Triple
T2934348
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Averøya |
E79229
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Møre og Romsdal county |
E114915
|
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: Møre og Romsdal county | Statement: [Averøya, locatedIn, Møre og Romsdal county]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Møre og Romsdal county Context triple: [Averøya, locatedIn, Møre og Romsdal county]
-
A.
Møre og Romsdal
chosen
Møre og Romsdal is a coastal county in western Norway known for its dramatic fjords, islands, and mountainous landscapes.
-
B.
Oppland
Oppland is a former inland county in southeastern Norway known for its mountainous terrain, national parks, and popular skiing and hiking areas.
-
C.
Rogaland
Rogaland is a county in southwestern Norway known for its rugged coastline, fjords, and the oil industry centered around the city of Stavanger.
-
D.
Hedmark
Hedmark is a former county in eastern Norway known for its vast forests, agriculture, and inland landscapes along the Swedish border.
-
E.
Aust-Agder
Aust-Agder was a former county in southern Norway known for its coastal towns, forests, and role in the country’s maritime and timber industries.
- 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_69ad8b0fbab081908f6a61567c045d8d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad983b65f881909b8b7d3dc5c224fd |
completed | March 8, 2026, 3:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b0867ba1b48190a54d00c32b075548 |
completed | March 10, 2026, 9 p.m. |
Created at: March 8, 2026, 2:56 p.m.