Triple

T19867752
Position Surface form Disambiguated ID Type / Status
Subject Noir Alley E477433 entity
Predicate hasHost P2592 FINISHED
Object Eddie Muller NE NERFINISHED

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: Eddie Muller | Statement: [Noir Alley, hasHost, Eddie Muller]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eddie Muller
Context triple: [Noir Alley, hasHost, Eddie Muller]
  • A. Eddie Muller chosen
    Eddie Muller is an American film historian, author, and television host best known for presenting and curating classic film noir, particularly through his work on Turner Classic Movies.
  • B. Anthony DeCurtis
    Anthony DeCurtis is an American music critic, author, and longtime Rolling Stone contributor known for his influential writing on popular music and culture.
  • C. Paul Feldman
    Paul Feldman is a computer scientist and cryptographer known for his work on digital signatures and other foundational topics in modern cryptography.
  • D. Philip Franks
    Philip Franks is a British actor and director best known for his television work, including a prominent role in the popular series "The Darling Buds of May."
  • E. Peter Knobler
    Peter Knobler is an American writer and journalist best known for co-authoring numerous celebrity and political memoirs.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e51e7d948190aedbcd6c30361c39 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e658a0a7288190a82b85e3ae056d6b completed April 20, 2026, 4:47 p.m.
Created at: April 10, 2026, 1:51 p.m.