Triple

T14398060
Position Surface form Disambiguated ID Type / Status
Subject Forget Paris E357001 entity
Predicate editedBy P1954 FINISHED
Object Kent Beyda E369466 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: Kent Beyda | Statement: [Forget Paris, editedBy, Kent Beyda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kent Beyda
Context triple: [Forget Paris, editedBy, Kent Beyda]
  • A. Kent Beyda chosen
    Kent Beyda is a film editor best known for his work on major studio comedies and family films, including the 2002 live-action Scooby-Doo movie.
  • B. Michael Begler
    Michael Begler is an American television writer and producer best known for co-creating the period medical drama series "The Knick."
  • C. Eric Wetzels
    Eric Wetzels is a Dutch politician who serves as the chairperson of the People's Party for Freedom and Democracy (VVD).
  • D. Anthony Meyer
    Anthony Meyer is a British politician and former Conservative Member of Parliament best known for mounting a symbolic leadership challenge against Prime Minister Margaret Thatcher in 1989.
  • E. Kevin Biegel
    Kevin Biegel is an American television writer and producer best known for co-creating the sitcom Cougar Town and working on shows like Scrubs and Enlisted.
  • 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_69d827927c988190ad98bb0360981783 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de9083f9d081908fe5c99655c410b3 completed April 14, 2026, 7:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69ff01d196d88190a8fa54468b2de1bb completed May 9, 2026, 9:43 a.m.
Created at: April 10, 2026, 1:17 a.m.