Triple

T9249252
Position Surface form Disambiguated ID Type / Status
Subject Islington Woods E222277 entity
Predicate locatedIn P40 FINISHED
Object Vaughan E40928 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: Vaughan | Statement: [Islington Woods, locatedIn, Vaughan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vaughan
Context triple: [Islington Woods, locatedIn, Vaughan]
  • A. Vaughan chosen
    Vaughan is a rapidly growing suburban city in the Greater Toronto Area known for its diverse communities, shopping and entertainment complexes, and attractions like Canada’s Wonderland.
  • B. Vaughan
    Vaughan is a surname of Welsh origin that is notably associated with influential figures such as blues guitarist Stevie Ray Vaughan.
  • C. Brampton
    Brampton is a large suburban city in the Greater Toronto Area known for its diverse population and rapidly growing economy.
  • D. Brampton
    Brampton is a market town in Cumbria, England, known for its historic architecture and proximity to Hadrian’s Wall.
  • E. Etobicoke
    Etobicoke is a large suburban district in the western part of Toronto, Ontario, known for its residential neighborhoods, parks, and industrial areas along the waterfront.
  • 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_69ca841d2b18819089f9faf5b2c2aec0 completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd05f7e9848190939f9199d0c1a572 completed April 1, 2026, 11:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1d58be8a4819088c419888a879e3e completed April 5, 2026, 3:22 a.m.
Created at: March 30, 2026, 7:31 p.m.