Triple

T20873877
Position Surface form Disambiguated ID Type / Status
Subject Bearskin Airlines E513965 entity
Predicate regionServed P82 FINISHED
Object Central Canada NE NERFINISHED

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: Central Canada | Statement: [Bearskin Airlines, regionServed, Central Canada]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Central Canada
Context triple: [Bearskin Airlines, regionServed, Central Canada]
  • A. central Canada chosen
    Central Canada is a broad geographic region of the country that includes parts of Ontario and Manitoba and has long been home to Indigenous peoples such as the Ojibwe.
  • 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. Central Ontario
    Central Ontario is a predominantly rural and recreational region of Ontario known for its lakes, forests, cottage country, and outdoor tourism.
  • D. North Ontario
    North Ontario is the former name of a historic community area that later became part of the city of Upland in San Bernardino County, California.
  • 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 (2 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_69e0b4f675cc8190b4e745225b62eb66 completed April 16, 2026, 10:07 a.m.
NER Named-entity recognition batch_69e6c46639308190a616193f3975d453 completed April 21, 2026, 12:27 a.m.
Created at: April 16, 2026, 12:45 p.m.