Triple
T8350298
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mazda6 |
E196141
|
entity |
| Predicate | manufacturer |
P490
|
FINISHED |
| Object | Mazda |
E91822
|
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 | Statement: [Mazda6, manufacturer, Mazda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mazda Context triple: [Mazda6, manufacturer, Mazda]
-
A.
Mazda Motor Corporation
chosen
Mazda Motor Corporation is a Japanese automaker known for its innovative engineering, including rotary engines and driver-focused vehicles, and for its global presence in the automotive market.
-
B.
Mazda Motor Manufacturing USA
Mazda Motor Manufacturing USA was a former automotive manufacturing joint venture facility in Flat Rock, Michigan, that produced vehicles for Mazda (and later Ford) before being renamed the Flat Rock Assembly Plant.
-
C.
Honda
Honda is a historic Colombian river port city on the Magdalena River, known for its colonial architecture and numerous bridges.
-
D.
Honda
Honda is a major Japanese automobile and motorcycle manufacturer known for its reliable, fuel-efficient vehicles and global market presence.
-
E.
Mitsubishi
Mitsubishi is a major Japanese multinational conglomerate known for its diverse businesses in industries such as automotive, heavy industry, finance, and electronics.
- 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_69ca82edd63c8190b876b8465464c5fa |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb80181ca48190bbf2e6a6aae80d69 |
completed | March 31, 2026, 8:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cde7b2d00c8190b7df13a0853a6374 |
completed | April 2, 2026, 3:51 a.m. |
Created at: March 30, 2026, 5:59 p.m.