Triple

T16850256
Position Surface form Disambiguated ID Type / Status
Subject Death Note (2017 film) E409655 entity
Predicate cinematographyBy P1953 FINISHED
Object David Tattersall E212900 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: David Tattersall | Statement: [Death Note (2017 film), cinematographyBy, David Tattersall]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: David Tattersall
Context triple: [Death Note (2017 film), cinematographyBy, David Tattersall]
  • A. David Tattersall chosen
    David Tattersall is a British cinematographer known for his work on major films including entries in the Star Wars prequel trilogy.
  • B. Richard Hiscott
    Richard Hiscott is an editor known for his work on the television series "Willow."
  • C. Stephen Fitzpatrick
    Stephen Fitzpatrick is a British entrepreneur best known as the founder and CEO of the UK-based energy supplier OVO Energy.
  • D. Mark Bignell
    Mark Bignell is a British charity worker and chief executive of the addiction-focused charity Hamoaze House, known publicly as the husband of comedian and actress Dawn French.
  • E. David Bretherton
    David Bretherton was an American film editor known for his work on numerous Hollywood productions, including the musical comedy "The Best Little Whorehouse in Texas."
  • 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_69d88395e6c88190b22730f335107c14 completed April 10, 2026, 4:59 a.m.
NER Named-entity recognition batch_69e3b378dda48190ab81d75f2cfe3ab3 completed April 18, 2026, 4:38 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00bb1f02648190937c692af83843dc completed May 10, 2026, 5:06 p.m.
Created at: April 10, 2026, 5:24 a.m.