Triple
T19937574
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shizuoka Prefecture |
E479212
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Kakegawa |
—
|
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: Kakegawa | Statement: [Shizuoka Prefecture, hasCity, Kakegawa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kakegawa Context triple: [Shizuoka Prefecture, hasCity, Kakegawa]
-
A.
Nakatsugawa
Nakatsugawa is a city in Gifu Prefecture, Japan, known as a historic post town gateway to the scenic Kiso Valley and the Nakasendō route.
-
B.
Nakatsugawa
Nakatsugawa is a river associated with the city of Atsugi in Kanagawa Prefecture, Japan.
-
C.
Kakegawa City
chosen
Kakegawa City is a regional city in central Japan known for its historic Kakegawa Castle and high-quality green tea production.
-
D.
Ōtsu
Ōtsu is a Japanese city on the southwestern shore of Lake Biwa, known for its historic temples, scenic lake views, and role as a transportation hub near Kyoto.
-
E.
Chigasaki
Chigasaki is a coastal city in Kanagawa Prefecture, Japan, known for its beaches, surfing culture, and relaxed Shōnan seaside atmosphere.
- 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_69d8e522a17c819095165d4d24939fd8 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65a17fb2c8190b3aaae88e741648a |
completed | April 20, 2026, 4:53 p.m. |
Created at: April 10, 2026, 1:53 p.m.