Triple
T23011479
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Isuzu Motors Lynx |
E572916
|
entity |
| Predicate | sponsor |
P67
|
FINISHED |
| Object | Isuzu Motors |
—
|
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: Isuzu Motors | Statement: [Isuzu Motors Lynx, sponsor, Isuzu Motors]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Isuzu Motors Context triple: [Isuzu Motors Lynx, sponsor, Isuzu Motors]
-
A.
Isuzu
chosen
Isuzu is a Japanese automotive manufacturer best known for producing commercial vehicles, pickup trucks, and diesel engines for global markets.
-
B.
Isuzu-gawa
Isuzu-gawa is a river in Ise, Mie Prefecture, Japan, best known for flowing through the sacred grounds of the Ise Grand Shrine.
-
C.
Isuzu Yamada
Isuzu Yamada was a renowned Japanese actress celebrated for her powerful performances in classic films, particularly in collaboration with director Akira Kurosawa.
-
D.
Daihatsu Motor Co., Ltd.
Daihatsu Motor Co., Ltd. is a Japanese automobile manufacturer best known for producing compact cars and kei vehicles, and operates as a subsidiary within the Toyota group.
-
E.
Mitsubishi Motors
Mitsubishi Motors is a Japanese automotive manufacturer known for producing a wide range of passenger cars, SUVs, and light commercial vehicles and for its involvement in global automotive alliances.
- 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_69e245b764cc8190a51be76f1d9611e1 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1835b0cb881908d3d2dd40cffcbc2 |
completed | April 29, 2026, 4:04 a.m. |
Created at: April 17, 2026, 3:51 p.m.