Triple
T21572272
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | InterCity 225 |
E532312
|
entity |
| Predicate | controlVehicle |
P144304
|
FINISHED |
| Object | Driving Van Trailer |
—
|
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: Driving Van Trailer | Statement: [InterCity 225, controlVehicle, Driving Van Trailer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: controlVehicle Context triple: [InterCity 225, controlVehicle, Driving Van Trailer]
-
A.
mainVehicle
Indicates that one vehicle is the primary or most important vehicle associated with a given entity or context.
-
B.
applicationVehicle
Indicates a relationship where a particular vehicle is used, specified, or associated within the context of an application or application process.
-
C.
vehicleFor
Indicates that one entity serves as the means of transportation or conveyance for another entity.
-
D.
hostVehicle
Indicates that one entity serves as the primary or reference vehicle in relation to another entity or context.
-
E.
vehicleType
Indicates the specific kind or category of vehicle associated with an entity (e.g., car, bus, bicycle).
- F. None of above. chosen
Provenance (4 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_69e0c460db088190828c64206a450273 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69eee9cd50188190b44eb25fb87312bb |
completed | April 27, 2026, 4:45 a.m. |
| PD | Predicate disambiguation | batch_69e6320c8c2c81908bf031447d66a052 |
completed | April 20, 2026, 2:02 p.m. |
| PDg | Predicate description generation | batch_69e633bf34c481909925d8dc1a633a65 |
completed | April 20, 2026, 2:10 p.m. |
Created at: April 16, 2026, 6:30 p.m.