Triple
T15751292
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Andselv |
E381850
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Bardufoss |
—
|
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: Bardufoss | Statement: [Andselv, near, Bardufoss]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bardufoss Context triple: [Andselv, near, Bardufoss]
-
A.
Bardufoss
chosen
Bardufoss is a town in northern Norway known for its military base, including the main headquarters of the Norwegian Army in the region, and its nearby airport.
-
B.
Nordfjordeid
Nordfjordeid is a village in western Norway known as a regional center in Nordfjord and the birthplace of mathematician Sophus Lie.
-
C.
Balestrand
Balestrand is a picturesque village in western Norway known for its fjordside scenery, historic wooden hotels, and role as a gateway to exploring the Sognefjord region.
-
D.
Raufoss
Raufoss is an industrial town in Norway known for its manufacturing sector, particularly in defense and automotive components.
-
E.
Farsund
Farsund is a coastal town and municipality in southern Norway known for its maritime heritage, beaches, and historic wooden architecture.
- 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_69d86d9e6b44819085d1f6a969ecb74c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e05030e31081908c307a8dc7067db4 |
completed | April 16, 2026, 2:57 a.m. |
Created at: April 10, 2026, 4:47 a.m.