Triple

T13996886
Position Surface form Disambiguated ID Type / Status
Subject Steven Geray E336721 entity
Predicate name P16 FINISHED
Object Steven Geray E336721 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: Steven Geray | Statement: [Steven Geray, name, Steven Geray]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Steven Geray
Context triple: [Steven Geray, name, Steven Geray]
  • A. Steven Geray chosen
    Steven Geray was a Hungarian-American character actor known for his numerous supporting roles in classic Hollywood films of the 1940s and 1950s.
  • B. Peter Garnsey
    Peter Garnsey is a prominent historian of the ancient world, particularly known for his influential scholarship on the social, economic, and legal history of the Roman Empire.
  • C. Philip Voss
    Philip Voss was a British actor known for his extensive work in theatre, television, and radio, including roles with the Royal Shakespeare Company and appearances in popular UK dramas.
  • D. Michael Sayers
    Michael Sayers was a screenwriter known for contributing to the script of the 1967 satirical James Bond film "Casino Royale."
  • E. Stephen Greenhorn
    Stephen Greenhorn is a Scottish playwright and screenwriter known for his work in theatre, television, and film, including creating the TV series "River City" and writing for "Doctor Who."
  • 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_69d81c645c5c8190b1fd16a285a1b78a completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2eb68ba88190bfaf10777d607bf3 completed April 14, 2026, 12:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69fbac9d4a54819091c7efbeb4dcc5f7 completed May 6, 2026, 9:03 p.m.
Created at: April 9, 2026, 10:19 p.m.