Triple
T16428787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shiraike Jigoku |
E399015
|
entity |
| Predicate | partOf |
P40
|
FINISHED |
| Object | Beppu Hells |
E803210
|
NE 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: Beppu Hells | Statement: [Shiraike Jigoku, partOf, Beppu Hells]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beppu Hells Context triple: [Shiraike Jigoku, partOf, Beppu Hells]
-
A.
Beppu Jigoku
chosen
Beppu Jigoku is a famous collection of vividly colored, steaming hot spring "hells" in Beppu, Japan, known for their dramatic geothermal landscapes and as a major sightseeing spot.
-
B.
Beppu
Beppu is a famous Japanese city on the island of Kyushu renowned for its numerous hot springs and geothermal attractions.
-
C.
Chinoike Jigoku
Chinoike Jigoku is a famous “blood pond” hot spring in Beppu, Japan, known for its striking red-colored boiling water and dramatic geothermal scenery.
-
D.
Oniyama Jigoku
Oniyama Jigoku is one of Beppu, Japan’s famous “hell” hot spring attractions, known for its intense geothermal activity and dramatic, steamy landscape.
-
E.
Shiraike Jigoku
Shiraike Jigoku is a famous “white pond hell” hot spring in Beppu, Japan, known for its milky-white, geothermally heated waters and scenic garden setting.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d87f2b9024819085c20e52de95d583 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e328fd49708190abb5065fa430eff1 |
completed | April 18, 2026, 6:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a014134601c81909f7f4a95d558e067 |
completed | May 11, 2026, 2:38 a.m. |
Created at: April 10, 2026, 5:09 a.m.