Triple
T17929176
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lyngenfjord |
E448281
|
entity |
| Predicate | hasNearbyMunicipality |
P4647
|
FINISHED |
| Object | Lyngen |
—
|
NE NERFINISHED |
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: Lyngen | Statement: [Lyngenfjord, hasNearbyMunicipality, Lyngen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lyngen Context triple: [Lyngenfjord, hasNearbyMunicipality, Lyngen]
-
A.
Lyngen
chosen
Lyngen is a municipality in Troms og Finnmark county in northern Norway, known for its dramatic fjord landscapes and surrounding alpine mountains.
-
B.
Trysil
Trysil is a Norwegian municipality renowned for its large alpine ski resort and extensive outdoor recreation opportunities.
-
C.
Ørskog
Ørskog is a village and former municipality in western Norway, located in the county of Møre og Romsdal.
-
D.
Fagernes
Fagernes is a small town in central Norway that serves as a regional hub and gateway to the mountainous Valdres district.
-
E.
Nissedal
Nissedal is a rural municipality in Vestfold og Telemark county, Norway, known for its forests, lakes, and outdoor recreation opportunities.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9f79d14819095540856928f0e25 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e4a5511a408190973cf5fa1f286a26 |
completed | April 19, 2026, 9:50 a.m. |
Created at: April 10, 2026, 10:20 a.m.