Triple

T5944607
Position Surface form Disambiguated ID Type / Status
Subject Bellach E132248 entity
Predicate neighboringMunicipality P17964 FINISHED
Object Langendorf E166452 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: Langendorf | Statement: [Bellach, neighboringMunicipality, Langendorf]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Langendorf
Context triple: [Bellach, neighboringMunicipality, Langendorf]
  • A. Langendorf chosen
    Langendorf is a municipality in the canton of Solothurn in northwestern Switzerland.
  • B. Leutenberg
    Leutenberg is a small town in the German state of Thuringia, known for its location in the Thuringian Slate Mountains and its historical sites.
  • C. Lippendorf
    Lippendorf is a village in Saxony, Germany, historically notable as the birthplace of Katharina von Bora, the wife of Martin Luther.
  • D. Köstendorf
    Köstendorf is a small Austrian municipality in the state of Salzburg, known for its rural character and proximity to the city of Salzburg.
  • E. Zinnowitz
    Zinnowitz is a seaside resort town on Germany’s Baltic Sea coast, known for its sandy beaches, historic spa architecture, and tourism on the island of Usedom.
  • 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_69c00869d3308190af89b2453e0f7546 completed March 22, 2026, 3:19 p.m.
NER Named-entity recognition batch_69c03937b4a88190819a1fd63fc3d3ed completed March 22, 2026, 6:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69c1135303bc81909f78f6d8d39c7de6 completed March 23, 2026, 10:17 a.m.
Created at: March 22, 2026, 4:01 p.m.