Triple
T20707681
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Raumabanen |
E508946
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Lesja Station |
—
|
NE NERFINISHED |
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: Lesja Station | Statement: [Raumabanen, hasStation, Lesja Station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lesja Station Context triple: [Raumabanen, hasStation, Lesja Station]
-
A.
Lesja Station
chosen
Lesja Station is a rural railway stop in Norway serving the village of Lesja and connecting the area to the wider national rail network.
-
B.
Vejby Station
Vejby Station is a local railway station in the town of Vejby in North Zealand, Denmark, serving passengers on the regional rail network.
-
C.
Bolna Station
Bolna Station is a remote railway stop on Norway’s Nordland Line, serving the mountainous Saltfjellet region just north of the Arctic Circle.
-
D.
Poroy station
Poroy station is a railway station near Cusco, Peru, serving as a key departure point for trains traveling to Machu Picchu and the Sacred Valley.
-
E.
Liziba Station
Liziba Station is a famous Chongqing Metro station known for its striking design where trains appear to pass directly through a residential building.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4c40ad88190b81f77695366d328 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c1952e888190877b79933970f7b0 |
completed | April 21, 2026, 12:15 a.m. |
Created at: April 16, 2026, 12:14 p.m.