Triple
T6488090
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kolsås |
E146564
|
entity |
| Predicate | hasTerminus |
P388
|
FINISHED |
| Object |
Kolsås station
Kolsås station is a metro terminus on the Kolsås Line of the Oslo Metro system in Bærum, Norway.
|
E632559
|
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: Kolsås station | Statement: [Kolsås, hasTerminus, Kolsås station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kolsås station Context triple: [Kolsås, hasTerminus, Kolsås station]
-
A.
Nydalen station
Nydalen station is an Oslo Metro station serving the Nydalen area in the Nordre Aker borough of Oslo, Norway.
-
B.
Kjelsås station
Kjelsås station is a railway station in the Kjelsås neighborhood of Oslo, Norway, serving the Gjøvik Line.
-
C.
Drammen Station
Drammen Station is a major railway hub in Drammen, Norway, connecting regional and long-distance train services to Oslo and other parts of the country.
-
D.
Skøyen Station
Skøyen Station is a major railway and commuter hub in Oslo, Norway, serving regional and local trains as part of the city's western transport corridor.
-
E.
Lysaker Station
Lysaker Station is a major railway station in the Oslo metropolitan area of Norway, serving as an important commuter and regional transport hub.
- 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: Kolsås station Triple: [Kolsås, hasTerminus, Kolsås station]
Generated description
Kolsås station is a metro terminus on the Kolsås Line of the Oslo Metro system in Bærum, Norway.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kolsås station Target entity description: Kolsås station is a metro terminus on the Kolsås Line of the Oslo Metro system in Bærum, Norway.
-
A.
Nydalen station
Nydalen station is an Oslo Metro station serving the Nydalen area in the Nordre Aker borough of Oslo, Norway.
-
B.
Kjelsås station
Kjelsås station is a railway station in the Kjelsås neighborhood of Oslo, Norway, serving the Gjøvik Line.
-
C.
Drammen Station
Drammen Station is a major railway hub in Drammen, Norway, connecting regional and long-distance train services to Oslo and other parts of the country.
-
D.
Skøyen Station
Skøyen Station is a major railway and commuter hub in Oslo, Norway, serving regional and local trains as part of the city's western transport corridor.
-
E.
Lysaker Station
Lysaker Station is a major railway station in the Oslo metropolitan area of Norway, serving as an important commuter and regional transport hub.
- 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_69c0090158c08190af0df9a2348d2d52 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c06a96a4048190a28dee5fd9258486 |
completed | March 22, 2026, 10:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7616bf79481909b21f5e295a4d48a |
completed | March 28, 2026, 5:04 a.m. |
| NEDg | Description generation | batch_69c762b87eb08190bfe526fed264f342 |
completed | March 28, 2026, 5:10 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c7635606148190aadbdd1025521704 |
completed | March 28, 2026, 5:12 a.m. |
Created at: March 22, 2026, 4:52 p.m.