Triple
T29716329
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 阿賀野川 |
E751918
|
entity |
| Predicate | 流域面積が大きい |
P28955
|
FINISHED |
| Object | 日本有数 |
—
|
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: 日本有数 | Statement: [阿賀野川, 流域面積が大きい, 日本有数]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 流域面積が大きい Context triple: [阿賀野川, 流域面積が大きい, 日本有数]
-
A.
hasLargeArea
Indicates that an entity occupies or covers a spatial region whose size exceeds a specified large-area threshold.
-
B.
hasTailwaterArea
Indicates that a water control structure (such as a dam or weir) is associated with a downstream tailwater area where water flows out and levels are influenced by the structure’s discharge.
-
C.
drainageAreaApprox
chosen
Indicates that one entity has an approximate drainage area measured or characterized by the other entity.
-
D.
hasLargestAreaOf
Indicates that the subject entity possesses the greatest area (size of surface or region) compared to the other entities in the specified set or context.
-
E.
isMajorDeepWaterAreaOf
Indicates that one area constitutes a primary or significant deep-water region of another area or entity.
- 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_69f0d628c00c8190ab5ee7e423d7ec3c |
completed | April 28, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f672dcd4b88190828b19990dfe6ed9 |
completed | May 2, 2026, 9:55 p.m. |
| PD | Predicate disambiguation | batch_69f6659f246081909821c5f452d14e8f |
completed | May 2, 2026, 8:59 p.m. |
Created at: April 28, 2026, 7:34 p.m.