Triple

T5635097
Position Surface form Disambiguated ID Type / Status
Subject Another Thin Man E147927 entity
Predicate editor P1954 FINISHED
Object Robert Kern E138041 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: Robert Kern | Statement: [Another Thin Man, editor, Robert Kern]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Robert Kern
Context triple: [Another Thin Man, editor, Robert Kern]
  • A. Robert Kern chosen
    Robert Kern was an American film editor active during Hollywood’s classic studio era, known for his work on numerous prominent MGM productions.
  • B. Ben Finney
    Ben Finney was an anthropologist and pioneer of experimental archaeology best known for reviving traditional Polynesian navigation and co-founding the Polynesian Voyaging Society.
  • C. Chris Angelico
    Chris Angelico is a Python developer and community contributor known for his involvement in Python Enhancement Proposals, including co-authoring PEP 572.
  • D. David Flanagan
    David Flanagan is a software developer and technical author best known for his widely used programming books, including "JavaScript: The Definitive Guide."
  • E. Phil Zimmermann
    Phil Zimmermann is an American cryptographer best known as the creator of Pretty Good Privacy (PGP), a widely used email encryption software that helped popularize strong cryptography for the public.
  • 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_69c00907bc8881909ed760d3ed73ef35 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c0226286208190b6ccf036cc09fe82 completed March 22, 2026, 5:09 p.m.
NED1 Entity disambiguation (via context triple) batch_69c05a1666d88190af4c1890247f897d completed March 22, 2026, 9:07 p.m.
Created at: March 22, 2026, 3:41 p.m.