Triple
T8341973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tjeldøya |
E195938
|
entity |
| Predicate | administrativeDivision |
P747
|
FINISHED |
| Object | Nordland county |
E401032
|
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 county | Statement: [Tjeldøya, administrativeDivision, Nordland county]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nordland county Context triple: [Tjeldøya, administrativeDivision, Nordland county]
-
A.
Nordland county
chosen
Nordland county is a long, coastal region in northern Norway known for its dramatic fjords, islands, and Arctic landscapes.
-
B.
Møre og Romsdal
Møre og Romsdal is a coastal county in western Norway known for its dramatic fjords, islands, and mountainous landscapes.
-
C.
Oppland
Oppland is a former inland county in southeastern Norway known for its mountainous terrain, national parks, and popular skiing and hiking areas.
-
D.
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.
-
E.
Sogn og Fjordane
Sogn og Fjordane was a former county in western Norway known for its dramatic fjords, mountains, and coastal landscapes.
- 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_69ca82ecbdc481908a55cad8ca062d88 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7fe9efec81908e0c9ded3963bac5 |
completed | March 31, 2026, 8:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cebb37161081908452cc449b4e903e |
completed | April 2, 2026, 6:53 p.m. |
Created at: March 30, 2026, 5:58 p.m.