Triple
T22641289
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beitstadfjorden |
E558831
|
entity |
| Predicate | hasNearbySettlement |
P4647
|
FINISHED |
| Object | Kjerknesvågen |
E558377
|
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: Kjerknesvågen | Statement: [Beitstadfjorden, hasNearbySettlement, Kjerknesvågen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kjerknesvågen Context triple: [Beitstadfjorden, hasNearbySettlement, Kjerknesvågen]
-
A.
Kjerknesvågen
chosen
Kjerknesvågen is a small coastal village in the municipality of Inderøy in Trøndelag county, Norway, known for its scenic fjordside setting and rural character.
-
B.
Vikevåg
Vikevåg is a village in Rogaland county, Norway, known for its coastal setting and role in local administration and services.
-
C.
Elnesvågen
Elnesvågen is a small coastal village in western Norway, situated in the county of Møre og Romsdal.
-
D.
Kvæfjorden
Kvæfjorden is a fjord in Troms county in northern Norway, known for its scenic coastal landscape and surrounding mountainous terrain.
-
E.
Kirkefjorden
Kirkefjorden is a scenic fjord on the island of Moskenesøya in Norway’s Lofoten archipelago, known for its steep mountainsides and dramatic coastal landscape.
- 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_69e24547f7fc819086e2c4ba3b979657 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f170116fe881908178cffef26e3ae7 |
completed | April 29, 2026, 2:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c676dfab48190b8ceb00dbfa455b2 |
completed | May 19, 2026, 1:36 p.m. |
Created at: April 17, 2026, 3:04 p.m.