Triple
T28813524
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 濃尾平野 |
E727577
|
entity |
| Predicate | 防災上の特徴 |
P165611
|
FINISHED |
| Object | 洪水常襲地帯であった歴史を持つ |
—
|
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: 洪水常襲地帯であった歴史を持つ | Statement: [濃尾平野, 防災上の特徴, 洪水常襲地帯であった歴史を持つ]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 防災上の特徴 Context triple: [濃尾平野, 防災上の特徴, 洪水常襲地帯であった歴史を持つ]
-
A.
disasterDepicted
Indicates that one entity visually represents or portrays a disaster involving or affecting another entity.
-
B.
causeOfDisaster
Indicates that the subject is responsible for bringing about or triggering the specified disaster.
-
C.
typeOfDisaster
Indicates that one entity is classified as a specific kind or category of disaster in relation to another entity.
-
D.
impactOfDisasters
Indicates the effects or consequences that disasters have on entities, conditions, or outcomes.
-
E.
notableEvacuation
Indicates a significant, widely recognized instance of people being moved or fleeing from a place for safety, typically due to danger or emergency conditions.
- 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_69f0319c38948190bca746ad60fd25ba |
completed | April 28, 2026, 4:03 a.m. |
| NER | Named-entity recognition | batch_69f6596b210481908af6cd555748f75b |
completed | May 2, 2026, 8:07 p.m. |
| PD | Predicate disambiguation | batch_69f65762b5e481908a30ca963dcba4be |
completed | May 2, 2026, 7:58 p.m. |
| PDg | Predicate description generation | batch_69f658ebeca4819096beb3f98f73fe31 |
completed | May 2, 2026, 8:05 p.m. |
Created at: April 28, 2026, 6:32 a.m.