Triple

T161946
Position Surface form Disambiguated ID Type / Status
Subject Roberta Bondar E3305 entity
Predicate residence P75 FINISHED
Object Ontario, Canada E3554 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: Ontario, Canada | Statement: [Roberta Bondar, residence, Ontario, Canada]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ontario, Canada
Context triple: [Roberta Bondar, residence, Ontario, Canada]
  • A. Ontario chosen
    Ontario is Canada’s most populous province, home to the nation’s capital Ottawa and its largest city Toronto, and a major economic and cultural hub.
  • B. Southern Ontario
    Southern Ontario is the densely populated, industrial and economic heartland of Ontario, Canada, encompassing major cities such as Toronto, Hamilton, and London.
  • C. Maple, Ontario, Canada
    Maple, Ontario, Canada is a suburban community in the city of Vaughan, north of Toronto, known for its residential neighborhoods and proximity to major urban amenities.
  • D. Windsor, Ontario
    Windsor, Ontario is a Canadian city in southwestern Ontario known as a major automotive and manufacturing hub situated directly across the river from Detroit, Michigan.
  • E. Northern Ontario
    Northern Ontario is a vast, sparsely populated region of Ontario known for its boreal forests, abundant lakes, mining and forestry industries, and predominantly rural and Indigenous communities.
  • 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_69a2527757ec819090b8becb2cf1a862 completed Feb. 28, 2026, 2:27 a.m.
NER Named-entity recognition batch_69a2585877648190a2ec320182a69343 completed Feb. 28, 2026, 2:52 a.m.
NED1 Entity disambiguation (via context triple) batch_69a3795173e88190afe87259edd4f275 completed Feb. 28, 2026, 11:25 p.m.
Created at: Feb. 28, 2026, 2:31 a.m.