Triple
T13228108
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mercedes-Benz EQV |
E314932
|
entity |
| Predicate | cargoCapacityConfigurable |
P108625
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Mercedes-Benz EQV, cargoCapacityConfigurable, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cargoCapacityConfigurable Context triple: [Mercedes-Benz EQV, cargoCapacityConfigurable, yes]
-
A.
cargoCapacityFeature
Indicates that an entity has a feature specifying how much cargo it can carry or accommodate.
-
B.
designedCargoCapacity
Indicates the maximum amount of cargo an object (such as a vehicle or container) was originally engineered or specified to carry.
-
C.
cargoSpace
Indicates that one entity provides storage capacity or room for carrying goods, equipment, or other items for another entity.
-
D.
supportsCargoType
Indicates that one entity is capable of handling, transporting, or accommodating a specified type of cargo.
-
E.
cargoLoading
Indicates the action or process of placing cargo onto a vehicle, vessel, or other transport medium for shipment or movement.
- 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_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. |
| PDg | Predicate description generation | batch_69d98c959ba08190adf29dc0c4e1fca6 |
completed | April 10, 2026, 11:49 p.m. |
Created at: April 9, 2026, 9:21 p.m.