Triple
T22452894
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gare d’Orange |
E555036
|
entity |
| Predicate | hasRailwayConnections |
P71547
|
FINISHED |
| Object | regional destinations in Provence-Alpes-Côte d’Azur |
—
|
LITERAL 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: regional destinations in Provence-Alpes-Côte d’Azur | Statement: [Gare d’Orange, hasRailwayConnections, regional destinations in Provence-Alpes-Côte d’Azur]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRailwayConnections Context triple: [Gare d’Orange, hasRailwayConnections, regional destinations in Provence-Alpes-Côte d’Azur]
-
A.
hasPassengerRailConnection
chosen
Indicates that there exists a passenger rail service linking one location or transport node to another.
-
B.
railwayConnectsTo
Indicates that one railway line, track, or network is directly linked or joined to another, allowing trains to move between them.
-
C.
countryRailConnection
Indicates that there is a railway connection or service linking two countries.
-
D.
connectsToRailStation
Indicates that one entity has a direct link, route, or access connection to a rail station.
-
E.
hasRailStation
Indicates that one entity possesses, contains, or is served by a rail station.
- 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_69e11e5113208190ab58c6b595f9d1d0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15b4d6368819082cafbbd83339fbf |
completed | April 29, 2026, 1:13 a.m. |
| PD | Predicate disambiguation | batch_69e898ad961c819098fd1e46129bddcc |
completed | April 22, 2026, 9:45 a.m. |
Created at: April 16, 2026, 8:48 p.m.