Triple

T14943912
Position Surface form Disambiguated ID Type / Status
Subject Miss Congeniality E372602 entity
Predicate cinematographyBy 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: [Miss Congeniality, cinematographyBy, László Kovács]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: László Kovács
Context triple: [Miss Congeniality, cinematographyBy, 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. Gyula Jankovits
    Gyula Jankovits was a Hungarian sculptor best known for creating prominent public monuments in Budapest, including the Gellért Monument.
  • C. László Papp
    László Papp was a legendary Hungarian boxer who became the first boxer to win three consecutive Olympic gold medals.
  • D. László Nagy
    László Nagy is a common Hungarian name shared by several notable figures, including a poet, a handball player, and a politician.
  • E. 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.
  • 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_69d85cc9da0c81908d583ca3f63a3908 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded68d20048190a403af85fe43dede completed April 15, 2026, 12:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff1a5f38488190b441dd0b385024b1 completed May 9, 2026, 11:28 a.m.
Created at: April 10, 2026, 2:38 a.m.