Triple
T22692910
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Evrysdi |
E561095
|
entity |
| Predicate | hasDosingBasis |
P149314
|
FINISHED |
| Object | patient body weight |
—
|
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: patient body weight | Statement: [Evrysdi, hasDosingBasis, patient body weight]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDosingBasis Context triple: [Evrysdi, hasDosingBasis, patient body weight]
-
A.
hasDosingRegimen
Indicates that an entity is associated with a specific dosing regimen, defining how and when a dose is to be administered.
-
B.
hasDefinedDailyDose
Indicates that an entity has an established standard amount intended to be taken or used per day.
-
C.
hasDoseUnit
Indicates the unit of measurement in which a specified dose or quantity of a substance is expressed.
-
D.
hasCommonStartingDose_mgPerDay
Indicates that two treatments share the same typical initial dosage, measured in milligrams per day.
-
E.
doseRegimen
Indicates the specific schedule, frequency, and amount with which a dose of a substance or medication is to be administered.
- 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_69e2454d71b48190a1f80af9f82b6fcf |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1789ba0148190891781d05ec64f3c |
completed | April 29, 2026, 3:18 a.m. |
| PD | Predicate disambiguation | batch_69ee62b2259c819091ed1387a748b9f3 |
completed | April 26, 2026, 7:08 p.m. |
| PDg | Predicate description generation | batch_69ee8843d3308190b6e22bb98ae5c3d8 |
completed | April 26, 2026, 9:48 p.m. |
Created at: April 17, 2026, 3:13 p.m.