Triple

T14567229
Position Surface form Disambiguated ID Type / Status
Subject Amour E341814 entity
Predicate partOf P40 FINISHED
Object Michael Haneke filmography E265954 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: Michael Haneke filmography | Statement: [Amour, partOf, Michael Haneke filmography]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Haneke filmography
Context triple: [Amour, partOf, Michael Haneke filmography]
  • A. Michael Haneke chosen
    Michael Haneke is an acclaimed Austrian film director and screenwriter known for his austere, unsettling dramas that critically examine modern society and human psychology.
  • B. Susanne Haneke
    Susanne Haneke is the wife of acclaimed Austrian filmmaker Michael Haneke.
  • C. Schygulla
    Schygulla is a German surname most famously borne by actress Hanna Schygulla, a prominent figure in New German Cinema.
  • D. The Piano Teacher
    The Piano Teacher is a dark psychological novel by Austrian Nobel laureate Elfriede Jelinek that explores repression, sexuality, and power through the disturbed inner life of a Vienna piano instructor.
  • E. Hungarian cinema
    Hungarian cinema is the national film industry and artistic tradition of Hungary, known for its influential directors, distinctive storytelling, and contributions to both European and world 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_69d822dcc6248190bed689984bceb0e2 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb38d89fc819086709fd3607b835f completed April 14, 2026, 9:37 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd8ac669cc819083e05620b1e8c370 completed May 8, 2026, 7:03 a.m.
Created at: April 10, 2026, 1:23 a.m.