Triple
T30514732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SNCF Class B 84500 |
E776530
|
entity |
| Predicate | railwayVehicleRole |
P1305
|
FINISHED |
| Object | regional multiple unit |
—
|
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 multiple unit | Statement: [SNCF Class B 84500, railwayVehicleRole, regional multiple unit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: railwayVehicleRole Context triple: [SNCF Class B 84500, railwayVehicleRole, regional multiple unit]
-
A.
railwayTrafficRole
Indicates the specific function or responsibility an entity has within the operation, management, or control of railway traffic.
-
B.
railwayCarriageUsedFor
Indicates that a railway carriage is employed or designated for a particular purpose, function, or type of use.
-
C.
rollingStockType
chosen
Indicates the specific category or type of railway rolling stock associated with an entity (e.g., locomotive, passenger car, freight wagon).
-
D.
ownedRollingStock
Indicates that one entity possesses or has ownership rights over specific rolling stock (such as trains, railcars, or locomotives).
-
E.
passengerRollingStock
Indicates that the rolling stock is designed or used for carrying passengers rather than freight or other purposes.
- 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_69f2249b23c4819087fa85496d92f43f |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f727afd5d88190ad48735cd1b32787 |
completed | May 3, 2026, 10:47 a.m. |
| PD | Predicate disambiguation | batch_69f72737c42c8190a3f781a5e98868ff |
completed | May 3, 2026, 10:45 a.m. |
Created at: April 29, 2026, 8:16 p.m.