Triple
T3579223
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lysefjord |
E75760
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Rogaland county |
E139000
|
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: Rogaland county | Statement: [Lysefjord, locatedIn, Rogaland county]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rogaland county Context triple: [Lysefjord, locatedIn, Rogaland county]
-
A.
Rogaland
chosen
Rogaland is a county in southwestern Norway known for its rugged coastline, fjords, and the oil industry centered around the city of Stavanger.
-
B.
Hordaland
Hordaland was a former county in western Norway known for its fjords, coastal landscapes, and the city of Bergen.
-
C.
Sogn og Fjordane
Sogn og Fjordane was a former county in western Norway known for its dramatic fjords, mountains, and coastal landscapes.
-
D.
Oslo county
Oslo county is Norway’s capital county, encompassing the city of Oslo and serving as the country’s political, economic, and cultural center.
-
E.
Hedmark
Hedmark is a former county in eastern Norway known for its vast forests, agriculture, and inland landscapes along the Swedish border.
- 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_69ad85d5e3008190bdfe0bacdd1f5a1b |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc0defe14819095a337a840e33300 |
completed | March 8, 2026, 6:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b556076b34819097b15b60a91b5164 |
completed | March 14, 2026, 12:35 p.m. |
Created at: March 8, 2026, 3:21 p.m.