Triple

T14011215
Position Surface form Disambiguated ID Type / Status
Subject Lake Lungern E337081 entity
Predicate hasNearbySettlement P4647 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: [Lake Lungern, hasNearbySettlement, Lungern]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lungern
Context triple: [Lake Lungern, hasNearbySettlement, 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. Langenfeld
    Langenfeld is a town in western Germany, located in the state of North Rhine-Westphalia between Düsseldorf and Cologne.
  • 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_69d81c645c5c8190b1fd16a285a1b78a completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2ed5cfd0819085b9c860b119a9de completed April 14, 2026, 12:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd27fb2e6c81909e358862e012c49b completed May 8, 2026, 12:02 a.m.
Created at: April 9, 2026, 10:19 p.m.