Triple

T5221538
Position Surface form Disambiguated ID Type / Status
Subject Canadian Classique E117879 entity
Predicate hasLocation P40 FINISHED
Object Montreal E2604 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: Montreal | Statement: [Canadian Classique, hasLocation, Montreal]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Montreal
Context triple: [Canadian Classique, hasLocation, Montreal]
  • A. Montreal chosen
    Montreal is the largest city in Quebec, Canada, known for its vibrant bilingual culture, historic architecture, and status as a major economic and cultural center.
  • B. Quebec City
    Quebec City is the historic capital of the Canadian province of Quebec, renowned for its well-preserved fortified old town and rich French colonial heritage.
  • C. Gatineau
    Gatineau is a city in western Quebec, Canada, located across the Ottawa River from Ottawa and forming part of the National Capital Region.
  • D. Trois-Rivières
    Trois-Rivières is a historic industrial and cultural city in the Canadian province of Quebec, located roughly midway between Montreal and Quebec City.
  • E. Montreal, Quebec, Canada
    Montreal, Quebec, Canada is a major French-speaking metropolis known for its vibrant arts scene, diverse culture, and historic architecture on the Saint Lawrence River.
  • 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_69bd4465e03081909bfcfd7113062590 completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd7aba05b48190b6a7fc52ab3532f0 completed March 20, 2026, 4:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69bf06982c5081908c275019c5d6b1c1 completed March 21, 2026, 8:59 p.m.
Created at: March 20, 2026, 1:48 p.m.