Triple
T16811713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Norwegian National Road 4 |
E408628
|
entity |
| Predicate | passesThroughTown |
P10456
|
FINISHED |
| Object | Nittedal |
—
|
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: Nittedal | Statement: [Norwegian National Road 4, passesThroughTown, Nittedal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nittedal Context triple: [Norwegian National Road 4, passesThroughTown, Nittedal]
-
A.
Nittedal
chosen
Nittedal is a municipality in Viken county, Norway, known for its forested landscapes and role as a commuter area north of Oslo.
-
B.
Tvedestrand
Tvedestrand is a coastal town and municipality in southern Norway known for its wooden houses, maritime heritage, and picturesque archipelago.
-
C.
Lørenskog
Lørenskog is a suburban municipality in Viken county, Norway, located just east of Oslo and known for its residential areas and commercial centers.
-
D.
Slemdal
Slemdal is a residential neighborhood in the Vestre Aker borough of Oslo, Norway, known for its green surroundings and affluent character.
-
E.
Sogndal
Sogndal is a village and municipality in Vestland county, Norway, known for its scenic fjord landscape, agriculture, and as a regional education and service center.
- 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_69d88393905081908d00a86b99996ac8 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3b2d0793c81909d938ac174a6e63a |
completed | April 18, 2026, 4:35 p.m. |
Created at: April 10, 2026, 5:23 a.m.