Triple
T5098122
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Møre og Romsdal |
E114915
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Vestland |
E75761
|
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: Vestland | Statement: [Møre og Romsdal, borderedBy, Vestland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vestland Context triple: [Møre og Romsdal, borderedBy, Vestland]
-
A.
Vestland
chosen
Vestland is a county in western Norway known for its dramatic fjords, coastal landscapes, and the city of Bergen.
-
B.
Fosen
Fosen is a peninsula and traditional district in central Norway known for its coastal landscape, wind farms, and location across the Trondheimsfjord from the city of Trondheim.
-
C.
Mykland
Mykland is a small village in Agder county, Norway, known for its rural setting and surrounding forests and lakes.
-
D.
Helgeland
Helgeland is a coastal region in northern Norway known for its dramatic fjords, islands, and mountain landscapes.
-
E.
Nordlandet
Nordlandet is one of the main islands and districts of the coastal Norwegian city of Kristiansund.
- 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_69bd443fc49c819089629c00e311310c |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7567d21081909227ed8f08b74c71 |
completed | March 20, 2026, 4:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beba8529ec8190bb1e97eb1044c899 |
completed | March 21, 2026, 3:34 p.m. |
Created at: March 20, 2026, 1:40 p.m.