Triple
T36417462
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Commercial Repayment Center |
E897049
|
entity |
| Predicate | typeOfOverpayment |
P203974
|
FINISHED |
| Object | conditional payments made by Medicare |
—
|
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: conditional payments made by Medicare | Statement: [Commercial Repayment Center, typeOfOverpayment, conditional payments made by Medicare]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typeOfOverpayment Context triple: [Commercial Repayment Center, typeOfOverpayment, conditional payments made by Medicare]
-
A.
typeOfPaymentAffected
Indicates that a particular type or method of payment is impacted or influenced by a specified condition, event, or action.
-
B.
penaltyTypes
Indicates the kinds or categories of penalties that are associated with or applied to an entity or action.
-
C.
ovalType
Indicates that one entity is classified as having an oval shape or belonging to an oval-shaped type.
-
D.
calculationType
Indicates the specific method, formula, or approach used to perform a calculation in the described relationship.
-
E.
payType
Indicates the method or category of payment used in a transaction or compensation arrangement.
- F. None of above. chosen
Provenance (4 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_69f76e54ce408190849acc3f7758937c |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a0301c45274819083b0dd9d335f7ee0 |
completed | May 12, 2026, 10:32 a.m. |
| PD | Predicate disambiguation | batch_6a03015c272481908a7bfe81befb1764 |
completed | May 12, 2026, 10:30 a.m. |
| PDg | Predicate description generation | batch_6a0301c3a34c81909af5de895b1ace80 |
completed | May 12, 2026, 10:32 a.m. |
Created at: May 3, 2026, 4:10 p.m.