Triple
T1095080
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Medicare Part D |
E24252
|
entity |
| Predicate | penalizes |
P1841
|
FINISHED |
| Object | late enrollment without creditable coverage |
—
|
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: late enrollment without creditable coverage | Statement: [Medicare Part D, penalizes, late enrollment without creditable coverage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: penalizes Context triple: [Medicare Part D, penalizes, late enrollment without creditable coverage]
-
A.
punishedBy
Indicates that an entity receives punishment administered by another entity.
-
B.
prohibits
Indicates that one entity forbids or disallows another entity from performing a specific action or being in a certain state.
-
C.
penaltyProvision
chosen
Indicates that a rule, contract, or law includes a clause specifying a punishment or sanction for non-compliance or violation.
-
D.
persecutes
Indicates that one entity persistently harasses, oppresses, or inflicts suffering on another, often in an unjust or hostile manner.
-
E.
discourages
Indicates an action or influence that deters, dissuades, or reduces the likelihood of someone performing a particular behavior or pursuing a certain outcome.
- 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_69a4940542308190ac2a0b1f730b7cfc |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4b99e92308190b8a8c499e1630672 |
completed | March 1, 2026, 10:11 p.m. |
| PD | Predicate disambiguation | batch_69a4b743175481908f3967e589717c55 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:42 p.m.