Triple
T13614349
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Setesdal |
E325272
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Bygland |
E325274
|
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: Bygland | Statement: [Setesdal, hasPart, Bygland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bygland Context triple: [Setesdal, hasPart, Bygland]
-
A.
Bygland
chosen
Bygland is a rural municipality in southern Norway known for its location along the Setesdal valley and the Byglandsfjorden lake.
-
B.
Mykland
Mykland is a small village in Agder county, Norway, known for its rural setting and surrounding forests and lakes.
-
C.
Helleland
Helleland is a small village in Rogaland county, Norway, situated within the municipality of Eigersund.
-
D.
Forlandet
Forlandet is a long, narrow island off the west coast of Spitsbergen in the Svalbard archipelago, known for its protected wilderness and rich Arctic wildlife.
-
E.
Sikkeland
Sikkeland is a Norwegian surname most notably associated with the physicist Torbjørn Sikkeland.
- 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_69d8076aae28819092cf636190ee5529 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbb0abe1208190a1e0a32dc141d836 |
completed | April 12, 2026, 2:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f77f9cbc388190972e949324144d2f |
completed | May 3, 2026, 5:02 p.m. |
Created at: April 9, 2026, 9:50 p.m.