Triple
T5769332
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Social Security Trust Funds |
E127288
|
entity |
| Predicate | benefitsFinanced |
P46311
|
FINISHED |
| Object | retirement benefits |
—
|
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: retirement benefits | Statement: [Social Security Trust Funds, benefitsFinanced, retirement benefits]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: benefitsFinanced Context triple: [Social Security Trust Funds, benefitsFinanced, retirement benefits]
-
A.
benefitAdministered
Indicates that a benefit (such as aid, service, or entitlement) has been formally provided or delivered to an eligible recipient by an administering party.
-
B.
benefits
Indicates that one entity gains an advantage, improvement, or positive outcome as a result of another entity or action.
-
C.
benefitsState
Indicates that one entity provides an advantage, improvement, or positive outcome to a state or governmental entity.
-
D.
hasBenefit
Indicates that one entity provides an advantage, improvement, or positive outcome to another entity.
-
E.
benefitProgramInvolved
chosen
Indicates that a benefit program participates in, is associated with, or plays a role in the referenced situation or relationship.
- 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_69c00834f6308190851b0abeddd8ed7e |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02acb12c081908e4beee4a957f9f9 |
completed | March 22, 2026, 5:45 p.m. |
| PD | Predicate disambiguation | batch_69c021ce8d3c81909b332cb1c33a61ad |
completed | March 22, 2026, 5:07 p.m. |
Created at: March 22, 2026, 3:50 p.m.