Triple
T21298722
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ørskog |
E524996
|
entity |
| Predicate | hasCountyCapitalNearby |
P41518
|
FINISHED |
| Object | Ålesund |
—
|
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: Ålesund | Statement: [Ørskog, hasCountyCapitalNearby, Ålesund]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ålesund Context triple: [Ørskog, hasCountyCapitalNearby, Ålesund]
-
A.
Ålesund
chosen
Ålesund is a coastal Norwegian city renowned for its distinctive Art Nouveau architecture and location across several islands in Western Norway.
-
B.
Kristiansund
Kristiansund is a coastal city in western Norway known for its historic clipfish industry, distinctive layout across several islands, and picturesque harbor.
-
C.
Hanøy
Hanøy is a small Norwegian island that forms part of Askøy Municipality in Vestland county.
-
D.
Stavanger
Stavanger is a coastal city in southwestern Norway known for its oil industry hub status, historic wooden houses, and proximity to natural attractions like the Lysefjord and Preikestolen.
-
E.
Egersund
Egersund is a coastal town in southwestern Norway known for its fishing industry, historic wooden architecture, and scenic harbor.
- 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_69e0b517e6748190850d6f6ddf323d69 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7385a24d08190bfd410c7f10fa6f7 |
completed | April 21, 2026, 8:42 a.m. |
Created at: April 16, 2026, 4:05 p.m.