Triple
T18859330
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Drammensfjord |
E461266
|
entity |
| Predicate | hasCityOnShore |
P969
|
FINISHED |
| Object | Røyken |
—
|
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: Røyken | Statement: [Drammensfjord, hasCityOnShore, Røyken]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Røyken Context triple: [Drammensfjord, hasCityOnShore, Røyken]
-
A.
Røyken
chosen
Røyken is a former municipality and suburban area in southeastern Norway, located along the Oslofjord and historically part of Buskerud county.
-
B.
Røssvoll
Røssvoll is a small village in Nordland county, Norway, known for its local airport serving the Rana region.
-
C.
Gjøvik
Gjøvik is a town and municipality in Innlandet county, Norway, known for its location along Lake Mjøsa and its mix of industrial heritage and modern sports and cultural facilities.
-
D.
Røyrvik
Røyrvik is a small rural municipality in Trøndelag county, Norway, known for its mountainous landscapes, reindeer herding traditions, and proximity to Børgefjell National Park.
-
E.
Lørenskog
Lørenskog is a suburban municipality in Viken county, Norway, located just east of Oslo and known for its residential areas and commercial centers.
- 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_69d8dcfb7b9c8190854e7b171b98ea2e |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5c05fb800819098951ec134a1fa2a |
completed | April 20, 2026, 5:57 a.m. |
Created at: April 10, 2026, 11:57 a.m.