Triple
T2214196
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Durant Motors |
E50985
|
entity |
| Predicate | product |
P490
|
FINISHED |
| Object | Durant automobiles |
E50985
|
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: Durant automobiles | Statement: [Durant Motors, product, Durant automobiles]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Durant automobiles Context triple: [Durant Motors, product, Durant automobiles]
-
A.
Durant Motors
chosen
Durant Motors was an early 20th-century American automobile manufacturer created by General Motors co-founder William C. Durant after his departure from GM.
-
B.
DS Automobiles
DS Automobiles is a French premium automotive brand known for its avant-garde design, advanced technology, and luxury-focused vehicles.
-
C.
Renault
Renault is a major French automobile manufacturer known for producing a wide range of passenger cars, commercial vehicles, and electric vehicles sold worldwide.
-
D.
Traton
Traton is a commercial vehicle manufacturer and holding company that oversees brands like MAN and Scania within the Volkswagen Group.
-
E.
Nissan
Nissan is a major Japanese automobile manufacturer known for producing a wide range of passenger cars, trucks, and electric vehicles sold globally.
- 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_69a88b06709c8190978fb2418470d1b6 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abbfef875c8190b642736b4cc11d4c |
completed | March 7, 2026, 6:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae6554f3308190a180cac3ad7e2ce4 |
completed | March 9, 2026, 6:14 a.m. |
Created at: March 4, 2026, 7:46 p.m.