Triple
T21273526
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bergensbanen |
E524327
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object | Gol 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: Gol station | Statement: [Bergensbanen, hasStation, Gol station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gol station Context triple: [Bergensbanen, hasStation, Gol station]
-
A.
Gol Station
chosen
Gol Station is a railway station in the village and municipality of Gol in Viken county, Norway, serving as a stop along the Bergen Line between Oslo and Bergen.
-
B.
Samgori station
Samgori station is a metro station on the Tbilisi Metro system in Tbilisi, Georgia.
-
C.
Hankar station
Hankar station is a Brussels Metro station on the city's Line 5, serving the Auderghem municipality in southeastern Brussels.
-
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.
Estagel station
Estagel station is a small regional railway stop serving the commune of Estagel in southern France.
- 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_69e0b516293c819089458ea2ec85f85e |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e73655717c819092f71ed1920f52b5 |
completed | April 21, 2026, 8:33 a.m. |
Created at: April 16, 2026, 4:01 p.m.