Triple

T13486698
Position Surface form Disambiguated ID Type / Status
Subject Dirty John E318521 entity
Predicate executiveProducer P7225 FINISHED
Object Charles Roven E11616 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: Charles Roven | Statement: [Dirty John, executiveProducer, Charles Roven]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Charles Roven
Context triple: [Dirty John, executiveProducer, Charles Roven]
  • A. Charles Roven chosen
    Charles Roven is an American film producer known for his work on major Hollywood blockbusters, including Christopher Nolan’s films such as Oppenheimer and The Dark Knight trilogy.
  • B. Michael Seitzman
    Michael Seitzman is an American screenwriter and producer known for his work on films such as "North Country" and for creating and producing several television series.
  • C. Robert Vogel
    Robert Vogel is a world-renowned practical shooting champion and firearms instructor known for his multiple IPSC and USPSA titles.
  • D. Joel Silver
    Joel Silver is a prominent American film producer known for high-octane action and genre-defining hits such as the "Lethal Weapon" and "The Matrix" franchises.
  • E. John Seitz
    John Seitz was an American cinematographer renowned for his influential work in classic Hollywood cinema, particularly in film noir and science fiction.
  • 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_69d806b6bfec819089222715b2e86c8e completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69dbaf3b9b488190bb4e11424ff599c8 completed April 12, 2026, 2:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69f75483a6f88190b3815fb8d97e65e4 completed May 3, 2026, 1:58 p.m.
Created at: April 9, 2026, 9:42 p.m.