Triple
T6251114
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Decision to provide ivermectin (Mectizan) free of charge for river blindness |
E140047
|
entity |
| Predicate | hasDrug |
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: [Decision to provide ivermectin (Mectizan) free of charge for river blindness, hasDrug, ivermectin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDrug Context triple: [Decision to provide ivermectin (Mectizan) free of charge for river blindness, hasDrug, ivermectin]
-
A.
usesDrug
Indicates that an entity consumes, administers, or otherwise makes use of a specified drug.
-
B.
hasNotableDrug
Indicates that an entity is associated with a drug that is considered notable or significant in some recognized context.
-
C.
hasPharmacologicalEffect
Indicates that one entity produces a specific pharmacological effect or action on another entity.
-
D.
hasFictionalSubstance
Indicates that one entity includes, contains, or involves a fictional or imaginary substance as part of its composition, setting, or narrative.
-
E.
hasAddictiveSubstance
Indicates that an entity contains or involves a substance capable of causing addiction in those who use or consume it.
- 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_69c008b4858c819095b0199114a9a87b |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c0633dde348190bbf02a943d94e3be |
completed | March 22, 2026, 9:46 p.m. |
| PD | Predicate disambiguation | batch_69c056037bf88190a0a3fe7429345d0b |
completed | March 22, 2026, 8:50 p.m. |
| PDg | Predicate description generation | batch_69c056df95ac8190bc5efe050d3af864 |
completed | March 22, 2026, 8:53 p.m. |
Created at: March 22, 2026, 4:24 p.m.