Triple
T11330538
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japan |
E268330
|
entity |
| Predicate | hasAutomotiveRegulation |
P49787
|
FINISHED |
| Object | right-hand-drive vehicles |
—
|
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: right-hand-drive vehicles | Statement: [Japan, hasAutomotiveRegulation, right-hand-drive vehicles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasAutomotiveRegulation Context triple: [Japan, hasAutomotiveRegulation, right-hand-drive vehicles]
-
A.
hasRegulations
Indicates that one entity imposes, contains, or is associated with rules or regulatory requirements that govern the behavior or operation of another entity.
-
B.
hasAutomotiveIndustryBrand
Indicates that an entity possesses, is associated with, or operates a specific brand within the automotive industry.
-
C.
hasSafetyRegulationCompliance
Indicates that an entity adheres to, satisfies, or is in conformity with specified safety regulations or standards.
-
D.
automotiveClassSupported
Indicates that a particular automotive class or category is supported or compatible within a given context or system.
-
E.
hasTrafficRegime
chosen
Indicates that a specified traffic control or regulatory system applies to a given road, area, or transport context.
- 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_69d6aacb1f0881908c84a349fd1be047 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e9fd38308190a5458be1bfcc89ea |
completed | April 9, 2026, 6:03 p.m. |
| PD | Predicate disambiguation | batch_69d787afe5a48190b8af1a3e19529641 |
completed | April 9, 2026, 11:04 a.m. |
Created at: April 8, 2026, 9:32 p.m.