Triple

T5392812
Position Surface form Disambiguated ID Type / Status
Subject Balm bei Günsberg E120373 entity
Predicate hasNeighboringMunicipality P224 FINISHED
Object Günsberg E183750 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: Günsberg | Statement: [Balm bei Günsberg, hasNeighboringMunicipality, Günsberg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Günsberg
Context triple: [Balm bei Günsberg, hasNeighboringMunicipality, Günsberg]
  • A. Günsberg chosen
    Günsberg is a Swiss municipality located in the canton of Solothurn, known for its scenic setting near the Jura Mountains.
  • B. Gernsbach
    Gernsbach is a historic town in southwestern Germany’s Black Forest region, known for its medieval old town and picturesque setting along the Murg River.
  • C. Herrenberg
    Herrenberg is a historic town in the German state of Baden-Württemberg, known for its well-preserved medieval center and proximity to the Schönbuch Nature Park.
  • D. Vellinghausen
    Vellinghausen is a village in western Germany known historically as the site of the Battle of Vellinghausen during the Seven Years' War.
  • E. Hettstadt
    Hettstadt is a small municipality in the Würzburg district of Bavaria, Germany, known for its rural character and proximity to the city of Würzburg.
  • 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_69bd46354c648190a38b26f107010a96 completed March 20, 2026, 1:05 p.m.
NER Named-entity recognition batch_69bd871b81d08190993928e2c6251226 completed March 20, 2026, 5:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69bfea92b36081909aa6e33b8b572f90 completed March 22, 2026, 1:11 p.m.
Created at: March 20, 2026, 2:04 p.m.