Triple
T29651503
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kennedy tax cut |
E750144
|
entity |
| Predicate | influencedByTheory |
P79559
|
FINISHED |
| Object | Keynesian economics |
—
|
NE NERFINISHED |
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: Keynesian economics | Statement: [Kennedy tax cut, influencedByTheory, Keynesian economics]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: influencedByTheory Context triple: [Kennedy tax cut, influencedByTheory, Keynesian economics]
-
A.
wereInfluencedBy
Indicates that one entity’s ideas, actions, or characteristics were shaped or affected by another entity.
-
B.
followsTheoryOf
chosen
Indicates that one entity adheres to, is based on, or is guided by the theoretical framework or principles established by another entity.
-
C.
theoreticalInspiration
Indicates that one entity serves as a conceptual or intellectual source of ideas, models, or frameworks that inform or shape the theory or approach of another entity.
-
D.
reflectsTheoryOf
Indicates that one entity embodies, expresses, or is based on the theoretical framework, principles, or assumptions developed by another entity.
-
E.
theorizedInField
Indicates that a theory, idea, or hypothesis was formulated or developed within a particular academic or scientific field.
- 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_69f0d6226fe881908819197c9ef9ee04 |
completed | April 28, 2026, 3:45 p.m. |
| NER | Named-entity recognition | batch_69f739a638748190808e7a2930dce16e |
completed | May 3, 2026, 12:03 p.m. |
| PD | Predicate disambiguation | batch_69f732f2dc6c8190a4e86da98cc5eb05 |
completed | May 3, 2026, 11:35 a.m. |
Created at: April 28, 2026, 6:52 p.m.