Triple
T13070032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BMW 4 Series (F32/F33/F36) |
E329430
|
entity |
| Predicate | chassisCodeGranCoupé |
P44383
|
FINISHED |
| Object | F36 |
—
|
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: F36 | Statement: [BMW 4 Series (F32/F33/F36), chassisCodeGranCoupé, F36]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: chassisCodeGranCoupé Context triple: [BMW 4 Series (F32/F33/F36), chassisCodeGranCoupé, F36]
-
A.
chassisCode
chosen
Indicates the specific chassis designation or code assigned to a vehicle model to distinguish its underlying structural platform or variant.
-
B.
chassis
Indicates that one entity serves as the structural frame or supporting base (chassis) for another entity.
-
C.
chassisConstruction
Indicates how the chassis of an object is built or assembled, specifying the construction method or structural design used.
-
D.
chassisMaterialFeature
Indicates that an entity has a chassis characterized by a specific material-related feature or property.
-
E.
chassisName
Indicates the designated name or identifier assigned to a chassis in a system or dataset.
- 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_69d80771749c81909a6d9197b9504872 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d980ee6130819095d835e7ff6a8c5b |
completed | April 10, 2026, 10:59 p.m. |
| PD | Predicate disambiguation | batch_69d9803d46688190bac6b7d208f08d01 |
completed | April 10, 2026, 10:57 p.m. |
Created at: April 9, 2026, 9 p.m.