Triple
T8929233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TDI engines |
E212609
|
entity |
| Predicate | marketedBy |
P4613
|
FINISHED |
| Object | Volkswagen |
E6000
|
NE 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: Volkswagen | Statement: [TDI engines, marketedBy, Volkswagen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Volkswagen Context triple: [TDI engines, marketedBy, Volkswagen]
-
A.
Volkswagen Group
chosen
Volkswagen Group is a major German multinational automotive manufacturer that owns brands such as Volkswagen, Audi, Porsche, and Škoda and is one of the largest car producers in the world.
-
B.
Audi
Audi is a German luxury automobile manufacturer known for its premium vehicles, advanced engineering, and signature quattro all-wheel-drive technology.
-
C.
Daimler
Daimler is a historic British luxury automobile manufacturer renowned for producing high-end saloon cars and limousines, particularly favored by royalty and official state fleets.
-
D.
Porsche
Porsche is a German luxury automobile manufacturer renowned for its high-performance sports cars, SUVs, and engineering excellence.
-
E.
Volkswagen Truck & Bus
Volkswagen Truck & Bus is a commercial vehicle manufacturer within the Volkswagen Group, known for producing trucks and buses for global markets.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca8395c438819087d7cb844ab5990c |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc667470308190a75ba63de803e3a2 |
completed | April 1, 2026, 12:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d23c6f38ac8190a652575b8dc2fd45 |
completed | April 5, 2026, 10:41 a.m. |
Created at: March 30, 2026, 6:57 p.m.