Triple
T4458883
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Inland Norway University of Applied Sciences |
E98200
|
entity |
| Predicate | hasCampusIn |
P4623
|
FINISHED |
| Object | Elverum |
E121198
|
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: Elverum | Statement: [Inland Norway University of Applied Sciences, hasCampusIn, Elverum]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elverum Context triple: [Inland Norway University of Applied Sciences, hasCampusIn, Elverum]
-
A.
Elverum
chosen
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.
-
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.
Hønefoss
Hønefoss is a Norwegian town known as a regional commercial and transport hub, situated along the Begna River northwest of Oslo.
-
D.
Larvik
Larvik is a coastal town and municipality in Vestfold, Norway, known for its harbor, beaches, and historic connections to the shipping and timber industries.
-
E.
Raufoss
Raufoss is an industrial town in Norway known for its manufacturing sector, particularly in defense and automotive components.
- 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_69b3454a7c608190944f5455c8031d73 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3567184f481908a2787e4ac9bb345 |
completed | March 13, 2026, 12:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf2196e8e88190b8da71ecfb07dfc8 |
completed | March 21, 2026, 10:54 p.m. |
Created at: March 12, 2026, 11:33 p.m.