Triple
T20721494
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beam Suntory |
E509323
|
entity |
| Predicate | brand |
P1500
|
FINISHED |
| Object | Yamazaki |
—
|
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: Yamazaki | Statement: [Beam Suntory, brand, Yamazaki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yamazaki Context triple: [Beam Suntory, brand, Yamazaki]
-
A.
Yamazaki
chosen
Yamazaki is a common Japanese surname and place name associated with various individuals, companies, and locations in Japan.
-
B.
Ozaki
Ozaki is a Japanese surname borne by various notable figures in politics, literature, and the arts.
-
C.
Yamakita
Yamakita is a rural town in Kanagawa Prefecture, Japan, known for its mountainous terrain, hot springs, and access to outdoor activities such as hiking and river sports.
-
D.
Oyamazaki
Oyamazaki is a town in Kyoto Prefecture, Japan, known for its historical significance and scenic location at the confluence of major rivers and transportation routes.
-
E.
Yamakoshi
Yamakoshi is a recurring character from the Disney XD sitcom "Pair of Kings," known as a mystical fish with prophetic abilities and a quirky, comedic presence.
- 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_69e0b4c4cc648190b45fda6e2b20af56 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c1d5eec08190832ffba8a90f3076 |
completed | April 21, 2026, 12:16 a.m. |
Created at: April 16, 2026, 12:27 p.m.