Triple
T17786965
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yamaguchi Prefecture |
E444041
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Ube |
—
|
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: Ube | Statement: [Yamaguchi Prefecture, hasCity, Ube]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ube Context triple: [Yamaguchi Prefecture, hasCity, Ube]
-
A.
Ube
chosen
Ube is an industrial city in western Japan known for its chemical industry, cement production, and efforts to improve its environment and urban landscape.
-
B.
Taro
Taro is a common Japanese male given name, often written with kanji meaning "eldest son" or similar traditional connotations.
-
C.
Taro
The Taro is a river in northern Italy that flows through the Emilia-Romagna region and ultimately joins the Po River.
-
D.
Shiso
Shiso is a small inland city in Japan’s Hyogo Prefecture known for its mountainous scenery, forests, and outdoor recreation.
-
E.
Wasabi
Wasabi is a French action-comedy film known for its blend of fast-paced thrills and humor, directed by Gérard Krawczyk and starring Jean Reno.
- 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_69d8b9ef17708190bdf7e2adbf14ddc2 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e487938418819096016ad717b014e6 |
completed | April 19, 2026, 7:43 a.m. |
Created at: April 10, 2026, 10:12 a.m.