Triple
T10248122
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Isuzu D-Max |
E240271
|
entity |
| Predicate | alsoSoldAs |
P21045
|
FINISHED |
| Object | Mazda BT-50 |
E395832
|
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: Mazda BT-50 | Statement: [Isuzu D-Max, alsoSoldAs, Mazda BT-50]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mazda BT-50 Context triple: [Isuzu D-Max, alsoSoldAs, Mazda BT-50]
-
A.
Mazda BT-50
chosen
The Mazda BT-50 is a mid-size pickup truck designed for both work and recreational use, known for its durability, towing capability, and shared underpinnings with Ford’s Ranger in earlier generations.
-
B.
Isuzu MU-X
The Isuzu MU-X is a mid-size SUV produced by Japanese automaker Isuzu, known for its rugged body-on-frame construction and strong diesel engine options.
-
C.
Nissan NV400
The Nissan NV400 is a large light commercial van developed in partnership with Renault and Opel/Vauxhall, sharing its platform with the Renault Master.
-
D.
Nissan Patrol
The Nissan Patrol is a large, rugged SUV renowned for its off-road capability and long-standing use as a durable 4x4 in markets worldwide.
-
E.
Isuzu D-Max
The Isuzu D-Max is a popular mid-size pickup truck known for its durability, strong diesel engines, and widespread use in both commercial and personal applications worldwide.
- 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_69d381a7e198819090280d5ab885d59e |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d23b620c8190b8a72d0eb0d16b93 |
completed | April 7, 2026, 9:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f7ade8448190830d950b7cee0c34 |
completed | April 9, 2026, 12:49 a.m. |
Created at: April 6, 2026, 11:27 a.m.