Triple

T6036497
Position Surface form Disambiguated ID Type / Status
Subject Valour FC E134434 entity
Predicate province P604 FINISHED
Object Manitoba E15186 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: Manitoba | Statement: [Valour FC, province, Manitoba]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Manitoba
Context triple: [Valour FC, province, Manitoba]
  • A. Manitoba chosen
    Manitoba is a central Canadian province known for its vast prairies, numerous lakes, and northern boreal forests.
  • B. Saskatchewan
    Saskatchewan is a prairie and boreal province in western Canada known for its vast flat landscapes, agriculture, and significant natural resources.
  • C. Alberta
    Alberta is a western Canadian province known for its vast prairies, Rocky Mountains, and significant natural resource industries.
  • D. Emerson, Manitoba
    Emerson, Manitoba is a small Canadian border town in southern Manitoba situated along the Red River near the U.S. state of North Dakota.
  • E. Churchill, Manitoba
    Churchill, Manitoba is a remote northern Canadian town on the shores of Hudson Bay, best known as one of the world’s premier destinations for viewing polar bears and beluga whales.
  • 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_69c00875db5c819099dd5bb833ec43c2 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c056b4e3ec819089b2d119ea2953fe completed March 22, 2026, 8:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69c13557696881909b50c8b72af6878c completed March 23, 2026, 12:43 p.m.
Created at: March 22, 2026, 4:08 p.m.