Triple

T9967087
Position Surface form Disambiguated ID Type / Status
Subject 95th Academy Awards E195709 entity
Predicate bestDirectorWinner P8113 FINISHED
Object Daniel Scheinert E375099 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: Daniel Scheinert | Statement: [95th Academy Awards, bestDirectorWinner, Daniel Scheinert]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Daniel Scheinert
Context triple: [95th Academy Awards, bestDirectorWinner, Daniel Scheinert]
  • A. Daniel Scheinert chosen
    Daniel Scheinert is an American filmmaker, best known as one half of the directing duo Daniels behind the acclaimed film "Everything Everywhere All at Once."
  • B. Daniel Roher
    Daniel Roher is a Canadian documentary filmmaker best known for directing the Oscar-winning political documentary "Navalny."
  • C. Christopher Franke
    Christopher Franke is a German composer and former Tangerine Dream member best known for his electronic and film scores, including work on science fiction and adventure productions.
  • D. Daniel Scharf
    Daniel Scharf is a film producer best known for his work on the influential 1992 Australian drama "Romper Stomper."
  • E. Michael Schiffer
    Michael Schiffer is an American screenwriter and playwright best known for scripting films such as "Lean on Me," "Crimson Tide," and "The Peacemaker."
  • 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_69ca82ebd1288190912f9e4482d1fa35 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cdb71f9d7c8190ac02c53052c1c6ad completed April 2, 2026, 12:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69d23dad53ac819097c4b687d49bd790 completed April 5, 2026, 10:47 a.m.
Created at: March 30, 2026, 8:47 p.m.