Triple

T1882813
Position Surface form Disambiguated ID Type / Status
Subject Jamaal Myers E39889 entity
Predicate employer P7 FINISHED
Object City of Toronto E1525 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: City of Toronto | Statement: [Jamaal Myers, employer, City of Toronto]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: City of Toronto
Context triple: [Jamaal Myers, employer, City of Toronto]
  • A. Toronto chosen
    Toronto is the largest city in Canada and a major cultural, financial, and media hub located in the province of Ontario.
  • B. Downtown Toronto
    Downtown Toronto is the city’s primary central business district and cultural core, known for its dense skyline, major attractions, and vibrant urban life.
  • C. City of Ontario
    The City of Ontario is a major suburban city in southwestern San Bernardino County, California, known for its international airport, logistics hubs, and role as an Inland Empire commercial center.
  • D. North York
    North York is a major district in the north end of Toronto, Ontario, known for its dense urban development, shopping centers, and mixed residential and commercial areas.
  • E. Greater Toronto Area
    The Greater Toronto Area is a large metropolitan region in Ontario, Canada, encompassing Toronto and its surrounding municipalities and suburbs.
  • 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_69a88633e4fc8190b7eb40463e048ec5 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb0fd4d6881908b8266bdae1517ce completed March 7, 2026, 5 a.m.
NED1 Entity disambiguation (via context triple) batch_69b055827da881909a589a8d5c577eb9 completed March 10, 2026, 5:31 p.m.
Created at: March 4, 2026, 7:34 p.m.