Triple
T37061577
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 琵琶湖 |
E917336
|
entity |
| Predicate | 関連する主要都市 |
P116685
|
FINISHED |
| Object | 大津市 |
—
|
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: 大津市 | Statement: [琵琶湖, 関連する主要都市, 大津市]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 関連する主要都市 Context triple: [琵琶湖, 関連する主要都市, 大津市]
-
A.
otherMajorCity
Indicates that one city is another major city associated with or comparable in importance or status to the first city.
-
B.
mainCities
chosen
Indicates that the related entities are the primary or most important cities associated with a given region, country, or area.
-
C.
majorUrbanCenterIn
Indicates that a city or metropolitan area functions as a primary or significant urban center within the specified region or administrative unit.
-
D.
cityAssociatedWith
Indicates that there is a notable connection or relationship between a city and another entity, such as relevance, involvement, or contextual association.
-
E.
capitalCityInvolved
Indicates that a capital city participates in, is affected by, or plays a role in a specified event, process, or relationship.
- 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_69f76e95fa40819091e14681087ae5e4 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb34e5576881909394355c8ec6ddd2 |
completed | May 6, 2026, 12:32 p.m. |
| PD | Predicate disambiguation | batch_69fb2f6171e88190bf1e0ee6a644b6a9 |
completed | May 6, 2026, 12:09 p.m. |
Created at: May 3, 2026, 4:14 p.m.