Triple

T10682386
Position Surface form Disambiguated ID Type / Status
Subject Cake (2014 film) E251789 entity
Predicate director P255 FINISHED
Object Daniel Barnz
Daniel Barnz is an American film director and screenwriter known for character-driven dramas such as "Cake" and "Won't Back Down."
 E881480 NE FINISHED

How this triple was built (4 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 Barnz | Statement: [Cake (2014 film), director, Daniel Barnz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Daniel Barnz
Context triple: [Cake (2014 film), director, Daniel Barnz]
  • A. Michael Barnathan
    Michael Barnathan is an American film producer known for working on major studio hits such as the "Night at the Museum" series and the "Harry Potter" films.
  • B. David Burrows
    David Burrows is a film editor best known for his work on major animated features, including The Lego Movie.
  • C. Andrew Barker
    Andrew Barker is a British electronic musician best known as a member of the influential Manchester acid house and techno group 808 State.
  • D. Dan Janvey
    Dan Janvey is an American film producer known for his work on acclaimed independent films, including the Academy Award–winning "Nomadland."
  • E. Leo Barnes
    Leo Barnes is a former police sergeant turned security chief who becomes a key resistance figure fighting to end the annual Purge in the dystopian horror-thriller film series.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Daniel Barnz
Triple: [Cake (2014 film), director, Daniel Barnz]
Generated description
Daniel Barnz is an American film director and screenwriter known for character-driven dramas such as "Cake" and "Won't Back Down."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Daniel Barnz
Target entity description: Daniel Barnz is an American film director and screenwriter known for character-driven dramas such as "Cake" and "Won't Back Down."
  • A. Michael Barnathan
    Michael Barnathan is an American film producer known for working on major studio hits such as the "Night at the Museum" series and the "Harry Potter" films.
  • B. David Burrows
    David Burrows is a film editor best known for his work on major animated features, including The Lego Movie.
  • C. Andrew Barker
    Andrew Barker is a British electronic musician best known as a member of the influential Manchester acid house and techno group 808 State.
  • D. Dan Janvey
    Dan Janvey is an American film producer known for his work on acclaimed independent films, including the Academy Award–winning "Nomadland."
  • E. Leo Barnes
    Leo Barnes is a former police sergeant turned security chief who becomes a key resistance figure fighting to end the annual Purge in the dystopian horror-thriller film series.
  • F. None of above. chosen

Provenance (5 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_69d6aa5bd7c08190a816e733b4045c23 completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d6fcc30be481909922844b539b622d completed April 9, 2026, 1:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69dbacf1a43c8190869f4a64f9d6a26c completed April 12, 2026, 2:32 p.m.
NEDg Description generation batch_69dbaeb211088190a9118c71918584e5 completed April 12, 2026, 2:39 p.m.
NED2 Entity disambiguation (via description) batch_69dbaf7c999c819097a8cdf5bd82f648 completed April 12, 2026, 2:43 p.m.
Created at: April 8, 2026, 9:10 p.m.