Triple
T20185347
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peugeot 605 |
E492841
|
entity |
| Predicate | manufacturer |
P490
|
FINISHED |
| Object | Peugeot |
—
|
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: Peugeot | Statement: [Peugeot 605, manufacturer, Peugeot]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peugeot Context triple: [Peugeot 605, manufacturer, Peugeot]
-
A.
Peugeot
chosen
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.
-
B.
Renault
Renault is a major French automobile manufacturer known for producing a wide range of passenger cars, commercial vehicles, and electric vehicles sold worldwide.
-
C.
Citroën
Citroën is a historic French automobile manufacturer known for its innovative engineering and distinctive car designs.
-
D.
Peugeot Partner
The Peugeot Partner is a compact panel van and leisure activity vehicle produced by the French automaker Peugeot, widely used for both commercial and family transport.
-
E.
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.
- 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_69da6268a034819081cbd9ea5a1c9475 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e668f2c03c819096336462f59dba91 |
completed | April 20, 2026, 5:57 p.m. |
Created at: April 11, 2026, 11:36 p.m.