Triple
T36011801
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lynk & Co |
E1041730
|
entity |
| Predicate | vehicleArchitecture |
P184493
|
FINISHED |
| Object | Compact Modular Architecture |
—
|
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: Compact Modular Architecture | Statement: [Lynk & Co, vehicleArchitecture, Compact Modular Architecture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: vehicleArchitecture Context triple: [Lynk & Co, vehicleArchitecture, Compact Modular Architecture]
-
A.
vehicleLayout
Indicates how the components or seating within a vehicle are arranged or configured relative to each other.
-
B.
chassisConstruction
Indicates how the chassis of an object is built or assembled, specifying the construction method or structural design used.
-
C.
powertrainCategory
Indicates the classification of a vehicle’s powertrain based on how it delivers propulsion (e.g., internal combustion, hybrid, electric).
-
D.
powertrainComponentOf
Indicates that one entity is a component or subsystem that forms part of the powertrain of another entity.
-
E.
featuresVehicle
Indicates that one entity includes, presents, or prominently incorporates a particular vehicle as part of its content, composition, or offering.
- 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_69f76e2b981881908e4e160607fa82eb |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7b35e32d481909ef0220e6f6ff4a8 |
completed | May 3, 2026, 8:43 p.m. |
| PD | Predicate disambiguation | batch_69f7b1bad2e88190963ab4ee5d4f2038 |
completed | May 3, 2026, 8:36 p.m. |
| PDg | Predicate description generation | batch_69f7b2c66054819083897e25edb65ba7 |
completed | May 3, 2026, 8:40 p.m. |
Created at: May 3, 2026, 4:07 p.m.