Triple
T35420295
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BMW 435i Gran Coupé |
E1023764
|
entity |
| Predicate | numberOfDoorsDescription |
P128223
|
FINISHED |
| Object | four-door coupé with frameless doors |
—
|
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: four-door coupé with frameless doors | Statement: [BMW 435i Gran Coupé, numberOfDoorsDescription, four-door coupé with frameless doors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfDoorsDescription Context triple: [BMW 435i Gran Coupé, numberOfDoorsDescription, four-door coupé with frameless doors]
-
A.
numberOfDoors
Indicates the quantity of doors associated with an entity.
-
B.
hasDoors
Indicates that an object or structure possesses one or more doors.
-
C.
vehicleDoorType
chosen
Indicates the specific style or configuration of doors that a vehicle has.
-
D.
doorsPerSidePerCar
Indicates the number of doors on each side of each car in a vehicle or train.
-
E.
hasDoorsOperation
Indicates that an entity has a specific type or mode of operation for its doors (e.g., how they open, close, or are controlled).
- 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_69f76df6704081909900c60be10d5849 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_6a037c8d06cc8190ab6a5e18d9d2571e |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a0324d08190ac5b610cc0f6a38c |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:03 p.m.