Triple

T23247249
Position Surface form Disambiguated ID Type / Status
Subject Forchtenberg E581613 entity
Predicate hasSubdivision P747 FINISHED
Object Wohlmuthausen 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: Wohlmuthausen | Statement: [Forchtenberg, hasSubdivision, Wohlmuthausen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Wohlmuthausen
Context triple: [Forchtenberg, hasSubdivision, Wohlmuthausen]
  • A. Wohlmuthausen chosen
    Wohlmuthausen is a small village in the German state of Baden-Württemberg that now forms part of the town of Forchtenberg.
  • B. Weipertshausen
    Weipertshausen is a small locality that forms part of the municipality of Münsing in Bavaria, Germany.
  • C. Helmarshausen
    Helmarshausen is a historic district of the spa town Bad Karlshafen in northern Hesse, Germany, known for its medieval heritage and former Benedictine monastery.
  • D. Lamprechtshausen
    Lamprechtshausen is a municipality in the Austrian state of Salzburg, known for its rural character and location within the Salzburg-Umgebung region.
  • E. Wittighausen
    Wittighausen is a small municipality in the Main-Tauber district of Baden-Württemberg in southern 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_69e24606b17c81908aba1a4911c8a8ba completed April 17, 2026, 2:39 p.m.
NER Named-entity recognition batch_69f193f1e8448190b8420a8dc6e24576 completed April 29, 2026, 5:15 a.m.
Created at: April 17, 2026, 4:10 p.m.