Triple
T24042673
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 446 series EMU |
E595426
|
entity |
| Predicate | hasTractionUnitType |
P132066
|
FINISHED |
| Object | motor coach |
—
|
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: motor coach | Statement: [446 series EMU, hasTractionUnitType, motor coach]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTractionUnitType Context triple: [446 series EMU, hasTractionUnitType, motor coach]
-
A.
usedTractionType
Indicates the type of traction or drive mechanism that was employed in performing the action or operating the entity.
-
B.
currentTractionType
Indicates the type or mode of traction currently being applied or in use in a given context.
-
C.
typicalTractionType
chosen
Indicates the usual or most common type of traction mechanism associated with an entity (such as how it is typically powered or driven).
-
D.
hasUndercarriageType
Indicates the specific type or configuration of undercarriage that an object (typically a vehicle or machine) possesses.
-
E.
hasRailwayTraction
Indicates that a railway system, line, or vehicle uses a specified type or method of traction (e.g., electric, diesel, steam) for its propulsion.
- 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_69e288c06a908190899cad4531f32c9a |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1d8db3b4c81908a36eace8ec136cc |
completed | April 29, 2026, 10:09 a.m. |
| PD | Predicate disambiguation | batch_69f1764345388190a3102b62ddb729b4 |
completed | April 29, 2026, 3:08 a.m. |
Created at: April 17, 2026, 9:58 p.m.