Triple
T11330536
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japan |
E268330
|
entity |
| Predicate | isMarketFor |
P5252
|
FINISHED |
| Object | Toyota Kluger |
E268327
|
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: Toyota Kluger | Statement: [Japan, isMarketFor, Toyota Kluger]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Toyota Kluger Context triple: [Japan, isMarketFor, Toyota Kluger]
-
A.
Toyota Kluger
chosen
The Toyota Kluger is a mid-size crossover SUV produced by Toyota, marketed in various regions as a comfortable, family-oriented vehicle with three-row seating.
-
B.
Isuzu
Isuzu is a Japanese automotive manufacturer best known for producing commercial vehicles, pickup trucks, and diesel engines for global markets.
-
C.
Azuga
Azuga is a small mountain resort town in Romania known for its ski slopes and scenic location in the Carpathian Mountains.
-
D.
Hino
Hino is a city in western Tokyo, Japan, known as a residential and industrial suburb within the Tama area.
-
E.
Hino
Hino is a town in Shiga Prefecture, Japan, known for its historical streetscapes and traditional industries.
- 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_69d6aacb1f0881908c84a349fd1be047 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e9fd38308190a5458be1bfcc89ea |
completed | April 9, 2026, 6:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e526236b688190aca4f2400a12e726 |
completed | April 19, 2026, 6:59 p.m. |
Created at: April 8, 2026, 9:32 p.m.