Triple

T13518799
Position Surface form Disambiguated ID Type / Status
Subject Travesties E322837 entity
Predicate hasCharacter P2308 FINISHED
Object Bennett E80464 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: Bennett | Statement: [Travesties, hasCharacter, Bennett]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bennett
Context triple: [Travesties, hasCharacter, Bennett]
  • A. Bennett chosen
    Bennett is a common English-language surname of Anglo-Norman origin borne by numerous notable individuals across politics, arts, and sciences.
  • B. Bennett
    Bennett is the main villain and former comrade-turned-mercenary antagonist who battles Arnold Schwarzenegger’s character in the 1985 action film "Commando."
  • C. Bennett Holiday
    Bennett Holiday is a young, ambitious Washington lawyer in the geopolitical thriller film "Syriana," navigating corruption and power struggles in the oil industry.
  • D. Bennet Tyler
    Bennet Tyler was a 19th-century American Congregationalist theologian and educator associated with the New England theological tradition.
  • E. Benet
    Benet is a commune in the Vendée department of western France, known for its rural character and location near the Marais Poitevin marshlands.
  • 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_69d80766a21881909f21a1b7421d3b8a completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbafa27f048190bed33a98e28c8d09 completed April 12, 2026, 2:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69f75498153c819096a28a7f0b608ff5 completed May 3, 2026, 1:58 p.m.
Created at: April 9, 2026, 9:44 p.m.