Triple
T5290859
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flytoget |
E119736
|
entity |
| Predicate | stationServed |
P6301
|
FINISHED |
| Object | Stabekk |
E227163
|
NE FINISHED |
How this triple was built (2 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: Stabekk | Statement: [Flytoget, stationServed, Stabekk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Stabekk Context triple: [Flytoget, stationServed, Stabekk]
-
A.
Stabekk
chosen
Stabekk is a suburban area in Bærum, Norway, known for its residential neighborhoods, proximity to Oslo, and good transport connections.
-
B.
Bekkestua
Bekkestua is a suburban center in Bærum, Norway, functioning as a local commercial and transport hub just west of Oslo.
-
C.
Storslett
Storslett is a small village and administrative center in Nordreisa Municipality in Troms og Finnmark county in northern Norway.
-
D.
Storlien
Storlien is a village and ski resort in central Sweden near the Norwegian border, known for its winter sports and cross-border rail connections.
-
E.
Blakstad
Blakstad is a village in Agder county, Norway, known as the main local hub for services and administration in the surrounding Froland area.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69bd446de5648190b313a90bd96730d2 |
completed | March 20, 2026, 12:58 p.m. |
| NER | Named-entity recognition | batch_69bd84eac7b88190900142bd1310c0fd |
completed | March 20, 2026, 5:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf1880bec88190a5b1ca453c783444 |
completed | March 21, 2026, 10:15 p.m. |
Created at: March 20, 2026, 1:52 p.m.