Triple

T17025903
Position Surface form Disambiguated ID Type / Status
Subject Encounters of the Spooky Kind E413061 entity
Predicate influenced P9 FINISHED
Object Mr. Vampire E413062 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: Mr. Vampire | Statement: [Encounters of the Spooky Kind, influenced, Mr. Vampire]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mr. Vampire
Context triple: [Encounters of the Spooky Kind, influenced, Mr. Vampire]
  • A. Mr. Vampire chosen
    Mr. Vampire is a classic 1985 Hong Kong horror-comedy film that popularized the hopping vampire (jiangshi) genre and became a landmark of Chinese supernatural cinema.
  • B. Vampiro
    Vampiro is a Canadian professional wrestler and commentator known for his dark, gothic persona and influential roles in Mexican and American lucha libre promotions.
  • C. The Vampire
    "The Vampire" is a film featuring actress Lydia Reed, known for her work in mid-20th-century American cinema.
  • D. The Vampire
    The Vampire is a famous 1897 painting by Philip Burne-Jones depicting a seductive female vampire looming over a male victim, often associated with themes of fatal attraction and the femme fatale.
  • E. Vampirina
    Vampirina is an animated Disney Junior television series that follows a young vampire girl adjusting to life in the human world after moving from Transylvania to Pennsylvania.
  • 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_69d886cc4170819093deddc7b8b4b6a7 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d5d46a5081908bc5681621dd8534 completed April 18, 2026, 7:04 p.m.
NED1 Entity disambiguation (via context triple) batch_6a011b53306081908972a0a5db474b6c completed May 10, 2026, 11:57 p.m.
Created at: April 10, 2026, 5:33 a.m.