Triple

T2294797
Position Surface form Disambiguated ID Type / Status
Subject William Friedkin E51585 entity
Predicate directed P7373 FINISHED
Object Bug E253569 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: Bug | Statement: [William Friedkin, directed, Bug]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bug
Context triple: [William Friedkin, directed, Bug]
  • A. Bug
    The Volkswagen Beetle, commonly called the Bug, is an iconic compact car originally designed in the 1930s that became one of the best-selling and most recognizable automobiles in history.
  • B. Bug chosen
    Bug is a 2006 psychological horror film directed by William Friedkin, adapted from Tracy Letts' play about paranoia and delusion consuming two isolated characters in a seedy motel room.
  • C. Bugzilla
    Bugzilla is an open-source, web-based bug tracking and issue management system widely used by software development projects to report, track, and resolve defects.
  • D. Buzz
    Buzz is the nickname of Edwin "Buzz" Aldrin, the American astronaut who became the second person to walk on the Moon during the Apollo 11 mission.
  • E. Buzz
    Buzz is the yellow jacket costumed mascot of the Georgia Institute of Technology, known for energizing crowds at the university’s athletic and campus events.
  • 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_69a88b09c644819090b503456d96bf70 completed March 4, 2026, 7:42 p.m.
NER Named-entity recognition batch_69abc5da667881909186adf23a2bd45b completed March 7, 2026, 6:29 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae8952322c81909d58b89139f51a27 completed March 9, 2026, 8:48 a.m.
Created at: March 4, 2026, 7:49 p.m.