Triple
T1499709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mobileye |
E29767
|
entity |
| Predicate | notableClient |
P7186
|
FINISHED |
| Object | Honda |
E16304
|
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: Honda | Statement: [Mobileye, notableClient, Honda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Honda Context triple: [Mobileye, notableClient, Honda]
-
A.
Honda
chosen
Honda is a major Japanese automobile and motorcycle manufacturer known for its reliable, fuel-efficient vehicles and global market presence.
-
B.
Mazda Motor Corporation
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.
-
C.
Honda Civic
The Honda Civic is a popular compact car known for its reliability, fuel efficiency, and long-standing presence in Honda’s global lineup.
-
D.
Acura
Acura is Honda's luxury vehicle division, known for producing premium cars and SUVs with a focus on performance, technology, and reliability.
-
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_69a498dba1d8819093b46a3a8d2485f1 |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c6f0ce988190aafab4a6e0dfd710 |
completed | March 1, 2026, 11:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad2331b49881908672251bb86418df |
completed | March 8, 2026, 7:20 a.m. |
Created at: March 1, 2026, 8:12 p.m.