Triple

T17203036
Position Surface form Disambiguated ID Type / Status
Subject Louis-Philippe Hébert E417526 entity
Predicate regionOfActivity P82 FINISHED
Object Ontario 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 | Statement: [Louis-Philippe Hébert, regionOfActivity, Ontario]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ontario
Context triple: [Louis-Philippe Hébert, regionOfActivity, Ontario]
  • 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. Ontario
    Ontario is a city in southwestern San Bernardino County, California, known as a major logistics and transportation hub anchored by Ontario International Airport and extensive freeway and rail connections.
  • C. Ontario
    Ontario is a small village in Belize’s Cayo District, known as a rural community along the George Price Highway west of the capital, Belmopan.
  • D. British Columbia
    British Columbia is a western Canadian province known for its Pacific coastline, mountainous landscapes, and major cities such as Vancouver and Victoria.
  • E. Manitoba
    Manitoba is a central Canadian province known for its vast prairies, numerous lakes, and northern boreal forests.
  • 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_69d886d6ba8c819093215917b3d01689 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e42db11fc881908291bf29cc740e09 completed April 19, 2026, 1:19 a.m.
NED1 Entity disambiguation (via context triple) batch_6a015fdc13d88190bbf9e6d1272814d2 completed May 11, 2026, 4:49 a.m.
Created at: April 10, 2026, 5:38 a.m.