Triple

T13701146
Position Surface form Disambiguated ID Type / Status
Subject The Pest E328519 entity
Predicate cinematographyBy P1953 FINISHED
Object John B. Aronson E732730 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: John B. Aronson | Statement: [The Pest, cinematographyBy, John B. Aronson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John B. Aronson
Context triple: [The Pest, cinematographyBy, John B. Aronson]
  • A. John Aronson chosen
    John Aronson is a cinematographer best known for his work on the World War II aviation film "Red Tails."
  • B. Robert G. Goldstein
    Robert G. Goldstein is an American business executive best known as the chief executive officer and a leading figure of the global casino and resort company Las Vegas Sands.
  • C. Neil A. Machlis
    Neil A. Machlis is a film producer best known for his work on major Hollywood comedies, including the classic road-trip film "Planes, Trains and Automobiles."
  • D. Paul S. Rosenbloom
    Paul S. Rosenbloom is a computer scientist and cognitive scientist known for his foundational work in cognitive architectures and artificial intelligence.
  • E. Edward A. Blatt
    Edward A. Blatt was an American film director and editor active in Hollywood during the mid-20th century, known for his work on studio features and genre films.
  • 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_69d8076ff62081908a7bd79889edd7a0 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbc879adc88190b03f1cf815b71061 completed April 12, 2026, 4:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69f794575d3881908de6ed988d848918 completed May 3, 2026, 6:30 p.m.
Created at: April 9, 2026, 9:54 p.m.