Triple
T9560399
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Forney locomotive |
E230656
|
entity |
| Predicate | hasWheelArrangementType |
P5627
|
FINISHED |
| Object | leading driving wheels rigidly mounted in frame |
—
|
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: leading driving wheels rigidly mounted in frame | Statement: [Forney locomotive, hasWheelArrangementType, leading driving wheels rigidly mounted in frame]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasWheelArrangementType Context triple: [Forney locomotive, hasWheelArrangementType, leading driving wheels rigidly mounted in frame]
-
A.
wheelArrangementSystem
chosen
Indicates the specific configuration or system by which the wheels of a vehicle or rolling stock are arranged and organized.
-
B.
wheelType
Indicates the specific kind or category of wheel associated with an entity.
-
C.
hasAxleCount
Indicates the number of axles that an object (typically a vehicle or rolling stock) possesses.
-
D.
numberOfWheels
Indicates the quantity of wheels that an entity possesses or is associated with.
-
E.
numberOfRoadWheelsPerSide
Indicates the count of road wheels present on each side of a vehicle or similar wheeled system.
- 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_69ca847e53a88190a60eed7e02257f10 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd994bde0c8190afcba5cb8fa8b984 |
completed | April 1, 2026, 10:16 p.m. |
| PD | Predicate disambiguation | batch_69ccd594d0ac8190a81bc11a3a538167 |
completed | April 1, 2026, 8:21 a.m. |
Created at: March 30, 2026, 8:03 p.m.