Triple

T4300213
Position Surface form Disambiguated ID Type / Status
Subject Sopron E99815 entity
Predicate hasTwinTown P919 FINISHED
Object Kempten E263855 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: Kempten | Statement: [Sopron, hasTwinTown, Kempten]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kempten
Context triple: [Sopron, hasTwinTown, Kempten]
  • A. Kempten chosen
    Kempten is a historic town in Bavaria, Germany, considered one of the country’s oldest urban settlements and known for its location in the Allgäu region.
  • B. Rosenheim
    Rosenheim is a town in Upper Bavaria, Germany, known as a regional economic and transportation hub near the Alps.
  • C. Füssen
    Füssen is a picturesque Bavarian town in southern Germany, known for its historic old town, proximity to Neuschwanstein Castle, and scenic location near the Alps.
  • D. Traunstein
    Traunstein is a town in southeastern Bavaria, Germany, known as a regional administrative and cultural center near the Chiemsee and the Alps.
  • E. Passau
    Passau is a historic city in southeastern Germany, renowned for its picturesque old town and location at the meeting point of three rivers: the Danube, Inn, and Ilz.
  • 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_69b345528ebc8190b5abc7e95094792d completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3509e8cb481909ccca7992aac31a3 completed March 12, 2026, 11:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69c097a5c6a4819082f4b9bf113ea33b completed March 23, 2026, 1:30 a.m.
Created at: March 12, 2026, 11:08 p.m.