Triple

T12585708
Position Surface form Disambiguated ID Type / Status
Subject Kander E300453 entity
Predicate mouthOfWaterBody P3817 FINISHED
Object Lake Thun E95249 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: Lake Thun | Statement: [Kander, mouthOfWaterBody, Lake Thun]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lake Thun
Context triple: [Kander, mouthOfWaterBody, Lake Thun]
  • A. Lake Thun chosen
    Lake Thun is a large alpine lake in the Bernese Oberland region of Switzerland, renowned for its scenic mountain backdrop, historic lakeside towns, and popular boating and water sports.
  • B. Lake Sarnen
    Lake Sarnen is a scenic alpine lake in the canton of Obwalden in central Switzerland, known for its clear waters and surrounding mountain landscapes.
  • C. Lake Brienz
    Lake Brienz is a deep, turquoise-colored alpine lake in central Switzerland, renowned for its dramatic mountain scenery and crystal-clear waters.
  • D. Oeschinen Lake
    Oeschinen Lake is a scenic alpine lake in the Bernese Oberland region of Switzerland, renowned for its turquoise waters, dramatic mountain backdrop, and outdoor recreation opportunities.
  • E. Walensee
    Walensee is a picturesque alpine lake in eastern Switzerland, known for its steep surrounding mountains and clear turquoise waters.
  • 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_69d7bde87b648190bcd0266e9efde098 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d954bac9708190ad8f277e7751bf5c completed April 10, 2026, 7:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69f75d78433081909ae0278e9e1abacb completed May 3, 2026, 2:36 p.m.
Created at: April 9, 2026, 5:04 p.m.