Triple

T18091818
Position Surface form Disambiguated ID Type / Status
Subject John Cranko E432988 entity
Predicate notableWork P4 FINISHED
Object Carmen (ballet) 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: Carmen (ballet) | Statement: [John Cranko, notableWork, Carmen (ballet)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Carmen (ballet)
Context triple: [John Cranko, notableWork, Carmen (ballet)]
  • A. Carmen chosen
    Carmen is a famous opera by Georges Bizet, renowned for its passionate music and tragic story centered on the free-spirited gypsy Carmen.
  • B. Carmen
    Carmen is a landlocked agricultural municipality in the province of North Cotabato on the island of Mindanao in the Philippines.
  • C. Carmen
    Carmen is a key character in the dark fantasy film "Pan’s Labyrinth," serving as the pregnant mother whose fragile health and marriage to a brutal captain frame the story’s wartime and familial tensions.
  • D. Carmen
    Carmen is a landlocked municipality in the central part of Bohol Island in the Philippines, known for its proximity to the famous Chocolate Hills.
  • E. Carmen
    Carmen is a pivotal character in the 1986 film "The Color of Money," serving as the savvy and manipulative girlfriend-manager of young pool hustler Vincent Lauria.
  • 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_69d8b907d05c819083cc3bd6021089e6 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4dd18b55081909c416fb7186bd4a4 completed April 19, 2026, 1:48 p.m.
Created at: April 10, 2026, 10:27 a.m.