Triple
T16428817
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shiraike Jigoku |
E399015
|
entity |
| Predicate | temperatureCategory |
P100818
|
FINISHED |
| Object | high-temperature hot spring |
—
|
LITERAL 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: high-temperature hot spring | Statement: [Shiraike Jigoku, temperatureCategory, high-temperature hot spring]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: temperatureCategory Context triple: [Shiraike Jigoku, temperatureCategory, high-temperature hot spring]
-
A.
temperatureConditions
Indicates the specific thermal or weather-related temperature state or range affecting an entity or situation.
-
B.
temperatureChange
Indicates a change in temperature between two states, times, or conditions, specifying how much and in which direction the temperature has varied.
-
C.
typicalTemperature
Indicates the usual or characteristic temperature associated with an entity under normal conditions.
-
D.
hasTemperatureCategory
chosen
Indicates that an entity is associated with a specific qualitative temperature classification (e.g., hot, cold, warm).
-
E.
temper
Indicates moderating, softening, or counterbalancing the intensity, effect, or quality of something through the influence of something else.
- F. None of above.
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. |
| PD | Predicate disambiguation | batch_69e22701d2288190bf8676050758f172 |
completed | April 17, 2026, 12:26 p.m. |
Created at: April 10, 2026, 5:09 a.m.