Triple

T10039995
Position Surface form Disambiguated ID Type / Status
Subject New York, New York E205269 entity
Predicate cinematographer P1953 FINISHED
Object László Kovács E260960 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: László Kovács | Statement: [New York, New York, cinematographer, László Kovács]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: László Kovács
Context triple: [New York, New York, cinematographer, László Kovács]
  • A. László Kovács chosen
    László Kovács was a renowned Hungarian-American cinematographer celebrated for his influential work in New Hollywood cinema, including landmark films of the late 1960s and 1970s.
  • B. László Papp
    László Papp was a legendary Hungarian boxer who became the first boxer to win three consecutive Olympic gold medals.
  • C. László Nagy
    László Nagy is a common Hungarian name shared by several notable figures, including a poet, a handball player, and a politician.
  • D. Gábor Vajna
    Gábor Vajna was a Hungarian fascist politician who served as Interior Minister in the pro-Nazi Arrow Cross regime during World War II.
  • E. László Bárdossy
    László Bárdossy was a Hungarian politician who served as prime minister during World War II and played a key role in aligning Hungary with Nazi Germany.
  • 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_69ca834f70e88190b2d74828b7767ec1 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cdcee186708190bc9fecd637b4f7e6 completed April 2, 2026, 2:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69d74fc3f9608190b4472b2b87009cca completed April 9, 2026, 7:05 a.m.
Created at: March 30, 2026, 8:55 p.m.