Triple
T6020665
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bjerke district |
E134053
|
entity |
| Predicate | hasNeighbourhood |
P4813
|
FINISHED |
| Object |
Bjerke
Bjerke is a neighborhood in the Bjerke borough of Oslo, Norway, known primarily as a residential area with local services and amenities.
|
E581622
|
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: Bjerke | Statement: [Bjerke district, hasNeighbourhood, Bjerke]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bjerke Context triple: [Bjerke district, hasNeighbourhood, Bjerke]
-
A.
Bjugn
Bjugn is a former municipality and coastal community in Trøndelag county, Norway, known for its fishing, agriculture, and location on the Fosen peninsula.
-
B.
Kvikne
Kvikne is a rural village area in central Norway, known historically for mining and as the birthplace of Nobel Prize–winning writer Bjørnstjerne Bjørnson.
-
C.
Bremsnes
Bremsnes is a village on the island of Averøya in Møre og Romsdal county, Norway, known for its coastal setting and local church.
-
D.
Gjerdrum
Gjerdrum is a small rural municipality in Viken county, Norway, known for its agricultural landscape and proximity to the Oslo metropolitan area.
-
E.
Sæbø
Sæbø is a small Norwegian village known for its scenic location amid steep mountains and fjord landscapes in western Norway.
- 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: Bjerke Triple: [Bjerke district, hasNeighbourhood, Bjerke]
Generated description
Bjerke is a neighborhood in the Bjerke borough of Oslo, Norway, known primarily as a residential area with local services and amenities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bjerke Target entity description: Bjerke is a neighborhood in the Bjerke borough of Oslo, Norway, known primarily as a residential area with local services and amenities.
-
A.
Bjugn
Bjugn is a former municipality and coastal community in Trøndelag county, Norway, known for its fishing, agriculture, and location on the Fosen peninsula.
-
B.
Kvikne
Kvikne is a rural village area in central Norway, known historically for mining and as the birthplace of Nobel Prize–winning writer Bjørnstjerne Bjørnson.
-
C.
Bremsnes
Bremsnes is a village on the island of Averøya in Møre og Romsdal county, Norway, known for its coastal setting and local church.
-
D.
Gjerdrum
Gjerdrum is a small rural municipality in Viken county, Norway, known for its agricultural landscape and proximity to the Oslo metropolitan area.
-
E.
Sæbø
Sæbø is a small Norwegian village known for its scenic location amid steep mountains and fjord landscapes in western Norway.
- 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_69c008742a5c8190b9cb9c2787a3d8b3 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04fba86a48190984e95d5adf7c7f1 |
completed | March 22, 2026, 8:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c518c29dfc8190a55a54fbe6608dd2 |
completed | March 26, 2026, 11:30 a.m. |
| NEDg | Description generation | batch_69c51dac60ec8190a2f72913fdd301ca |
completed | March 26, 2026, 11:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c51e0b131c8190a2a9c29ace182c27 |
completed | March 26, 2026, 11:52 a.m. |
Created at: March 22, 2026, 4:07 p.m.