Triple
T16428769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oniishibozu Jigoku |
E399014
|
entity |
| Predicate | hasNearbyAttraction |
P2064
|
FINISHED |
| Object | Umi Jigoku |
E398392
|
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: Umi Jigoku | Statement: [Oniishibozu Jigoku, hasNearbyAttraction, Umi Jigoku]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Umi Jigoku Context triple: [Oniishibozu Jigoku, hasNearbyAttraction, Umi Jigoku]
-
A.
Umi Jigoku
chosen
Umi Jigoku is one of Beppu’s famous “hell” hot springs in Japan, known for its striking cobalt-blue boiling water and scenic, geothermal landscape.
-
B.
Yama Jigoku
Yama Jigoku is one of Beppu’s famous “hell” hot spring sites, known for its boiling, vividly colored pools and dramatic geothermal scenery.
-
C.
Oniishibozu Jigoku
Oniishibozu Jigoku is one of Beppu’s famous “hell” hot spring sites, known for its bubbling, mud-like pools that resemble the shaved heads of Buddhist monks.
-
D.
Tatsumaki Jigoku
Tatsumaki Jigoku is one of Beppu’s famous “hell” hot spring attractions, known for its regularly erupting geyser.
-
E.
Naraka
Naraka is the concept of an underworld or hell in several Indian religions, where souls undergo punishment or purification after death.
- 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_6a00458331748190a4bd1c5d2d466e6d |
completed | May 10, 2026, 8:44 a.m. |
Created at: April 10, 2026, 5:09 a.m.