Triple
T30913573
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mercedes-Benz MRA platform |
E787520
|
entity |
| Predicate | vehicleSegmentSupported |
P97747
|
FINISHED |
| Object | mid-size segment |
—
|
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: mid-size segment | Statement: [Mercedes-Benz MRA platform, vehicleSegmentSupported, mid-size segment]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: vehicleSegmentSupported Context triple: [Mercedes-Benz MRA platform, vehicleSegmentSupported, mid-size segment]
-
A.
automotiveClassSupported
Indicates that a particular automotive class or category is supported or compatible within a given context or system.
-
B.
vehicleEligibility
Indicates whether a given vehicle satisfies the required conditions or criteria to be considered eligible for a specified purpose or program.
-
C.
supportsVehicle
Indicates that one entity provides the necessary strength, stability, or structure to bear the weight of a vehicle.
-
D.
vehicleClassServed
Indicates the class or type of vehicle that a service, facility, or operation is designed to accommodate or serve.
-
E.
intendedVehicleClass
chosen
Indicates that one entity is designed or specified to be used with, or is appropriate for, a particular class or category of vehicle.
- 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_69f224be300c8190a6513ce1ee0a7026 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f69dfdda708190be290c7bec205445 |
completed | May 3, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69f69d1a37e081908d1d86b90ff502bd |
completed | May 3, 2026, 12:55 a.m. |
Created at: April 29, 2026, 8:51 p.m.