Triple

T3759648
Position Surface form Disambiguated ID Type / Status
Subject Good Night, and Good Luck E82129 entity
Predicate starredActor P5563 FINISHED
Object Patricia Clarkson E298767 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: Patricia Clarkson | Statement: [Good Night, and Good Luck, starredActor, Patricia Clarkson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Patricia Clarkson
Context triple: [Good Night, and Good Luck, starredActor, Patricia Clarkson]
  • A. Patricia Clarkson chosen
    Patricia Clarkson is an American actress acclaimed for her versatile performances in film, television, and theater, often in complex supporting roles.
  • B. Mary Bellingham
    Mary Bellingham was the wife of John Bellingham, the man infamous for assassinating British Prime Minister Spencer Perceval in 1812.
  • C. Anneke Wills
    Anneke Wills is a British actress best known for playing the companion Polly in the classic science fiction television series Doctor Who during the 1960s.
  • D. Lesley Garrett
    Lesley Garrett is an English soprano and media personality known for her operatic performances and popular classical crossover work.
  • E. Edith Lesley
    Edith Lesley was an American educator and founder of the teacher-training institution that evolved into Lesley University in Cambridge, Massachusetts.
  • 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_69ad8b1db40081908b61ffa6b78afd4d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcbc3d3f48190974cec104080949f completed March 8, 2026, 7:19 p.m.
NED1 Entity disambiguation (via context triple) batch_69b51c6b2a488190a621cc223c673615 completed March 14, 2026, 8:29 a.m.
Created at: March 8, 2026, 3:35 p.m.