Triple

T4427759
Position Surface form Disambiguated ID Type / Status
Subject Lake Thun E95249 entity
Predicate nearCity P350 FINISHED
Object Thun E113677 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: Thun | Statement: [Lake Thun, nearCity, Thun]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Thun
Context triple: [Lake Thun, nearCity, Thun]
  • A. Thun chosen
    Thun is a historic Swiss town in the canton of Bern, known for its medieval old town, lakeside setting on Lake Thun, and views of the surrounding Alps.
  • B. Brienz
    Brienz is a picturesque Swiss village in the Bernese Oberland, known for its lakeside setting on Lake Brienz and its traditional woodcarving craftsmanship.
  • C. Sihlwald
    Sihlwald is a large forest and nature reserve near Zurich, Switzerland, known for its protected, near-natural woodland and recreational hiking trails.
  • D. Kiental
    Kiental is a picturesque alpine valley and village in the Bernese Oberland region of Switzerland, known for its dramatic mountain scenery and hiking opportunities.
  • E. Oberegg
    Oberegg is a Swiss municipality in the canton of Appenzell Innerrhoden, known for its rural landscape and location in the Appenzell region.
  • 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_69b3453c2a0c8190926b574c90766db9 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b355674d5481908bf2dfd611f3520f completed March 13, 2026, 12:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69bd421ea32c8190b291d10873fcb9a6 completed March 20, 2026, 12:48 p.m.
Created at: March 12, 2026, 11:30 p.m.