Triple
T23539716
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hita onsen ryokan |
E577705
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Hita |
—
|
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: Hita | Statement: [Hita onsen ryokan, locatedIn, Hita]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hita Context triple: [Hita onsen ryokan, locatedIn, Hita]
-
A.
Hita
chosen
Hita is a historic city in Ōita Prefecture on Japan’s Kyushu island, known for its preserved traditional townscape, riverside setting, and summer festivals.
-
B.
Hita
Hita is a historic town in the province of Guadalajara, Spain, known for its medieval architecture and literary associations.
-
C.
Itagi
Itagi is a historic town in Karnataka, India, renowned for its exquisitely carved Mahadeva Temple, a masterpiece of Western Chalukya architecture.
-
D.
Hamada
Hamada is the surname of Hiro Hamada, the young robotics prodigy and main protagonist of Disney's animated film "Big Hero 6."
-
E.
Hamada
Hamada is a coastal city in Shimane Prefecture, Japan, known for its fishing industry, beaches, and role as a regional transport hub.
- 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_69e245f9d5d08190a4a20004e1784e20 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f1ae1a66b88190811b38523ea606fe |
completed | April 29, 2026, 7:07 a.m. |
Created at: April 17, 2026, 6:10 p.m.