Triple

T15297126
Position Surface form Disambiguated ID Type / Status
Subject Tarzan (2013 film) E365688 entity
Predicate voiceActor P1507 FINISHED
Object Mark Deklin E1103793 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: Mark Deklin | Statement: [Tarzan (2013 film), voiceActor, Mark Deklin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mark Deklin
Context triple: [Tarzan (2013 film), voiceActor, Mark Deklin]
  • A. Mark Deklin chosen
    Mark Deklin is an American actor and fight coordinator known for his work in television dramas and soap operas, including roles on series like "Devious Maids" and "GCB."
  • B. Mark Derwin
    Mark Derwin is an American actor best known for his television roles in soap operas and family dramas, including a prominent part on *The Secret Life of the American Teenager*.
  • C. Garth Drabinsky
    Garth Drabinsky is a Canadian theatrical producer and former film executive best known for staging large-scale Broadway and international productions, including the musical "Ragtime."
  • D. Martin Brinkler
    Martin Brinkler is a film editor known for his work on the shark thriller "47 Meters Down: Uncaged."
  • E. Jonathan Hadary
    Jonathan Hadary is an American stage and screen actor known for his work in Broadway productions and various film and television roles.
  • 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_69d85a113ee881908e297a1d38dd79fa completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e036848c1881908fbaaae0216d6d27 completed April 16, 2026, 1:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff4542d4308190bebf13dff1ebfe08 completed May 9, 2026, 2:31 p.m.
Created at: April 10, 2026, 3:15 a.m.