Triple
T28929004
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Volkswagen Type 3 |
E733728
|
entity |
| Predicate | notchbackName |
P166026
|
FINISHED |
| Object | Volkswagen 1500 Notchback |
—
|
NE NERFINISHED |
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: Volkswagen 1500 Notchback | Statement: [Volkswagen Type 3, notchbackName, Volkswagen 1500 Notchback]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notchbackName Context triple: [Volkswagen Type 3, notchbackName, Volkswagen 1500 Notchback]
-
A.
isOnRearPlate
Indicates that one entity is positioned on or attached to the rear plate of another entity.
-
B.
steeringWheelType
Indicates the specific kind or design of steering wheel associated with an entity (such as a vehicle or driving setup).
-
C.
hasHatchback
Indicates that one entity possesses or is characterized by having a hatchback-style vehicle or body type.
-
D.
buttplateType
Indicates the specific style or design category of a buttplate used on an object, typically a firearm or similar equipment.
-
E.
chassisMaterialFeature
Indicates that an entity has a chassis characterized by a specific material-related feature or property.
- 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_69f05b0b49b08190b8994b339c7980f6 |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69f65bb75cd08190bbdb63c093ad6210 |
completed | May 2, 2026, 8:16 p.m. |
| PD | Predicate disambiguation | batch_69f659d02f1c8190831758ac52bb54e4 |
completed | May 2, 2026, 8:08 p.m. |
| PDg | Predicate description generation | batch_69f65b136b30819090cf59fb772f35f1 |
completed | May 2, 2026, 8:14 p.m. |
Created at: April 28, 2026, 8:26 a.m.