Triple

T15478060
Position Surface form Disambiguated ID Type / Status
Subject Theatreland E376835 entity
Predicate associatedWith P37 FINISHED
Object Covent Garden E81488 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: Covent Garden | Statement: [Theatreland, associatedWith, Covent Garden]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Covent Garden
Context triple: [Theatreland, associatedWith, Covent Garden]
  • A. Covent Garden Market
    Covent Garden Market is a historic London marketplace and tourist destination known for its shops, restaurants, street performers, and vibrant cultural atmosphere.
  • B. Covent Garden (part) chosen
    Covent Garden (part) is a historic and culturally vibrant district in central London known for its market, theatres, street performers, and shopping.
  • C. Kensington Arcade
    Kensington Arcade is a shopping arcade located on Kensington High Street in London, featuring a variety of retail stores and services.
  • D. Kensington Market
    Kensington Market is a vibrant, historically multicultural neighborhood in Toronto known for its eclectic shops, diverse food offerings, and bohemian street culture.
  • E. Leicester Square
    Leicester Square is a famous pedestrianized square in London’s West End, known for its cinemas, theatres, and entertainment venues.
  • 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_69d85cd21dcc81908646251b1c26ea00 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e03f88a5dc8190a2d7830748e29180 completed April 16, 2026, 1:46 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff56b9238081908fb2e6a7dcc2296f completed May 9, 2026, 3:46 p.m.
Created at: April 10, 2026, 3:34 a.m.