Triple
T22978890
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wazuka |
E571402
|
entity |
| Predicate | hasProduct |
P3585
|
FINISHED |
| Object | Uji-cha |
—
|
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: Uji-cha | Statement: [Wazuka, hasProduct, Uji-cha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uji-cha Context triple: [Wazuka, hasProduct, Uji-cha]
-
A.
Uji tea
chosen
Uji tea is a renowned Japanese green tea celebrated for its high quality and long history of production in the Uji region near Kyoto.
-
B.
Oicha
Oicha is a principal town in the Beni Territory of North Kivu Province in the eastern Democratic Republic of the Congo.
-
C.
Chai
Chai is the guitar-wielding, rhythm-obsessed main hero of the action-rhythm game Hi-Fi Rush, known for battling enemies in sync with the music.
-
D.
Chai
Chai is a popular JavaScript assertion library commonly used in testing frameworks like Mocha to provide expressive, readable test assertions.
-
E.
Sayama tea
Sayama tea is a high-quality Japanese green tea from the Sayama region of Saitama Prefecture, renowned for its rich flavor and thick, full-bodied leaves.
- 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_69e245b3c50481908bb3741ec9f40862 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18293f830819095cca91af7abd742 |
completed | April 29, 2026, 4:01 a.m. |
Created at: April 17, 2026, 3:49 p.m.