Triple

T12015194
Position Surface form Disambiguated ID Type / Status
Subject English royal court E286005 entity
Predicate location P40 FINISHED
Object Westminster E14557 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: Westminster | Statement: [English royal court, location, Westminster]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Westminster
Context triple: [English royal court, location, Westminster]
  • A. Westminster
    Westminster is a city in Orange County, California, known for its large Vietnamese-American community and vibrant Little Saigon district.
  • B. 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.
  • C. 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.
  • D. 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.
  • 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 (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_69d6ab45a368819084fce08bf0dc3705 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d903d9b17881908894be80d7c1b64e completed April 10, 2026, 2:06 p.m.
NED1 Entity disambiguation (via context triple) batch_69f49d4156ec8190bfc6d1180ac41d06 completed May 1, 2026, 12:32 p.m.
Created at: April 8, 2026, 9:47 p.m.