major government institutions.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e8d7ad488190a3373045029b0f3b completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e633618c2881908f3d2a9cabb02289 completed April 20, 2026, 2:08 p.m.
Created at: April 10, 2026, 1:38 p.m.