Triple
T26367041
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tanaka Rie |
E660364
|
entity |
| Predicate | hasOccupationCategory |
P75042
|
FINISHED |
| Object | Japanese voice actresses |
—
|
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: Japanese voice actresses | Statement: [Tanaka Rie, hasOccupationCategory, Japanese voice actresses]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOccupationCategory Context triple: [Tanaka Rie, hasOccupationCategory, Japanese voice actresses]
-
A.
hasOccupationInWork
Indicates that an entity holds or performs a specific occupation within a particular work, project, or creative production.
-
B.
hasTypicalOccupation
Indicates that an entity commonly or characteristically works in a particular job or profession.
-
C.
hasOccupationTheme
Indicates that something (such as a work or resource) centrally involves or focuses on a particular occupation or type of work as its main theme.
-
D.
hasOccupationSector
Indicates that an entity’s occupation belongs to or is categorized within a particular economic or professional sector.
-
E.
occupationType
chosen
Indicates the specific kind or category of work, profession, or role that an entity performs or holds.
- 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_69ee8126d52c8190bc0b34337c2c9aa8 |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f6102cee1081908fc0060af9412706 |
completed | May 2, 2026, 2:54 p.m. |
| PD | Predicate disambiguation | batch_69f5f800fa9c8190aab0962669fde8ac |
completed | May 2, 2026, 1:11 p.m. |
Created at: April 26, 2026, 10:55 p.m.