Triple
T15878176
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Magerøya |
E385002
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object | Kamøyvær |
E1127996
|
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: Kamøyvær | Statement: [Magerøya, hasSettlement, Kamøyvær]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kamøyvær Context triple: [Magerøya, hasSettlement, Kamøyvær]
-
A.
Kamøyvær
chosen
Kamøyvær is a small coastal fishing village in northern Norway, known for its picturesque harbor and Arctic scenery within Nordkapp Municipality.
-
B.
Skreda
Skreda is a small village located on the island municipality of Vestvågøy in Norway’s Lofoten archipelago.
-
C.
Blizne
Blizne is a village in southeastern Poland best known for its historic wooden All Saints Church, a UNESCO World Heritage Site.
-
D.
Sykkylven
Sykkylven is a municipality in Møre og Romsdal county, Norway, known for its fjord landscape and strong furniture manufacturing industry.
-
E.
Oppstryn
Oppstryn is a village in Stryn Municipality in Vestland county, Norway, known for its scenic location near Oppstrynsvatnet lake and surrounding mountains.
- 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_69d86da4e86481909f1325fdc971b5ec |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e155fec9d4819081efea504e1e3952 |
completed | April 16, 2026, 9:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffb043b6d48190bf9a36a3e00403c0 |
completed | May 9, 2026, 10:08 p.m. |
Created at: April 10, 2026, 4:51 a.m.