Triple
T21312151
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vorma |
E525365
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Viken county |
—
|
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: Viken county | Statement: [Vorma, region, Viken county]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Viken county Context triple: [Vorma, region, Viken county]
-
A.
Viken county
chosen
Viken county is an administrative region in southeastern Norway that includes several municipalities and borders Sweden and the Oslofjord.
-
B.
Skåne County
Skåne County is Sweden’s southernmost county, known for its fertile farmland, coastal landscapes, and major cities such as Malmö and Lund.
-
C.
Östergötland County
Östergötland County is an administrative region in southeastern Sweden known for its mix of historic cities, fertile plains, and coastal and archipelago landscapes along the Baltic Sea.
-
D.
Västmanland County
Västmanland County is an administrative region in central Sweden known for its mix of industrial towns, forests, and lakes.
-
E.
Halland County
Halland County is a coastal county in southwestern Sweden known for its beaches along the Kattegat, agriculture, and proximity to the city of Gothenburg.
- 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_69e0b518b8948190ad69cf9a8784d397 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e75dca66ac8190a5f0a372a9bc25a7 |
completed | April 21, 2026, 11:21 a.m. |
Created at: April 16, 2026, 4:22 p.m.