Triple

T19456287
Position Surface form Disambiguated ID Type / Status
Subject Bus 174 E486739 entity
Predicate reviewedBy P1394 FINISHED
Object Variety 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: Variety | Statement: [Bus 174, reviewedBy, Variety]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Variety
Context triple: [Bus 174, reviewedBy, Variety]
  • A. Variety chosen
    Variety is a leading American entertainment trade magazine and website known for its coverage of film, television, theater, and the broader media industry.
  • B. Variété
    Variété is a multi-volume collection of essays by French writer Paul Valéry, blending literary criticism, philosophy, and reflections on art and culture.
  • C. Variety Speak
    "Variety Speak" is a comedic musical number from Animaniacs in which Yakko Warner humorously riffs on show-business jargon and entertainment-industry lingo.
  • D. Variety Girl
    Variety Girl is a 1947 Hollywood musical comedy film known for its star-studded Paramount studio cast and lighthearted, behind-the-scenes showbiz story.
  • E. At the Movies
    At the Movies was an Australian film review television program best known for its long-running co-hosts Margaret Pomeranz and David Stratton, who offered in-depth critiques and discussions of new cinema releases.
  • 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_69d8e8d86d608190bd199a98d0297f27 completed April 10, 2026, 12:11 p.m.
NER Named-entity recognition batch_69e633c4088881908f23f25a82a513f6 completed April 20, 2026, 2:10 p.m.
Created at: April 10, 2026, 1:38 p.m.