Triple
T5677091
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abenomics |
E125111
|
entity |
| Predicate | structuralReformArea |
P28568
|
FINISHED |
| Object | labor market reform |
—
|
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: labor market reform | Statement: [Abenomics, structuralReformArea, labor market reform]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: structuralReformArea Context triple: [Abenomics, structuralReformArea, labor market reform]
-
A.
strategicArea
Indicates that an entity is designated as an area of strategic importance or priority within a broader plan, operation, or context.
-
B.
subjectToReformBy
Indicates that an entity is undergoing or designated for changes, improvements, or restructuring carried out by another entity.
-
C.
thematicArea
chosen
Indicates the subject or item is associated with, or falls under, a particular thematic area or topic of focus.
-
D.
reform
Indicates bringing about significant changes to an existing system, practice, or entity in order to improve or correct it.
-
E.
areaComponent
Indicates that one area is a constituent or sub-area that forms part of a larger area.
- 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_69c008295c808190acfe78915e7d656a |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c025303860819093e51f176babed71 |
completed | March 22, 2026, 5:21 p.m. |
| PD | Predicate disambiguation | batch_69c021bc3894819084f37d14ba4b2644 |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:43 p.m.