Triple

T21380595
Position Surface form Disambiguated ID Type / Status
Subject Schwanau E527341 entity
Predicate hasSubdivision P747 FINISHED
Object Wittenweier 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: Wittenweier | Statement: [Schwanau, hasSubdivision, Wittenweier]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wittenweier
Context triple: [Schwanau, hasSubdivision, Wittenweier]
  • A. Weilersbach
    Weilersbach is a small municipality in the Forchheim district of Bavaria, Germany, known for its rural character and proximity to the Franconian Switzerland region.
  • B. Appenweier chosen
    Appenweier is a municipality in southwestern Germany’s Baden-Württemberg region, situated within the Ortenau district near the Rhine and the French border.
  • C. Wieseck
    Wieseck is a small river in the German state of Hesse that flows through the city of Giessen and its surrounding region.
  • D. Kleinheubach
    Kleinheubach is a small market town in Lower Franconia, Bavaria, Germany, known for its historic noble residences and riverside setting along the Main.
  • E. Buhlbach
    Buhlbach is a small locality or district within the Black Forest municipality of Baiersbronn in southwestern Germany.
  • 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_69e0b51f363c8190944000ab5523b02b completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e8b0cdab8c8190a7eebe6e5961ee75 completed April 22, 2026, 11:28 a.m.
Created at: April 16, 2026, 5:11 p.m.