Triple

T8329468
Position Surface form Disambiguated ID Type / Status
Subject Any Which Way You Can E195038 entity
Predicate starring P1507 FINISHED
Object Geoffrey Lewis E380117 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: Geoffrey Lewis | Statement: [Any Which Way You Can, starring, Geoffrey Lewis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Geoffrey Lewis
Context triple: [Any Which Way You Can, starring, Geoffrey Lewis]
  • A. Geoffrey Lewis chosen
    Geoffrey Lewis was an American character actor known for his prolific film and television career, often appearing in Westerns and Clint Eastwood movies.
  • B. Geoffrey Dawson
    Geoffrey Dawson was a British newspaper editor and influential public figure who notably served as editor of The Times during the early 20th century.
  • C. Michael Gwynn
    Michael Gwynn was a British character actor known for his roles in mid-20th-century film and television, including appearances in classic horror and science fiction productions.
  • D. Geoffrey Howard
    Geoffrey Howard is a personal name shared by several notable individuals, including figures in British politics, law, and public service.
  • E. Geoff Travis
    Geoff Travis is a British music industry figure best known as the founder of the influential independent label Rough Trade Records.
  • 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_69ca82e87f2c8190bdb71ee29dfc642d completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb7fb812508190aed8a283dacf712e completed March 31, 2026, 8:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69ce6cc36a74819082713f53bb6755d7 completed April 2, 2026, 1:18 p.m.
Created at: March 30, 2026, 5:56 p.m.