Triple

T13214171
Position Surface form Disambiguated ID Type / Status
Subject Büchenbach E314566 entity
Predicate partOfUrbanArea P294 FINISHED
Object Erlangen-West E295851 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: Erlangen-West | Statement: [Büchenbach, partOfUrbanArea, Erlangen-West]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Erlangen-West
Context triple: [Büchenbach, partOfUrbanArea, Erlangen-West]
  • A. Oranienburger Vorstadt
    Oranienburger Vorstadt is a historic neighborhood in central Berlin, known for its 19th-century urban fabric, cultural sites, and proximity to key political and intellectual centers of the city.
  • B. Roßdorf
    Roßdorf is a municipality in the German state of Hesse, located near the city of Darmstadt.
  • C. Ludwigsfelde
    Ludwigsfelde is a town in the German state of Brandenburg, located just south of Berlin and known for its industrial history and automotive manufacturing.
  • D. Erlangen-Höchstadt chosen
    Erlangen-Höchstadt is a rural district in the Bavarian region of Middle Franconia in Germany, known for encompassing towns such as Herzogenaurach and parts of the metropolitan area around Erlangen.
  • E. Wilmersdorf
    Wilmersdorf is a residential district in southwestern Berlin known for its affluent neighborhoods, shopping streets like Kurfürstendamm, and a mix of historic and modern architecture.
  • 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_69d806aee7308190b70a237ba2a6e3e1 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98cf054f88190b05ced98d5a22a62 completed April 10, 2026, 11:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6ff1c9f348190aae39073bdcee84d completed May 3, 2026, 7:54 a.m.
Created at: April 9, 2026, 9:17 p.m.