Triple

T14684869
Position Surface form Disambiguated ID Type / Status
Subject Fright Nights E344883 entity
Predicate theme P261 FINISHED
Object Halloween E83570 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: Halloween | Statement: [Fright Nights, theme, Halloween]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Halloween
Context triple: [Fright Nights, theme, Halloween]
  • A. Halloween chosen
    Halloween is an annual celebration observed on October 31, characterized by costumes, trick-or-treating, spooky decorations, and themes of the supernatural and the macabre.
  • B. Trick or Treat
    "Trick or Treat" is a 1986 horror-comedy film about a heavy metal-obsessed teenager who unwittingly resurrects a dead rock star through a cursed record, directed by Charles Martin Smith.
  • C. One Halloween
    "One Halloween" is a song featured on the musical theatre album *Applause*.
  • D. Trick 'r Treat
    Trick 'r Treat is a 2007 anthology horror film that interweaves multiple Halloween-themed stories and has become a cult favorite for its darkly comedic tone and inventive storytelling.
  • E. 31 Nights of Halloween
    31 Nights of Halloween is Freeform’s annual month-long programming event featuring Halloween-themed movies, specials, and family-friendly spooky content.
  • 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_69d822e34b348190ada4d1cdb6c7c226 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb56bdb8081909ff86440ba20fb1f completed April 14, 2026, 9:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69fde18592088190892ae1cc371165be completed May 8, 2026, 1:13 p.m.
Created at: April 10, 2026, 1:28 a.m.