Triple

T15379984
Position Surface form Disambiguated ID Type / Status
Subject Juliet Landau E367772 entity
Predicate parent P120 FINISHED
Object Martin Landau E78240 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: Martin Landau | Statement: [Juliet Landau, parent, Martin Landau]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Martin Landau
Context triple: [Juliet Landau, parent, Martin Landau]
  • A. Martin Landau chosen
    Martin Landau was an American actor renowned for his versatile film and television roles, including an Oscar-winning performance as Bela Lugosi in "Ed Wood."
  • B. Paul Frees
    Paul Frees was a prolific American voice actor known for his work in classic animated films, television specials, and theme park attractions, often referred to as "The Man of a Thousand Voices."
  • C. John Dehner
    John Dehner was an American character actor known for his prolific work in Western films and television series during the mid-20th century.
  • D. Seymour Cassel
    Seymour Cassel was an American character actor known for his longtime collaboration with director John Cassavetes and his roles in numerous independent and mainstream films.
  • E. Hans Conried
    Hans Conried was an American character actor and voice actor best known for his comedic and often villainous roles in mid-20th-century film, radio, television, and animation.
  • 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_69d85a1551a08190ba2caea7cd51c639 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03e6044488190b0499db109f7f821 completed April 16, 2026, 1:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff1347c8448190aa1088d66bca2722 completed May 9, 2026, 10:58 a.m.
Created at: April 10, 2026, 3:19 a.m.