Triple
T291349
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Volkswagen Group |
E6000
|
entity |
| Predicate | owns |
P347
|
FINISHED |
| Object | Cupra |
E37746
|
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: Cupra | Statement: [Volkswagen Group, owns, Cupra]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cupra Context triple: [Volkswagen Group, owns, Cupra]
-
A.
SEAT
chosen
SEAT is a Spanish automobile manufacturer known for producing affordable, stylish cars and operating as a subsidiary of the Volkswagen Group.
-
B.
Maserati
Maserati is an Italian luxury automobile manufacturer renowned for its high-performance sports cars and grand tourers distinguished by elegant design and racing heritage.
-
C.
Abarth
Abarth is an Italian performance car and racing brand known for tuning and producing sporty versions of Fiat and other compact vehicles.
-
D.
Lotus Elise
The Lotus Elise is a lightweight, mid-engined British sports car renowned for its agile handling and minimalist, driver-focused design.
-
E.
Lancia
Lancia is an Italian automobile manufacturer renowned for its historic innovations and success in motorsport, particularly rally racing.
- 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_69a2e79114b081909490b3bf5a5dbb51 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2e975d2c0819082bbf6a0f3d928af |
completed | Feb. 28, 2026, 1:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3a5d16298819094d742e34ec86f97 |
completed | March 1, 2026, 2:34 a.m. |
Created at: Feb. 28, 2026, 1:06 p.m.