Triple
T10239094
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 菅 義偉 |
E243540
|
entity |
| Predicate | 在任中の主な出来事 |
P17853
|
FINISHED |
| Object | 新型コロナウイルス感染症対策 |
—
|
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: 新型コロナウイルス感染症対策 | Statement: [菅 義偉, 在任中の主な出来事, 新型コロナウイルス感染症対策]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 在任中の主な出来事 Context triple: [菅 義偉, 在任中の主な出来事, 新型コロナウイルス感染症対策]
-
A.
notableEventDuringTerm
chosen
Indicates that a significant or noteworthy event occurred during the time span of a particular term in office or period of service.
-
B.
mainEvents
Indicates that the referenced entities are the primary or most significant events within a given context, sequence, or narrative.
-
C.
significantEvent
Indicates that an event involving the entities is of notable importance or impact within a given context.
-
D.
meetsDuringPresidencyOf
Indicates that one entity meets another while a specified person is serving as president.
-
E.
functionDuringReign
Indicates that a function, role, or activity occurred or was performed during the time span of a particular ruler’s reign.
- 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_69d381b0f97c819085c9b45799a5fb7c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d23b620c8190b8a72d0eb0d16b93 |
completed | April 7, 2026, 9:45 a.m. |
| PD | Predicate disambiguation | batch_69d4d1e9798c8190b437d53d48554ba1 |
completed | April 7, 2026, 9:44 a.m. |
Created at: April 6, 2026, 11:23 a.m.