Triple
T32425130
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japanese Black |
E828556
|
entity |
| Predicate | primaryUseInJapan |
P10000
|
FINISHED |
| Object | high-quality beef |
—
|
LITERAL 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: high-quality beef | Statement: [Japanese Black, primaryUseInJapan, high-quality beef]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryUseInJapan Context triple: [Japanese Black, primaryUseInJapan, high-quality beef]
-
A.
primaryUseOf
Indicates that one entity serves as the main or principal function, purpose, or application of another entity.
-
B.
isPrimarilyUsedAs
Indicates that one entity serves mainly or most commonly in the role, function, or purpose specified by the other entity.
-
C.
usedPrimarilyIn
chosen
Indicates that something is mainly or most commonly employed within a particular context, domain, or purpose.
-
D.
rankByCommonnessInJapan
Indicates how items are ordered based on how commonly they occur or are found in Japan.
-
E.
introducedToJapan
Indicates that one entity brought or presented another entity into Japan for the first time or for initial exposure there.
- F. None of above.
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_69f3491b28bc8190b75cea7a507f337b |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a037c894b488190bcbec2eccaff4a01 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379edf2d88190b492fca86ed23cac |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 12:54 a.m.