Triple
T10427927
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | municipality of Hønefoss |
E245833
|
entity |
| Predicate | hasName |
P744
|
FINISHED |
| Object | Hønefoss |
E444788
|
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: Hønefoss | Statement: [municipality of Hønefoss, hasName, Hønefoss]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hønefoss Context triple: [municipality of Hønefoss, hasName, Hønefoss]
-
A.
Hønefoss
chosen
Hønefoss is a Norwegian town known as a regional commercial and transport hub, situated along the Begna River northwest of Oslo.
-
B.
Sarpsborg
Sarpsborg is a historic city and municipality in Viken county, Norway, known as one of the country’s oldest towns and an important industrial and administrative center in the Østfold region.
-
C.
Torshov
Torshov is a residential neighborhood in Oslo, Norway, known for its early 20th-century architecture, green spaces, and vibrant local culture.
-
D.
Elverum
Elverum is a town and municipality in Innlandet county in eastern Norway, known for its forestry, military camp, and role in Norwegian World War II history.
-
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 (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_69d381bf3dc08190bf35a2643e4e8f22 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4ea4a7dcc81909a830e08656a1c0c |
completed | April 7, 2026, 11:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d95e5197cc8190ad70c665ec2f8fa8 |
completed | April 10, 2026, 8:32 p.m. |
Created at: April 6, 2026, 12:13 p.m.