Triple
T2999844
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mercedes-Benz A-Class |
E81158
|
entity |
| Predicate | hasPerformanceVariant |
P12592
|
FINISHED |
| Object | Mercedes-AMG A-Class models |
—
|
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: Mercedes-AMG A-Class models | Statement: [Mercedes-Benz A-Class, hasPerformanceVariant, Mercedes-AMG A-Class models]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPerformanceVariant Context triple: [Mercedes-Benz A-Class, hasPerformanceVariant, Mercedes-AMG A-Class models]
-
A.
hasVariant
Indicates that one entity exists as an alternative form, version, or variation of another entity.
-
B.
hasVariantSystem
Indicates that one system is an alternative or variant form of another system within the same general framework or category.
-
C.
hasVariantSeries
Indicates a relationship where one entity is a variant or alternative series derived from or associated with another series.
-
D.
notableHighPerformanceVariant
chosen
Indicates that an entity is recognized as a particularly high-performance version or variant of another entity.
-
E.
supportsModelVariant
Indicates that one entity is capable of operating with, being compatible with, or otherwise accommodating a specific variant of a model.
- 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_69ad8b187fc8819085914d3c9ea3142d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad99f8eb248190b50fd539a06a5a62 |
completed | March 8, 2026, 3:47 p.m. |
| PD | Predicate disambiguation | batch_69ad9615fefc8190ad96da92519cb7a3 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 2:59 p.m.