Triple

T5747044
Position Surface form Disambiguated ID Type / Status
Subject The Black Sleep E126760 entity
Predicate hasCastMember P2308 FINISHED
Object Bela Lugosi E116562 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: Bela Lugosi | Statement: [The Black Sleep, hasCastMember, Bela Lugosi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bela Lugosi
Context triple: [The Black Sleep, hasCastMember, Bela Lugosi]
  • A. Bela Lugosi chosen
    Bela Lugosi was a Hungarian-American actor best known for his iconic portrayal of Count Dracula in early horror cinema.
  • B. Bela Lugosi Jr.
    Bela Lugosi Jr. is an American attorney and the son of legendary horror film actor Bela Lugosi, known for his legal work related to his father's legacy and likeness rights.
  • C. Boris Karloff
    Boris Karloff was an English actor best known for his iconic portrayals in classic horror films, particularly as Frankenstein's monster in the 1931 film "Frankenstein."
  • D. Vincent Price
    Vincent Price was an American actor renowned for his distinctive voice and charismatic presence, particularly in classic horror films and gothic dramas.
  • E. Lionel Atwill
    Lionel Atwill was an English-American character actor best known for his sinister roles in 1930s and 1940s horror and mystery 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_69c0083179548190b384b0bf3c08ca4d completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c02885b0288190835809681a364b1f completed March 22, 2026, 5:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69c07e2c54c0819087ab79ae855b9677 completed March 22, 2026, 11:41 p.m.
Created at: March 22, 2026, 3:48 p.m.