Triple
T772211
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Honda |
E16304
|
entity |
| Predicate | competitor |
P1375
|
FINISHED |
| Object | Nissan |
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 | Statement: [Honda, competitor, Nissan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nissan Context triple: [Honda, competitor, Nissan]
-
A.
Nissan
chosen
Nissan is a major Japanese automobile manufacturer known for producing a wide range of passenger cars, trucks, and electric vehicles sold globally.
-
B.
Toyota Group
Toyota Group is a Japanese multinational corporate conglomerate centered around Toyota Motor Corporation, encompassing a network of automotive and related businesses.
-
C.
Subaru
Subaru is the Japanese name for the Pleiades star cluster, often associated with unity and prominently used as the brand name and logo motif of a major Japanese automobile manufacturer.
-
D.
Toyota Motor Corporation
Toyota Motor Corporation is a Japanese multinational automaker renowned for its reliable vehicles, pioneering of lean manufacturing and the Toyota Production System, and global leadership in hybrid technology.
-
E.
Isuzu
Isuzu is a Japanese automotive manufacturer best known for producing commercial vehicles, pickup trucks, and diesel engines for global markets.
- 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_69a49369a0848190af883934cee3db4c |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a706abf88190a1cbc2dfbbf9968a |
completed | March 1, 2026, 8:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7a3adf1d88190acec593f0aca1ec0 |
completed | March 4, 2026, 3:14 a.m. |
Created at: March 1, 2026, 7:37 p.m.