Triple
T414371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United States Social Security system |
E9559
|
entity |
| Predicate | providesBenefitType |
P10121
|
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: [United States Social Security system, providesBenefitType, retirement benefits]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: providesBenefitType Context triple: [United States Social Security system, providesBenefitType, retirement benefits]
-
A.
hasBenefit
Indicates that one entity provides an advantage, improvement, or positive outcome to another entity.
-
B.
benefits
Indicates that one entity gains an advantage, improvement, or positive outcome as a result of another entity or action.
-
C.
benefitForm
chosen
Indicates that one entity is a specific form, type, or variant in which a benefit is provided or realized for another entity.
-
D.
typeOfIncentive
Indicates the specific kind or category of incentive associated with an entity or action.
-
E.
typeOfSupport
Indicates the kind or category of assistance, help, or backing provided in a given context.
- 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_69a2e80111fc8190961d5b7c6154123f |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2eebde1d881908fb212bfba9d7c67 |
completed | Feb. 28, 2026, 1:33 p.m. |
| PD | Predicate disambiguation | batch_69a2edcff4688190809d83d112ff25a5 |
completed | Feb. 28, 2026, 1:29 p.m. |
Created at: Feb. 28, 2026, 1:09 p.m.