Triple

T280391
Position Surface form Disambiguated ID Type / Status
Subject University College, University of Toronto E5340 entity
Predicate region P40 FINISHED
Object Downtown Toronto E18465 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: Downtown Toronto | Statement: [University College, University of Toronto, region, Downtown Toronto]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Downtown Toronto
Context triple: [University College, University of Toronto, region, Downtown Toronto]
  • A. Downtown Toronto chosen
    Downtown Toronto is the city’s primary central business district and cultural core, known for its dense skyline, major attractions, and vibrant urban life.
  • B. Toronto
    Toronto is the largest city in Canada and a major cultural, financial, and media hub located in the province of Ontario.
  • C. Eastern Toronto
    Eastern Toronto is the part of Toronto that includes areas such as Scarborough and other eastern neighborhoods of the city.
  • D. Greater Toronto Area
    The Greater Toronto Area is a large metropolitan region in Ontario, Canada, encompassing Toronto and its surrounding municipalities and suburbs.
  • E. PortsToronto
    PortsToronto is a government business enterprise that manages and operates key transportation and waterfront assets in Toronto, including Billy Bishop Toronto City Airport and the city’s port and marina facilities.
  • 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_69a257e6c8788190987dfe705ca2912a completed Feb. 28, 2026, 2:50 a.m.
NER Named-entity recognition batch_69a25e0868708190ad551ca06cc57f4a completed Feb. 28, 2026, 3:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69a44f4eb77c81909e0e0d1729dd170c completed March 1, 2026, 2:38 p.m.
Created at: Feb. 28, 2026, 2:59 a.m.