Triple
T15216750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sogn region |
E363655
|
entity |
| Predicate | hasSubregion |
P285
|
FINISHED |
| Object | Inner Sogn |
E1130035
|
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: Inner Sogn | Statement: [Sogn region, hasSubregion, Inner Sogn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Inner Sogn Context triple: [Sogn region, hasSubregion, Inner Sogn]
-
A.
Sogn
chosen
Sogn is a traditional district in western Norway known for its dramatic fjord landscapes, including parts of the famous Sognefjord.
-
B.
Sundet
Sundet is the main town and local hub of Eidsvoll municipality in Norway, serving as its commercial and service center.
-
C.
Sennevika
Sennevika is a small settlement located on the island of Senja in northern Norway, known for its coastal Arctic landscape.
-
D.
Sognsvann
Sognsvann is a popular recreational lake and surrounding forested area in northern Oslo, Norway, known for hiking, swimming, and outdoor activities.
-
E.
Snogebæk
Snogebæk is a small coastal village and fishing hamlet on the Danish island of Bornholm, known for its harbor, beaches, and holiday atmosphere.
- 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_69d85a0ce24c81909c4d3b6475548c95 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0076f90c481909989befe031a2cae |
completed | April 15, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fed343f51481908f04c35d37b39ad2 |
completed | May 9, 2026, 6:25 a.m. |
Created at: April 10, 2026, 3:11 a.m.