Triple

T9098550
Position Surface form Disambiguated ID Type / Status
Subject Boris Karloff E218089 entity
Predicate coStarredWith P14987 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: [Boris Karloff, coStarredWith, Bela Lugosi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bela Lugosi
Context triple: [Boris Karloff, coStarredWith, 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_69ca83d9844081908e561e367fda6d45 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cc970f340881909f5551219f151acf completed April 1, 2026, 3:54 a.m.
NED1 Entity disambiguation (via context triple) batch_69d03014ea488190abc71a0ee182bcad completed April 3, 2026, 9:24 p.m.
Created at: March 30, 2026, 7:15 p.m.