Triple
T3927742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bern metropolitan area |
E93317
|
entity |
| Predicate | contains |
P35
|
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: [Bern metropolitan area, contains, Thun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thun Context triple: [Bern metropolitan area, contains, 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.
Sihlwald
Sihlwald is a large forest and nature reserve near Zurich, Switzerland, known for its protected, near-natural woodland and recreational hiking trails.
-
C.
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.
-
D.
Oberegg
Oberegg is a Swiss municipality in the canton of Appenzell Innerrhoden, known for its rural landscape and location in the Appenzell region.
-
E.
Saas-Fee
Saas-Fee is a high-altitude Swiss alpine village and ski resort in the Valais Alps, known for its car-free center, extensive glacier skiing, and dramatic mountain scenery.
- 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_69aed96bfa1081908f7b30f2c647dee6 |
completed | March 9, 2026, 2:30 p.m. |
| NER | Named-entity recognition | batch_69aeeda4f9d481908dda1b5a826ab64d |
completed | March 9, 2026, 3:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b589c2e33c8190af908a6a021a2a82 |
completed | March 14, 2026, 4:16 p.m. |
Created at: March 9, 2026, 3:23 p.m.