Triple
T3911290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beppu |
E87326
|
entity |
| Predicate | hasNumberOfHotSprings |
P52851
|
FINISHED |
| Object | over 2000 hot spring sources |
—
|
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: over 2000 hot spring sources | Statement: [Beppu, hasNumberOfHotSprings, over 2000 hot spring sources]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfHotSprings Context triple: [Beppu, hasNumberOfHotSprings, over 2000 hot spring sources]
-
A.
hasHotSpring
Indicates that one entity possesses, contains, or is associated with a hot spring.
-
B.
hasNumberOfWaterfalls
Indicates the quantity of waterfalls associated with a given entity.
-
C.
hasFreshwaterSprings
Indicates that the subject contains or is associated with natural sources of freshwater emerging from the ground.
-
D.
hasSpaResort
Indicates that one entity possesses, includes, or is associated with a spa resort as an amenity or feature.
-
E.
hasNumberOfCampsites
Indicates the specific quantity of campsites associated with a given place, facility, or area.
- F. None of above. chosen
Provenance (4 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_69aed9424514819086e9c58adde6652d |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef1abe2dc81909c18aeae9b286898 |
completed | March 9, 2026, 4:13 p.m. |
| PD | Predicate disambiguation | batch_69aee75cff148190b6d5979d17fae085 |
completed | March 9, 2026, 3:29 p.m. |
| PDg | Predicate description generation | batch_69aef1aada308190821a3dfa6af170b3 |
completed | March 9, 2026, 4:13 p.m. |
Created at: March 9, 2026, 3:22 p.m.