Triple

T21993522
Position Surface form Disambiguated ID Type / Status
Subject Black Book E543146 entity
Predicate producer P490 FINISHED
Object Jens Meurer 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: Jens Meurer | Statement: [Black Book, producer, Jens Meurer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jens Meurer
Context triple: [Black Book, producer, Jens Meurer]
  • A. Jens Meurer chosen
    Jens Meurer is a German film producer and documentary filmmaker known for his work on international arthouse and historical dramas.
  • B. Christian Lemmerz
    Christian Lemmerz is a contemporary German-Danish artist known for his provocative sculptures and installations that often explore themes of mortality, religion, and the human body.
  • C. Markus Oberhumer
    Markus Oberhumer is an Austrian software developer best known as the creator of the UPX executable packer and contributor to various open-source compression and optimization tools.
  • D. Jochen Theodorou
    Jochen Theodorou is a core contributor and long-time maintainer of the Groovy programming language, known for shaping its design and implementation in the JVM ecosystem.
  • E. Michael Grunst
    Michael Grunst is a German local politician who serves as the borough mayor of Berlin’s Lichtenberg district.
  • 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_69e11e2c814c8190837d072789000486 completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f1270f77fc8190aadcc02760d65ac0 completed April 28, 2026, 9:30 p.m.
Created at: April 16, 2026, 8:17 p.m.