Triple
T3768474
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arctic Race of Norway |
E82737
|
entity |
| Predicate | hasHostRegion |
P285
|
FINISHED |
| Object | Nordland |
E80792
|
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: Nordland | Statement: [Arctic Race of Norway, hasHostRegion, Nordland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nordland Context triple: [Arctic Race of Norway, hasHostRegion, Nordland]
-
A.
Nordland
chosen
Nordland is a long coastal county in northern Norway known for its dramatic fjords, islands like the Lofoten archipelago, and Arctic landscapes.
-
B.
Nordlandet
Nordlandet is one of the main islands and districts of the coastal Norwegian city of Kristiansund.
-
C.
Finnmark
Finnmark is a sparsely populated, historically Norwegian region in the far northeast of Scandinavia, known for its Arctic climate, Sami culture, and dramatic coastal and tundra landscapes.
-
D.
Helgeland
Helgeland is a coastal region in northern Norway known for its dramatic fjords, islands, and mountain landscapes.
-
E.
Troms og Finnmark
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.
- 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_69ad8b207b0081909d2b48843fbd8795 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcc2d4b848190bf63fb3ed5d3b2d9 |
completed | March 8, 2026, 7:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5c6ed5910819095de0dce09bd50b8 |
completed | March 14, 2026, 8:37 p.m. |
Created at: March 8, 2026, 3:35 p.m.