Triple

T20024899
Position Surface form Disambiguated ID Type / Status
Subject The Amityville Horror E494953 entity
Predicate author P4 FINISHED
Object Jay Anson 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: Jay Anson | Statement: [The Amityville Horror, author, Jay Anson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jay Anson
Context triple: [The Amityville Horror, author, Jay Anson]
  • A. Jay Anson chosen
    Jay Anson was an American author best known for writing the purportedly true horror book "The Amityville Horror," which became a cultural phenomenon and inspired a long-running film franchise.
  • B. Steven Ansell
    Steven Ansell is a film editor known for his work on the feature film "She Wants Me."
  • C. Jay Jopling
    Jay Jopling is a prominent British art dealer and founder of the influential White Cube gallery, known for representing leading contemporary artists.
  • D. Andrew Rennison
    Andrew Rennison is a British public official known for serving as the inaugural Surveillance Camera Commissioner, overseeing the regulation and ethical use of CCTV and related surveillance technologies in the UK.
  • E. Jeff Nathanson
    Jeff Nathanson is an American screenwriter and film director best known for writing high-profile Hollywood films such as "Catch Me If You Can," "The Terminal," and Disney's live-action "The Lion King."
  • 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_69da626bfd288190aa5d65098b6433ae completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e6628b6b7c81909a660fbec9c92295 completed April 20, 2026, 5:29 p.m.
Created at: April 11, 2026, 3:35 p.m.