Triple
T15216475
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bybanen |
E363648
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Nesttun station
Nesttun station is a light rail stop on Bergen's Bybanen system in Norway, serving the Nesttun neighborhood as a local transit hub.
|
E1143507
|
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: Nesttun station | Statement: [Bybanen, hasStation, Nesttun station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nesttun station Context triple: [Bybanen, hasStation, Nesttun station]
-
A.
Gnesta Station
Gnesta Station is a key railway station in the town of Gnesta, Sweden, serving as an important terminus and hub on the Stockholm commuter rail network.
-
B.
Vestli station
Vestli station is an Oslo Metro rapid transit station located in the Vestli neighborhood in the Stovner borough of Oslo, Norway.
-
C.
Linderud station
Linderud station is an Oslo Metro stop in the Linderud neighborhood, providing urban rail access on the city's east side.
-
D.
Vallentuna station
Vallentuna station is a commuter rail stop on Stockholm’s Roslagsbanan narrow-gauge railway serving the locality of Vallentuna in Sweden.
-
E.
Hengst station
Hengst station is a mountain railway terminus serving as the upper endpoint of the Schneeberg line in Lower Austria.
- 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: Nesttun station Triple: [Bybanen, hasStation, Nesttun station]
Generated description
Nesttun station is a light rail stop on Bergen's Bybanen system in Norway, serving the Nesttun neighborhood as a local transit hub.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nesttun station Target entity description: Nesttun station is a light rail stop on Bergen's Bybanen system in Norway, serving the Nesttun neighborhood as a local transit hub.
-
A.
Gnesta Station
Gnesta Station is a key railway station in the town of Gnesta, Sweden, serving as an important terminus and hub on the Stockholm commuter rail network.
-
B.
Vestli station
Vestli station is an Oslo Metro rapid transit station located in the Vestli neighborhood in the Stovner borough of Oslo, Norway.
-
C.
Linderud station
Linderud station is an Oslo Metro stop in the Linderud neighborhood, providing urban rail access on the city's east side.
-
D.
Vallentuna station
Vallentuna station is a commuter rail stop on Stockholm’s Roslagsbanan narrow-gauge railway serving the locality of Vallentuna in Sweden.
-
E.
Hengst station
Hengst station is a mountain railway terminus serving as the upper endpoint of the Schneeberg line in Lower Austria.
- 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_69d85a0ce24c81909c4d3b6475548c95 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0076f90c481909989befe031a2cae |
completed | April 15, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fed343f51481908f04c35d37b39ad2 |
completed | May 9, 2026, 6:25 a.m. |
| NEDg | Description generation | batch_69fed44b2e3c8190aad111e2bc2b56a2 |
completed | May 9, 2026, 6:29 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fed547192c8190b89755fff48ca620 |
completed | May 9, 2026, 6:33 a.m. |
Created at: April 10, 2026, 3:11 a.m.