Triple
T3626179
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nærøyfjord |
E76844
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Sognefjord |
E74083
|
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: Sognefjord | Statement: [Nærøyfjord, partOf, Sognefjord]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sognefjord Context triple: [Nærøyfjord, partOf, Sognefjord]
-
A.
Sognefjord
chosen
Sognefjord is Norway’s longest and deepest fjord, renowned for its dramatic cliffs, glacial landscapes, and scenic coastal villages.
-
B.
Oslofjord
Oslofjord is a large inlet in southeastern Norway known for its islands, coastal towns, and role as the maritime gateway to Oslo.
-
C.
Trondheimsfjord
Trondheimsfjord is a major Norwegian fjord on the central coast, known for its deep waters, rich marine life, and the city of Trondheim along its shores.
-
D.
Osafjorden
Osafjorden is a side fjord of Norway’s Hardangerfjord, known for its steep mountain scenery and tranquil, narrow waters.
-
E.
Hardangerfjord
Hardangerfjord is one of Norway’s longest and most scenic fjords, renowned for its dramatic mountains, waterfalls, and fruit orchards.
- 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_69ad85dc03948190b35b7189e4175bcc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adc2dc011c8190a6596f4b483fb078 |
completed | March 8, 2026, 6:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5a826850c8190b7c6853e12d09606 |
completed | March 14, 2026, 6:25 p.m. |
Created at: March 8, 2026, 3:23 p.m.