Triple
T6053978
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yoshiko Ikeda |
E134861
|
entity |
| Predicate | notableRoleDuringSpouseTenure |
P29717
|
FINISHED |
| Object | hosting official functions |
—
|
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: hosting official functions | Statement: [Yoshiko Ikeda, notableRoleDuringSpouseTenure, hosting official functions]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableRoleDuringSpouseTenure Context triple: [Yoshiko Ikeda, notableRoleDuringSpouseTenure, hosting official functions]
-
A.
spouseNotableFor
Indicates that a person's spouse is recognized or distinguished for a particular achievement, role, or characteristic.
-
B.
spouseLaterOffice
Indicates that one person’s spouse held a particular office or position at a later time than the person in question.
-
C.
roleDuringHusbandPresidency
chosen
Indicates the role or position a person held specifically during her husband's term as president.
-
D.
spouseNotableWorkField
Indicates that the notable work or professional field associated with a person’s spouse is being specified.
-
E.
spouseOffice
Indicates that one entity holds an office or position that is associated with, or held by, the spouse of another 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_69c00877b6d4819096b0e163728b73a3 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05708fda48190ad3d1860969ebb6a |
completed | March 22, 2026, 8:54 p.m. |
| PD | Predicate disambiguation | batch_69c049edc6f0819092ca620d9073ad26 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:09 p.m.