Triple
T20634248
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Datsun |
E507035
|
entity |
| Predicate | associatedCompany |
P629
|
FINISHED |
| Object | Renault |
—
|
NE NERFINISHED |
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: Renault | Statement: [Datsun, associatedCompany, Renault]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Renault Context triple: [Datsun, associatedCompany, Renault]
-
A.
Renault
chosen
Renault is a major French automobile manufacturer known for producing a wide range of passenger cars, commercial vehicles, and electric vehicles sold worldwide.
-
B.
Peugeot
Peugeot is a historic French automobile manufacturer known for producing a wide range of passenger cars and commercial vehicles, now operating as a core brand within the multinational automotive group Stellantis.
-
C.
Citroën
Citroën is a historic French automobile manufacturer known for its innovative engineering and distinctive car designs.
-
D.
Matra Transport
Matra Transport is a French company known for designing and producing automated urban transit and people-mover systems used in cities and airports worldwide.
-
E.
Renault Wind
The Renault Wind is a compact two-seat convertible roadster produced by the French automaker Renault in the early 2010s.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4bd4a0081908d4e97a590a33fb2 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6ad0d808c81908a60abd02a22ed92 |
completed | April 20, 2026, 10:47 p.m. |
Created at: April 16, 2026, 11:42 a.m.