Triple
T1903577
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SEAT |
E37746
|
entity |
| Predicate | collaboratesWith |
P37
|
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: [SEAT, collaboratesWith, Volkswagen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Volkswagen Context triple: [SEAT, collaboratesWith, 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.
Porsche
Porsche is a German luxury automobile manufacturer renowned for its high-performance sports cars, SUVs, and engineering excellence.
-
D.
Volkswagen Truck & Bus
Volkswagen Truck & Bus is a commercial vehicle manufacturer within the Volkswagen Group, known for producing trucks and buses for global markets.
-
E.
Volkswagen Commercial Vehicles
Volkswagen Commercial Vehicles is a division of the Volkswagen Group specializing in the development, production, and sale of light commercial vehicles such as vans and pickups for business and private use.
- 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_69a8861be7148190a680937ec451a304 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb1909aec8190b3259c8f969ce81e |
completed | March 7, 2026, 5:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae95e5cf008190a6638bb2c8d4d505 |
completed | March 9, 2026, 9:41 a.m. |
Created at: March 4, 2026, 7:35 p.m.