Triple

T4866262
Position Surface form Disambiguated ID Type / Status
Subject University of Winnipeg E108777 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: [University of Winnipeg, province, Manitoba]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Manitoba
Context triple: [University of Winnipeg, 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_69bd440b965081908b0557721cae6338 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6d7a42f88190bb1ef7261bcbc2a8 completed March 20, 2026, 3:53 p.m.
NED1 Entity disambiguation (via context triple) batch_69be6fa2f3e0819084ca792c1b08e3b9 completed March 21, 2026, 10:14 a.m.
Created at: March 20, 2026, 1:26 p.m.