Triple

T19343766
Position Surface form Disambiguated ID Type / Status
Subject CRFM E483820 entity
Predicate hasMember P10 FINISHED
Object Montserrat NE NERFINISHED

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: Montserrat | Statement: [CRFM, hasMember, Montserrat]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Montserrat
Context triple: [CRFM, hasMember, Montserrat]
  • A. Montserrat
    Montserrat is a small Caribbean island and British Overseas Territory known for its volcanic activity and lush, mountainous landscape.
  • B. Montserrat massif chosen
    Montserrat massif is a distinctive multi-peaked mountain range in Catalonia, Spain, famed for its unique rock formations and the Montserrat Monastery.
  • C. Monte Grande
    Monte Grande is a suburban city in the Buenos Aires metropolitan area of Argentina, known as the administrative seat of the Esteban Echeverría Partido.
  • D. Monte
    Monte was the nickname of Monte Irvin, a Hall of Fame American baseball player renowned as one of the early Black stars to break Major League Baseball’s color barrier.
  • E. Monte
    Monte is a burial site associated with Charles IV, likely a historically significant location connected to his death and interment.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e8d244f8819080eb1f3491300db2 completed April 10, 2026, 12:10 p.m.
NER Named-entity recognition batch_69e618598dc08190a723398dd6fe551d completed April 20, 2026, 12:13 p.m.
Created at: April 10, 2026, 1:33 p.m.