Triple
T15760453
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lebesby |
E382081
|
entity |
| Predicate | containsSettlement |
P847
|
FINISHED |
| Object | Kjøllefjord |
—
|
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: Kjøllefjord | Statement: [Lebesby, containsSettlement, Kjøllefjord]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kjøllefjord Context triple: [Lebesby, containsSettlement, Kjøllefjord]
-
A.
Kjøllefjord
chosen
Kjøllefjord is a small coastal village in Troms og Finnmark county in northern Norway, known for its fishing industry and Arctic coastal landscape.
-
B.
Flekkefjord
Flekkefjord is a coastal town in southern Norway known for its historic wooden architecture, maritime heritage, and picturesque fjord setting.
-
C.
Sunnfjord
Sunnfjord is a district in Vestland county in Western Norway, known for its fjords, valleys, and traditional rural communities.
-
D.
Gullesfjord
Gullesfjord is a small settlement in Kvæfjord Municipality in Troms og Finnmark county in northern Norway, situated near the fjord of the same name.
-
E.
Ranfjorden
Ranfjorden is a long, narrow fjord in Nordland county, Norway, known for its dramatic coastal landscape and the industrial town of Mo i Rana along its shores.
- 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_69e050b52c548190a0ffa4493a4eb15c |
completed | April 16, 2026, 3 a.m. |
Created at: April 10, 2026, 4:47 a.m.