Triple

T9117129
Position Surface form Disambiguated ID Type / Status
Subject Cesar Romero E218748 entity
Predicate name P16 FINISHED
Object Cesar Romero E218748 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: Cesar Romero | Statement: [Cesar Romero, name, Cesar Romero]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cesar Romero
Context triple: [Cesar Romero, name, Cesar Romero]
  • A. Cesar Romero chosen
    Cesar Romero was an American actor and dancer best known for his suave supporting roles in classic Hollywood films and for portraying the Joker in the 1960s Batman television series.
  • B. Victor Jory
    Victor Jory was a Canadian-born American character actor known for his distinctive deep voice and frequent portrayals of villains in film, television, and theater during the mid-20th century.
  • C. Lionel Atwill
    Lionel Atwill was an English-American character actor best known for his sinister roles in 1930s and 1940s horror and mystery films.
  • D. 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.
  • E. Bela Lugosi
    Bela Lugosi was a Hungarian-American actor best known for his iconic portrayal of Count Dracula in early horror cinema.
  • 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_69ca83dc94ac8190b9ef42684d36ff39 completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69cca8a4c9e08190ba3603a5d00afb20 completed April 1, 2026, 5:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69d0307299ec8190acade4f388642e23 completed April 3, 2026, 9:26 p.m.
Created at: March 30, 2026, 7:17 p.m.