Triple
T5098096
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Møre og Romsdal |
E114915
|
entity |
| Predicate | containsSettlement |
P847
|
FINISHED |
| Object | Giske |
E370242
|
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: Giske | Statement: [Møre og Romsdal, containsSettlement, Giske]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Giske Context triple: [Møre og Romsdal, containsSettlement, Giske]
-
A.
Giske
chosen
Giske is a coastal municipality in Møre og Romsdal county, Norway, known for its islands, fishing communities, and proximity to the town of Ålesund.
-
B.
Bolnes
Bolnes is a Dutch surname most notably associated with Catharina Bolnes, the wife of painter Johannes Vermeer.
-
C.
Porsanger
Porsanger is a large municipality in Troms og Finnmark county in northern Norway, known for its vast Arctic landscapes, Sámi culture, and the long Porsangerfjorden.
-
D.
Blæstad
Blæstad is a campus location of Inland Norway University of Applied Sciences, known for its focus on agricultural and environmental studies.
-
E.
Dæhlie
Dæhlie is a Norwegian surname most famously associated with legendary cross-country skier Bjørn Dæhlie.
- 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_69bd443fc49c819089629c00e311310c |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7567d21081909227ed8f08b74c71 |
completed | March 20, 2026, 4:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69befe4e829c819092acbc078e552d75 |
completed | March 21, 2026, 8:23 p.m. |
Created at: March 20, 2026, 1:40 p.m.