Triple
T15790550
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | C257 |
E382852
|
entity |
| Predicate | modelGeneration |
P2006
|
FINISHED |
| Object | third-generation Mercedes-Benz CLS |
—
|
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: third-generation Mercedes-Benz CLS | Statement: [C257, modelGeneration, third-generation Mercedes-Benz CLS]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: modelGeneration Context triple: [C257, modelGeneration, third-generation Mercedes-Benz CLS]
-
A.
architectureGeneration
Indicates the creation or design of an architectural structure, system, or layout from given inputs or specifications.
-
B.
model
chosen
Indicates that one entity serves as a representation, example, or simulation of another entity or concept.
-
C.
generation
Indicates the relationship in which one entity produces, creates, or brings another entity into existence.
-
D.
architecturalGeneration
Indicates a relationship where one entity is responsible for creating, designing, or originating the architecture of another entity.
-
E.
neuralEngineGeneration
Indicates the generation or version of a device’s neural processing engine used to perform machine learning or AI-related computations.
- 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_69d86da16e188190b89af699f1ed0bfe |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e0b4d819c881908bc43a6124a1bb2e |
completed | April 16, 2026, 10:07 a.m. |
| PD | Predicate disambiguation | batch_69e00537bd1c81908d6e832792fd934f |
completed | April 15, 2026, 9:37 p.m. |
Created at: April 10, 2026, 4:48 a.m.