Triple

T17831344
Position Surface form Disambiguated ID Type / Status
Subject Worb E445262 entity
Predicate hasNeighboringMunicipality P224 FINISHED
Object Münsingen 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: Münsingen | Statement: [Worb, hasNeighboringMunicipality, Münsingen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Münsingen
Context triple: [Worb, hasNeighboringMunicipality, 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. Menzingen
    Menzingen is a municipality in the canton of Zug in central Switzerland, known for its rural landscape and location in the pre-Alpine region.
  • C. Mötzingen
    Mötzingen is a small municipality in the German state of Baden-Württemberg, located in the Böblingen region of southwestern Germany.
  • D. 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.
  • E. Miesbach
    Miesbach is a historic town in southern Germany known for its traditional Bavarian culture and picturesque Alpine foothill setting.
  • 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_69d8b9f1a6d881909f024bc603111cdb completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e48d248ecc8190b3f0d001b539d960 completed April 19, 2026, 8:07 a.m.
Created at: April 10, 2026, 10:15 a.m.