Triple
T16851997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Biktarvy |
E409696
|
entity |
| Predicate | typicalDosingFrequency |
P125216
|
FINISHED |
| Object | once daily |
—
|
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: once daily | Statement: [Biktarvy, typicalDosingFrequency, once daily]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalDosingFrequency Context triple: [Biktarvy, typicalDosingFrequency, once daily]
-
A.
dosingInterval
Indicates the time period that should elapse between consecutive doses of a medication or treatment.
-
B.
hasDosingRegimen
Indicates that an entity is associated with a specific dosing regimen, defining how and when a dose is to be administered.
-
C.
hasMaintenanceDose
Indicates that an entity is associated with a specific ongoing dose used to maintain a desired therapeutic effect after initial treatment.
-
D.
doseRegimen
Indicates the specific schedule, frequency, and amount with which a dose of a substance or medication is to be administered.
-
E.
typicalDosageCategories
Indicates the standard dosage ranges or categories typically associated with a given treatment, substance, or medication.
- 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_69d88395e6c88190b22730f335107c14 |
completed | April 10, 2026, 4:59 a.m. |
| NER | Named-entity recognition | batch_69e3b379b8fc81908d8bc9950c7f8bad |
completed | April 18, 2026, 4:38 p.m. |
| PD | Predicate disambiguation | batch_69e32b8cbb048190878a259cc5be960e |
completed | April 18, 2026, 6:58 a.m. |
| PDg | Predicate description generation | batch_69e355722040819098830dabf207ecd6 |
completed | April 18, 2026, 9:57 a.m. |
Created at: April 10, 2026, 5:24 a.m.