Triple
T354496
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Social Security Amendments of 1965 |
E7513
|
entity |
| Predicate | establishedProgramType |
P2192
|
FINISHED |
| Object | federal health insurance program |
—
|
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: federal health insurance program | Statement: [Social Security Amendments of 1965, establishedProgramType, federal health insurance program]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: establishedProgramType Context triple: [Social Security Amendments of 1965, establishedProgramType, federal health insurance program]
-
A.
programType
chosen
Indicates the category or kind of program to which an entity belongs or with which it is associated.
-
B.
offersProgramsIn
Indicates that an institution or provider makes educational or training programs available in a particular field, subject, or area.
-
C.
eligibilityLevel
Indicates the degree or tier of qualification an entity has for a given benefit, service, or status.
-
D.
establishedAs
Indicates that one entity is formally created, designated, or recognized in a particular role, status, or identity as another entity.
-
E.
offersProgramLevel
Indicates that an entity provides or makes available an academic or training program at a specified level (e.g., undergraduate, graduate, certificate).
- 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_69a2e7e696948190bebc966535995e45 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2eb8312f4819084dc222e665fded3 |
completed | Feb. 28, 2026, 1:20 p.m. |
| PD | Predicate disambiguation | batch_69a2e9589e7c8190b2d3af8f858c96af |
completed | Feb. 28, 2026, 1:10 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.