Triple
T13228104
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mercedes-Benz EQV |
E314932
|
entity |
| Predicate | longWheelbaseVersionLengthApproximate |
P3491
|
FINISHED |
| Object | 5370 mm |
—
|
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: 5370 mm | Statement: [Mercedes-Benz EQV, longWheelbaseVersionLengthApproximate, 5370 mm]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: longWheelbaseVersionLengthApproximate Context triple: [Mercedes-Benz EQV, longWheelbaseVersionLengthApproximate, 5370 mm]
-
A.
wheelbase
Indicates the distance between the centers of the front and rear wheels of a vehicle.
-
B.
wheelHeightApprox
Indicates that the height of a wheel is approximately equal to a specified value or to the height of another wheel.
-
C.
wheelbaseVariantOf
Indicates a relationship where one vehicle’s wheelbase configuration is a variant or modified version of another vehicle’s wheelbase.
-
D.
hasCarLength
chosen
Indicates that an entity is associated with a specific measurement representing the length of a car.
-
E.
wheelbaseClass
Indicates the classification of a vehicle based on the length or category of its wheelbase.
- 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_69d806affc688190a25b6ccc588e9c72 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98d3232d48190a3c792b025c596a6 |
completed | April 10, 2026, 11:52 p.m. |
| PD | Predicate disambiguation | batch_69d98bcb21648190aef241de1e7887e2 |
completed | April 10, 2026, 11:46 p.m. |
Created at: April 9, 2026, 9:21 p.m.