Triple
T2206999
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sarpsborg |
E50822
|
entity |
| Predicate | isPartOf |
P10
|
FINISHED |
| Object | Viken fylke |
E50816
|
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: Viken fylke | Statement: [Sarpsborg, isPartOf, Viken fylke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Viken fylke Context triple: [Sarpsborg, isPartOf, Viken fylke]
-
A.
Agder
Agder is a county in southern Norway known for its long coastline, maritime heritage, and popular coastal towns and islands.
-
B.
Viken county
chosen
Viken county is an administrative region in southeastern Norway that includes several municipalities and borders Sweden and the Oslofjord.
-
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.
Trøndelag
Trøndelag is a central region of Norway known for its historic city of Trondheim, coastal landscapes, and strong cultural traditions.
- 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_69a88b06709c8190978fb2418470d1b6 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abbfcbb83081908d5b2f1603c7b4d2 |
completed | March 7, 2026, 6:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aef085c9f48190a9adfff98c30f4f5 |
completed | March 9, 2026, 4:08 p.m. |
Created at: March 4, 2026, 7:46 p.m.