Triple
T17615009
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alstahaug |
E429059
|
entity |
| Predicate | borders |
P224
|
FINISHED |
| Object | Dønna |
—
|
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: Dønna | Statement: [Alstahaug, borders, Dønna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dønna Context triple: [Alstahaug, borders, Dønna]
-
A.
Dønna
chosen
Dønna is a scenic island municipality in Nordland county, Norway, known for its rugged coastline, fishing communities, and views of the Helgeland archipelago.
-
B.
Sunndal
Sunndal is a municipality in Møre og Romsdal county in western Norway, known for its dramatic fjord landscape and significant aluminum industry.
-
C.
Vaksdal
Vaksdal is a village in Vestland county, Norway, situated along the Veafjorden and known for its historic textile industry and railway connections.
-
D.
Ottosdal
Ottosdal is a small agricultural town in South Africa’s North West province, known for its grain farming and rural character.
-
E.
Nissedal
Nissedal is a rural municipality in Vestfold og Telemark county, Norway, known for its forests, lakes, and outdoor recreation opportunities.
- 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_69d889e1c6148190ba76241e74688f8b |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46d3174008190a2b5bb1b061ea4df |
completed | April 19, 2026, 5:50 a.m. |
Created at: April 10, 2026, 5:51 a.m.