Triple

T8033661
Position Surface form Disambiguated ID Type / Status
Subject canton of Obwalden E187048 entity
Predicate containsMunicipality P852 FINISHED
Object Lungern E428451 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: Lungern | Statement: [canton of Obwalden, containsMunicipality, Lungern]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lungern
Context triple: [canton of Obwalden, containsMunicipality, Lungern]
  • A. Lungern chosen
    Lungern is a picturesque Swiss village in the canton of Obwalden, known for its lakeside setting amid alpine mountains and its popularity for outdoor recreation.
  • B. Langenau
    Langenau is a small town in the Alb-Donau district of Baden-Württemberg in southern Germany, known for its historic center and proximity to the Swabian Jura.
  • C. Lülsfeld
    Lülsfeld is a small municipality in the Schweinfurt district of Lower Franconia in northern Bavaria, Germany.
  • D. Langenberg
    Langenberg is a prominent mountain in the Rothaargebirge range of Germany, known as the highest peak in the state of North Rhine-Westphalia.
  • E. Lohmar
    Lohmar is a town in the Rhein-Sieg district of North Rhine-Westphalia, Germany, situated near Cologne and known for its green surroundings and residential character.
  • 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_69ca82ae2d1081909dbfee42b41db419 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3ef3e6848190913c4b1bef506aae completed March 31, 2026, 3:26 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc56ede1ac8190afd8a050e9a25851 completed March 31, 2026, 11:21 p.m.
Created at: March 30, 2026, 5:22 p.m.