Triple

T5050477
Position Surface form Disambiguated ID Type / Status
Subject Bern–Thun railway line E113771 entity
Predicate hasStation P35 FINISHED
Object Münsingen E427362 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: Münsingen | Statement: [Bern–Thun railway line, hasStation, Münsingen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Münsingen
Context triple: [Bern–Thun railway line, hasStation, Münsingen]
  • A. Münsingen chosen
    Münsingen is a Swiss municipality in the canton of Bern, known for its scenic location in the Aare valley between Bern and Thun.
  • B. Memmingen
    Memmingen is a historic town in the Bavarian region of Germany, known for its well-preserved medieval old town and role as a regional transport hub.
  • C. Miesbach
    Miesbach is a historic town in southern Germany known for its traditional Bavarian culture and picturesque Alpine foothill setting.
  • D. Münklingen
    Münklingen is a village and district of the town Weil der Stadt in the German state of Baden-Württemberg.
  • E. Möhringen
    Möhringen is a district of Stuttgart in the German state of Baden-Württemberg, known as a residential area that also hosts U.S. military facilities.
  • 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_69bd44391fc48190a311ce9c826c209b completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd7425df74819091cfde348dd16a68 completed March 20, 2026, 4:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69beba5ec4308190aff8b1c4e494e0e2 completed March 21, 2026, 3:33 p.m.
Created at: March 20, 2026, 1:37 p.m.