Triple

T16102300
Position Surface form Disambiguated ID Type / Status
Subject Parc Jean-Drapeau E390651 entity
Predicate hasPart P35 FINISHED
Object Casino de Montréal E429584 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: Casino de Montréal | Statement: [Parc Jean-Drapeau, hasPart, Casino de Montréal]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Casino de Montréal
Context triple: [Parc Jean-Drapeau, hasPart, Casino de Montréal]
  • A. Montreal Casino chosen
    Montreal Casino is a major gambling and entertainment complex in Montreal, Quebec, known as one of Canada’s largest and most prominent casinos.
  • B. Casino de la Forêt
    Casino de la Forêt is a historic gambling and entertainment venue located in the seaside resort town of Le Touquet-Paris-Plage in northern France.
  • C. Casino de Paris
    Casino de Paris is a historic and iconic Parisian music hall and cabaret venue renowned for its variety shows and star performances.
  • D. Casino de Beaulieu-sur-Mer
    Casino de Beaulieu-sur-Mer is an elegant seaside casino and entertainment venue on the French Riviera, known for its Belle Époque architecture and upscale gaming atmosphere.
  • E. Normandie Casino
    Normandie Casino is a long-standing card club and gambling venue located in Gardena, California.
  • 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_69d87f1a8dd881909f1de6ef78849874 completed April 10, 2026, 4:39 a.m.
NER Named-entity recognition batch_69e1ff6976ec8190b499e99b196b0285 completed April 17, 2026, 9:37 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffeba007c08190bf4d3cf092abc7dd completed May 10, 2026, 2:21 a.m.
Created at: April 10, 2026, 5 a.m.