Triple
T15276368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sogn og Fjordane |
E365150
|
entity |
| Predicate | containsPart |
P35
|
FINISHED |
| Object | Gloppen |
E385011
|
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: Gloppen | Statement: [Sogn og Fjordane, containsPart, Gloppen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gloppen Context triple: [Sogn og Fjordane, containsPart, Gloppen]
-
A.
Gloppen
chosen
Gloppen is a municipality in Vestland county, Norway, known for its fjord landscapes, agriculture, and the village of Sandane as its administrative center.
-
B.
Gisundet
Gisundet is a narrow strait in northern Norway that separates the island of Senja from the mainland and connects the Malangen fjord to the Gisundet sound.
-
C.
Bekkestua
Bekkestua is a suburban center in Bærum, Norway, functioning as a local commercial and transport hub just west of Oslo.
-
D.
Bjorli
Bjorli is a Norwegian village known for its ski resort and scenic mountain surroundings in Innlandet county.
-
E.
Gjerdrum
Gjerdrum is a small rural municipality in Viken county, Norway, known for its agricultural landscape and proximity to the Oslo metropolitan area.
- 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_69d85a103d9081908c1ea6c4c73ac8e3 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e00953bc848190b83919f39d5ee37b |
completed | April 15, 2026, 9:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff8757325c8190ad97f50368862ca5 |
completed | May 9, 2026, 7:13 p.m. |
Created at: April 10, 2026, 3:14 a.m.