Triple

T22046270
Position Surface form Disambiguated ID Type / Status
Subject Gurudongmar Lake E544769 entity
Predicate nearbySettlement P350 FINISHED
Object Lachen 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: Lachen | Statement: [Gurudongmar Lake, nearbySettlement, Lachen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lachen
Context triple: [Gurudongmar Lake, nearbySettlement, Lachen]
  • A. Lachen
    Lachen is a small district or locality within the Bavarian municipality of Dießen am Ammersee in Germany.
  • B. Lachen chosen
    Lachen is a small town in the North Sikkim district of India, known as a gateway to high Himalayan regions and nearby scenic river valleys.
  • C. Frümsel
    Frümsel is a prominent mountain peak in the Churfirsten range of the Swiss Alps, known for its steep limestone faces and panoramic views over Lake Walen.
  • D. Heiteren
    Heiteren is a small commune in the Haut-Rhin department of the Grand Est region in northeastern France.
  • E. Rottenegg
    Rottenegg is a small village that forms one of the local subdivisions of the Bavarian town of Geisenfeld in Germany.
  • 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_69e11e32445c8190ab97089b48a130bb completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f1282f4a448190bca55348c457a4bd completed April 28, 2026, 9:35 p.m.
Created at: April 16, 2026, 8:26 p.m.