Triple
T15751462
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tennevoll |
E381854
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Lavangen
Lavangen is a small coastal municipality in Troms og Finnmark county in northern Norway, known for its fjord landscape and rural communities.
|
E1272795
|
NE FINISHED |
How this triple was built (4 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: Lavangen | Statement: [Tennevoll, hasMunicipality, Lavangen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lavangen Context triple: [Tennevoll, hasMunicipality, Lavangen]
-
A.
Vangsnes
Vangsnes is a small village in Vestland county, Norway, situated along the Sognefjorden and known for its scenic fjord landscape and agricultural surroundings.
-
B.
Gravdal
Gravdal is a small coastal village on the island of Vestvågøy in Norway’s Lofoten archipelago.
-
C.
Kalvåg
Kalvåg is a coastal fishing village in Bremanger Municipality in Vestland county, Norway, known for its well-preserved wooden waterfront buildings and maritime heritage.
-
D.
Vennesla
Vennesla is a municipality in Agder county in southern Norway, known for its industrial heritage and scenic river valley setting.
-
E.
Sørenga
Sørenga is a modern waterfront neighborhood in Oslo, Norway, known for its residential developments, seaside promenade, and popular public seawater pool and beach.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Lavangen Triple: [Tennevoll, hasMunicipality, Lavangen]
Generated description
Lavangen is a small coastal municipality in Troms og Finnmark county in northern Norway, known for its fjord landscape and rural communities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lavangen Target entity description: Lavangen is a small coastal municipality in Troms og Finnmark county in northern Norway, known for its fjord landscape and rural communities.
-
A.
Vangsnes
Vangsnes is a small village in Vestland county, Norway, situated along the Sognefjorden and known for its scenic fjord landscape and agricultural surroundings.
-
B.
Gravdal
Gravdal is a small coastal village on the island of Vestvågøy in Norway’s Lofoten archipelago.
-
C.
Kalvåg
Kalvåg is a coastal fishing village in Bremanger Municipality in Vestland county, Norway, known for its well-preserved wooden waterfront buildings and maritime heritage.
-
D.
Vennesla
Vennesla is a municipality in Agder county in southern Norway, known for its industrial heritage and scenic river valley setting.
-
E.
Sørenga
Sørenga is a modern waterfront neighborhood in Oslo, Norway, known for its residential developments, seaside promenade, and popular public seawater pool and beach.
- F. None of above. chosen
Provenance (5 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_69d86d9e6b44819085d1f6a969ecb74c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e05030e31081908c307a8dc7067db4 |
completed | April 16, 2026, 2:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01c1e360048190a94b9ce8724cb4b7 |
completed | May 11, 2026, 11:47 a.m. |
| NEDg | Description generation | batch_6a01c60989d08190839909d7018f48f4 |
completed | May 11, 2026, 12:05 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01c701504c8190ad1979b65cb73336 |
completed | May 11, 2026, 12:09 p.m. |
Created at: April 10, 2026, 4:47 a.m.