Triple
T37146653
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Font-Romeu-Odeillo-Via |
E920257
|
entity |
| Predicate | hasNearestRailStation |
P4625
|
FINISHED |
| Object | Latour-de-Carol-Enveitg 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: Latour-de-Carol-Enveitg station | Statement: [Font-Romeu-Odeillo-Via, hasNearestRailStation, Latour-de-Carol-Enveitg station]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearestRailStation Context triple: [Font-Romeu-Odeillo-Via, hasNearestRailStation, Latour-de-Carol-Enveitg station]
-
A.
hasNearbyRailwayStation
Indicates that a railway station is located within a short or convenient distance from the referenced entity.
-
B.
nearestPassengerRailStation
chosen
Indicates that one entity is the closest passenger rail station in distance to another entity.
-
C.
hasNearbyRailway
Indicates that one entity is located close to a railway associated with or relevant to another entity.
-
D.
nearestSuburbanRailwayStation
Indicates the relationship where a given place is associated with the suburban railway station that is geographically closest to it.
-
E.
nearestRailwayTerminus
Indicates that one location is the closest railway terminus to another specified place.
- F. None of above.
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_69f76e9f87c08190b4c8f7fafbd8345a |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a008b8938608190adaccbd720111a18 |
completed | May 10, 2026, 1:43 p.m. |
| PD | Predicate disambiguation | batch_6a008b24baac8190baaaf50c2e9cc6bd |
completed | May 10, 2026, 1:41 p.m. |
Created at: May 3, 2026, 4:15 p.m.