Triple
T13443066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zhejiang Geely Holding Group |
E320412
|
entity |
| Predicate | hasDivision |
P35
|
FINISHED |
| Object | Volvo Car Group |
E83041
|
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: Volvo Car Group | Statement: [Zhejiang Geely Holding Group, hasDivision, Volvo Car Group]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Volvo Car Group Context triple: [Zhejiang Geely Holding Group, hasDivision, Volvo Car Group]
-
A.
Volvo Cars
chosen
Volvo Cars is a Swedish automotive manufacturer known for its focus on safety, practical design, and premium vehicles.
-
B.
Volvo Group
Volvo Group is a Swedish multinational manufacturing company best known for producing trucks, buses, construction equipment, and marine and industrial engines.
-
C.
Saab Automobile
Saab Automobile was a Swedish car manufacturer known for its innovative engineering, turbocharged engines, and distinctive, safety-focused designs.
-
D.
Saab AB
Saab AB is a Swedish aerospace and defense company known for developing military aircraft, advanced defense systems, and security solutions.
-
E.
Scania
Scania is a Swedish manufacturer renowned for its heavy trucks, buses, and industrial and marine engines.
- 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_69d80761e6cc8190a90c844589998ecc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaee881888190811ddf01bc699864 |
completed | April 12, 2026, 2:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7547b484c8190881e869b3a722e89 |
completed | May 3, 2026, 1:58 p.m. |
Created at: April 9, 2026, 9:40 p.m.