Triple

T16732197
Position Surface form Disambiguated ID Type / Status
Subject North American Numbering Plan E406618 entity
Predicate countryCoverage P19200 FINISHED
Object Montserrat E17737 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: Montserrat | Statement: [North American Numbering Plan, countryCoverage, Montserrat]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Montserrat
Context triple: [North American Numbering Plan, countryCoverage, Montserrat]
  • A. Montserrat chosen
    Montserrat is a small Caribbean island and British Overseas Territory known for its volcanic activity and lush, mountainous landscape.
  • B. Montserrat massif
    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 the costumed grizzly bear mascot who represents the University of Montana at athletic events and campus activities.
  • 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_69d8838f242881908abd8bc138795886 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e39c362bb88190921fab43d76c3ee8 completed April 18, 2026, 2:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a009d4a94688190aabe56c34e8cc2c3 completed May 10, 2026, 2:59 p.m.
Created at: April 10, 2026, 5:20 a.m.