Triple
T15751288
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Andselv |
E381850
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Troms region |
—
|
NE ONNED1 |
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: Troms region | Statement: [Andselv, locatedIn, Troms region]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Troms region Context triple: [Andselv, locatedIn, Troms region]
-
A.
Bodø region
The Bodø region is a coastal area in Northern Norway centered around the city of Bodø, known for its Arctic landscape, maritime industries, and role as a regional hub for education, transport, and commerce.
-
B.
Troms og Finnmark
chosen
Troms og Finnmark is Norway’s northernmost and largest county, known for its Arctic landscapes, Sami culture, and phenomena like the midnight sun and northern lights.
-
C.
Nordlandet
Nordlandet is one of the main islands and districts of the coastal Norwegian city of Kristiansund.
-
D.
Nordland
Nordland is a long coastal county in northern Norway known for its dramatic fjords, islands like the Lofoten archipelago, and Arctic landscapes.
-
E.
Trøndelag
Trøndelag is a central region of Norway known for its historic city of Trondheim, coastal landscapes, and strong cultural traditions.
- 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_69d86d9e6b44819085d1f6a969ecb74c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e05030e31081908c307a8dc7067db4 |
completed | April 16, 2026, 2:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01953eaa6c819091f7d63a1e3e7070 |
in_progress | May 11, 2026, 8:37 a.m. |
Created at: April 10, 2026, 4:47 a.m.