Triple
T455224
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Keynesian economics |
E7217
|
entity |
| Predicate | policyImplication |
P13993
|
FINISHED |
| Object | active stabilization policy |
—
|
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: active stabilization policy | Statement: [Keynesian economics, policyImplication, active stabilization policy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: policyImplication Context triple: [Keynesian economics, policyImplication, active stabilization policy]
-
A.
policyStance
Indicates the position or viewpoint an entity holds regarding a specific policy or set of policies.
-
B.
policyContext
Indicates the situational or regulatory framework within which a policy is defined, interpreted, or applied.
-
C.
policyLevel
Indicates the degree or tier of strictness, scope, or priority associated with a given policy.
-
D.
policyTool
Indicates that an entity is a tool, mechanism, or instrument used to design, implement, or enforce a policy.
-
E.
explainsPolicy
Indicates that one entity provides a clarification or detailed description of a policy to another entity.
- F. None of above. chosen
Provenance (4 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_69a2e7e5c5bc8190a1dc8178218fba40 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ef87cc7c8190a0fec933457821e2 |
completed | Feb. 28, 2026, 1:37 p.m. |
| PD | Predicate disambiguation | batch_69a2ede4de008190b5a6c159e741522e |
completed | Feb. 28, 2026, 1:30 p.m. |
| PDg | Predicate description generation | batch_69a2eeeb85f881909580a6a86332b4a5 |
completed | Feb. 28, 2026, 1:34 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.