Triple
T6003338
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trulicity |
E133647
|
entity |
| Predicate | dosingFrequency |
P26891
|
FINISHED |
| Object | once weekly |
—
|
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 weekly | Statement: [Trulicity, dosingFrequency, once weekly]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dosingFrequency Context triple: [Trulicity, dosingFrequency, once weekly]
-
A.
dosingInterval
chosen
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.
doseRegimen
Indicates the specific schedule, frequency, and amount with which a dose of a substance or medication is to be administered.
-
D.
hasMaintenanceDose
Indicates that an entity is associated with a specific ongoing dose used to maintain a desired therapeutic effect after initial treatment.
-
E.
serviceFrequencyType
Indicates how often a service occurs or is scheduled within a given time period.
- 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_69c00872444c8190bfaf1739dcec765c |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04f0f63148190826198281dce3713 |
completed | March 22, 2026, 8:20 p.m. |
| PD | Predicate disambiguation | batch_69c049e3316c819087ea635fa7ee8472 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:06 p.m.