Triple
T6020668
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bjerke district |
E134053
|
entity |
| Predicate | hasNeighbourhood |
P4813
|
FINISHED |
| Object |
Kalbakken
Kalbakken is a residential neighborhood in Oslo, Norway, known for its apartment blocks, green areas, and access to public transportation.
|
E560830
|
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: Kalbakken | Statement: [Bjerke district, hasNeighbourhood, Kalbakken]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kalbakken Context triple: [Bjerke district, hasNeighbourhood, Kalbakken]
-
A.
Stabekk
Stabekk is a suburban area in Bærum, Norway, known for its residential neighborhoods, proximity to Oslo, and good transport connections.
-
B.
Birkenes
Birkenes is a rural municipality in Agder county in southern Norway, known for its forests, rivers, and small villages.
-
C.
Kongsseteren
Kongsseteren is a historic winter residence and retreat used by the Norwegian royal family near Oslo.
-
D.
Storslett
Storslett is a small village and administrative center in Nordreisa Municipality in Troms og Finnmark county in northern Norway.
-
E.
Skillebekk
Skillebekk is a residential neighborhood in Oslo, Norway, known for its central location, historic architecture, and proximity to the Frogner and city center areas.
- 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: Kalbakken Triple: [Bjerke district, hasNeighbourhood, Kalbakken]
Generated description
Kalbakken is a residential neighborhood in Oslo, Norway, known for its apartment blocks, green areas, and access to public transportation.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kalbakken Target entity description: Kalbakken is a residential neighborhood in Oslo, Norway, known for its apartment blocks, green areas, and access to public transportation.
-
A.
Stabekk
Stabekk is a suburban area in Bærum, Norway, known for its residential neighborhoods, proximity to Oslo, and good transport connections.
-
B.
Birkenes
Birkenes is a rural municipality in Agder county in southern Norway, known for its forests, rivers, and small villages.
-
C.
Kongsseteren
Kongsseteren is a historic winter residence and retreat used by the Norwegian royal family near Oslo.
-
D.
Storslett
Storslett is a small village and administrative center in Nordreisa Municipality in Troms og Finnmark county in northern Norway.
-
E.
Skillebekk
Skillebekk is a residential neighborhood in Oslo, Norway, known for its central location, historic architecture, and proximity to the Frogner and city center areas.
- 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_69c108c2e1c88190a1629c6438f6c8bd |
completed | March 23, 2026, 9:32 a.m. |
| NEDg | Description generation | batch_69c1096d5f2881909126730848ca0e78 |
completed | March 23, 2026, 9:35 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c10b2699088190a468989beded758e |
completed | March 23, 2026, 9:43 a.m. |
Created at: March 22, 2026, 4:07 p.m.