Triple
T35758111
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mercedes-Benz W140 |
E1033499
|
entity |
| Predicate | topModelEngine |
P183632
|
FINISHED |
| Object | 6.0-litre V12 |
—
|
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: 6.0-litre V12 | Statement: [Mercedes-Benz W140, topModelEngine, 6.0-litre V12]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: topModelEngine Context triple: [Mercedes-Benz W140, topModelEngine, 6.0-litre V12]
-
A.
originalEngineModel
Indicates that one engine is the original or initial model from which another engine or engine variant is derived.
-
B.
plannedEngineModel
Indicates that an engine model has been selected or designated as the one intended to be used or implemented in a future plan or configuration.
-
C.
modelIn
Indicates that one entity serves as a representation or simulation of another entity.
-
D.
racingModel
Indicates that one entity is a specific model or version designed or configured for racing in relation to another entity.
-
E.
mostPopularModel
Indicates that the referenced model is the one with the highest popularity or usage compared to all other models in the relevant set.
- 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_69f76e1262f48190a313318665acc189 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7a1f64f1081908cc2774840684310 |
completed | May 3, 2026, 7:28 p.m. |
| PD | Predicate disambiguation | batch_69f7a070e23881909a233370acb57384 |
completed | May 3, 2026, 7:22 p.m. |
| PDg | Predicate description generation | batch_69f7a162672481909773f8383d91159a |
completed | May 3, 2026, 7:26 p.m. |
Created at: May 3, 2026, 4:06 p.m.