Triple

T13416595
Position Surface form Disambiguated ID Type / Status
Subject Wuppertal-Langerfeld-Beyenburg E313230 entity
Predicate contains P35 FINISHED
Object Beyenburg E681490 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: Beyenburg | Statement: [Wuppertal-Langerfeld-Beyenburg, contains, Beyenburg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Beyenburg
Context triple: [Wuppertal-Langerfeld-Beyenburg, contains, Beyenburg]
  • A. Beyenburg chosen
    Beyenburg is a historic district in the eastern part of Wuppertal, Germany, known for its medieval monastery, reservoir, and well-preserved village character.
  • B. Brenkhausen
    Brenkhausen is a village and district of the town of Höxter in North Rhine-Westphalia, Germany.
  • C. Braunsberg
    Braunsberg is a locality in former East Prussia (now in Poland) known for its proximity to the World War II Heiligenbeil pocket battlefield.
  • D. Faulbach
    Faulbach is a district (Ortsteil) of the town of Hadamar in the Limburg-Weilburg district of Hesse, Germany.
  • E. Biesenthal
    Biesenthal is a small town in the Barnim district of Brandenburg, Germany, known for its surrounding lakes, forests, and location within the Barnim Nature Park.
  • 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_69d806ad0c44819088833ae1ec9e9690 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69dbaeb6e904819098cc9153fd2feaf5 completed April 12, 2026, 2:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69f75475d7648190b050e44a5312d13d completed May 3, 2026, 1:58 p.m.
Created at: April 9, 2026, 9:39 p.m.