Triple
T30360578
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Audi A3 e-tron |
E772270
|
entity |
| Predicate | co2Emissions |
P169111
|
FINISHED |
| Object | reduced compared to conventional petrol Audi A3 |
—
|
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: reduced compared to conventional petrol Audi A3 | Statement: [Audi A3 e-tron, co2Emissions, reduced compared to conventional petrol Audi A3]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: co2Emissions Context triple: [Audi A3 e-tron, co2Emissions, reduced compared to conventional petrol Audi A3]
-
A.
fuelConsumption
Indicates the amount of fuel used by an entity (such as a vehicle or device) over a specified distance, time, or operation.
-
B.
hasPersistentGasEmissions
Indicates that an entity continuously or repeatedly releases gaseous substances over an extended period.
-
C.
emissionsComparedToDiesel
Indicates how the emissions produced by something compare in amount or impact to those produced by diesel.
-
D.
fuelEfficiency
Indicates how effectively an entity uses fuel to perform a given amount of work or travel a certain distance.
-
E.
coalUse
Indicates the use or consumption of coal by an entity, typically as a fuel or energy source.
- 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_69f2248c6f5c8190a6177842bf791a3c |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f68242fc208190ae8a9ab1a4bdccfd |
completed | May 2, 2026, 11:01 p.m. |
| PD | Predicate disambiguation | batch_69f678d019fc8190913662cd2f87b857 |
completed | May 2, 2026, 10:21 p.m. |
| PDg | Predicate description generation | batch_69f679496c188190ba585792f987a1f4 |
completed | May 2, 2026, 10:23 p.m. |
Created at: April 29, 2026, 7:57 p.m.