Triple
T22041585
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Extra Help program |
E544647
|
entity |
| Predicate | costSharingEffect |
P100215
|
FINISHED |
| Object | reduces or eliminates Part D plan premiums up to a benchmark amount |
—
|
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: reduces or eliminates Part D plan premiums up to a benchmark amount | Statement: [Extra Help program, costSharingEffect, reduces or eliminates Part D plan premiums up to a benchmark amount]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: costSharingEffect Context triple: [Extra Help program, costSharingEffect, reduces or eliminates Part D plan premiums up to a benchmark amount]
-
A.
costSharing
Indicates how the financial burden of a cost is divided or shared among multiple parties.
-
B.
costImpact
chosen
Indicates the effect that one entity or action has on the cost associated with another entity or process.
-
C.
costToUser
Indicates the amount of cost or expense that is borne by, charged to, or incurred by the user.
-
D.
costModelTreatment
Indicates that a particular treatment is associated with or evaluated under a specific cost model.
-
E.
costarWith
Indicates that two performers appear together as significant cast members in the same production, such as a film, television show, or stage performance.
- 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_69e11e32445c8190ab97089b48a130bb |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f12829c1c081909edc7544242c9c7a |
completed | April 28, 2026, 9:35 p.m. |
| PD | Predicate disambiguation | batch_69e6f63b0d048190b241622759aab9de |
completed | April 21, 2026, 3:59 a.m. |
Created at: April 16, 2026, 8:25 p.m.