Triple
T16332174
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mazda CX-9 |
E396580
|
entity |
| Predicate | safetyTechnology |
P65838
|
FINISHED |
| Object | blind-spot monitoring |
—
|
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: blind-spot monitoring | Statement: [Mazda CX-9, safetyTechnology, blind-spot monitoring]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: safetyTechnology Context triple: [Mazda CX-9, safetyTechnology, blind-spot monitoring]
-
A.
safety
Indicates that an entity provides, ensures, or is associated with protection from harm, danger, or risk for another entity or within a given context.
-
B.
safetyCategory
Indicates the classification of something according to its level or type of safety.
-
C.
safetyConcept
Indicates that something embodies, represents, or is associated with a principle, idea, or framework related to safety.
-
D.
safetyBenefit
Indicates that one entity provides, contributes to, or results in an improvement in the safety or risk reduction experienced by another entity.
-
E.
usesVehicleTechnology
chosen
Indicates that an entity employs or applies a specific vehicle-related technology in its operations, products, or activities.
- 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_69d87f255b788190a400eba031dd85d8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2c4e0b1388190824b286e8452fb32 |
completed | April 17, 2026, 11:40 p.m. |
| PD | Predicate disambiguation | batch_69e226eba9b48190af6e80d3d1c2aed3 |
completed | April 17, 2026, 12:26 p.m. |
Created at: April 10, 2026, 5:07 a.m.