Triple
T10530179
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yoshisuke Aikawa |
E248417
|
entity |
| Predicate | hasEmployer |
P7
|
FINISHED |
| Object | Nissan Group |
E21556
|
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: Nissan Group | Statement: [Yoshisuke Aikawa, hasEmployer, Nissan Group]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nissan Group Context triple: [Yoshisuke Aikawa, hasEmployer, Nissan Group]
-
A.
Toyota Group
Toyota Group is a Japanese multinational corporate conglomerate centered around Toyota Motor Corporation, encompassing a network of automotive and related businesses.
-
B.
Nissan
chosen
Nissan is a major Japanese automobile manufacturer known for producing a wide range of passenger cars, trucks, and electric vehicles sold globally.
-
C.
Nissan
Nissan is a river in southwestern Sweden that flows through the province of Halland before reaching the Kattegat.
-
D.
Mitsubishi Group
Mitsubishi Group is a major Japanese conglomerate (keiretsu) comprising numerous companies across industries such as finance, heavy industry, automotive, and electronics.
-
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 (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_69d381c5c7448190bec34bee7ec72bac |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d509f7d8ac8190b90c1a7f77b23545 |
completed | April 7, 2026, 1:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d933fffc4c81908798094f72a06d18 |
completed | April 10, 2026, 5:31 p.m. |
Created at: April 6, 2026, 12:30 p.m.