Triple

T20460985
Position Surface form Disambiguated ID Type / Status
Subject Alexandra Maria Lara E501920 entity
Predicate workedWith P398 FINISHED
Object Bruno Ganz NE NERFINISHED

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: Bruno Ganz | Statement: [Alexandra Maria Lara, workedWith, Bruno Ganz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bruno Ganz
Context triple: [Alexandra Maria Lara, workedWith, Bruno Ganz]
  • A. Bruno Ganz chosen
    Bruno Ganz was a renowned Swiss actor acclaimed for his intense and nuanced performances in European cinema, particularly in films like "Wings of Desire" and "Downfall."
  • B. Jürgen Menzel
    Jürgen Menzel is a person notable enough to be recognized as a significant bearer of the surname Menzel.
  • C. Klaus Maria Brandauer
    Klaus Maria Brandauer is an acclaimed Austrian actor and director known internationally for his intense, charismatic performances in films such as "Out of Africa" and "Mephisto."
  • D. Klaus Menzel
    Klaus Menzel is a notable individual who shares the surname Menzel, recognized enough to be specifically distinguished among bearers of the name.
  • E. Armin Mueller-Stahl
    Armin Mueller-Stahl is a German actor and former East German film star known internationally for his versatile performances in both European cinema and Hollywood films.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4ad4940819098cf2ff6413574e5 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e696a642648190808709020f91122b completed April 20, 2026, 9:12 p.m.
Created at: April 16, 2026, 11:33 a.m.