Triple
T17456529
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Onchocerciasis Control Programme in West Africa |
E425042
|
entity |
| Predicate | drugUsed |
P69332
|
FINISHED |
| Object | ivermectin |
—
|
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: ivermectin | Statement: [Onchocerciasis Control Programme in West Africa, drugUsed, ivermectin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: drugUsed Context triple: [Onchocerciasis Control Programme in West Africa, drugUsed, ivermectin]
-
A.
usesDrug
Indicates that an entity consumes, administers, or otherwise makes use of a specified drug.
-
B.
hasDrug
chosen
Indicates that an entity possesses, is treated with, or is associated with a particular drug.
-
C.
drugClass
Indicates that one entity is classified as a particular pharmacological or therapeutic category of drugs in relation to another entity.
-
D.
drugProduced
Indicates that a particular drug is manufactured or generated by a specified producer or source.
-
E.
medicinalUse
Indicates that one entity is used as a treatment or remedy for a disease, condition, or health-related purpose affecting another entity.
- 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_69d889db0ba481908402409af3b37917 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e45142b08481908cdd290692d796c3 |
completed | April 19, 2026, 3:51 a.m. |
| PD | Predicate disambiguation | batch_69e3b4f0e3fc819094e466b74622c956 |
completed | April 18, 2026, 4:44 p.m. |
Created at: April 10, 2026, 5:47 a.m.