Triple
T22245398
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Plan F |
E549827
|
entity |
| Predicate | pays |
P18928
|
FINISHED |
| Object | 100% of Part A coinsurance and hospital costs up to an additional 365 days after Medicare benefits are used up |
—
|
LITERAL FINISHED |
How this triple was built (1 step)
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: 100% of Part A coinsurance and hospital costs up to an additional 365 days after Medicare benefits are used up | Statement: [Plan F, pays, 100% of Part A coinsurance and hospital costs up to an additional 365 days after Medicare benefits are used up]
Provenance (2 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_69e11e41d9408190bd770cf282e22753 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f13217c9f88190aa2ce7d644b57739 |
completed | April 28, 2026, 10:18 p.m. |
Created at: April 16, 2026, 8:38 p.m.