Triple
T1252816
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Action and Investment to defeat Malaria |
E26914
|
entity |
| Predicate | diseaseArea |
P20980
|
FINISHED |
| Object | vector-borne diseases |
—
|
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: vector-borne diseases | Statement: [Action and Investment to defeat Malaria, diseaseArea, vector-borne diseases]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: diseaseArea Context triple: [Action and Investment to defeat Malaria, diseaseArea, vector-borne diseases]
-
A.
diseaseType
Indicates that one entity is classified as a specific type or category of disease in relation to another entity.
-
B.
hasTargetDisease
chosen
Indicates that an entity (such as a treatment, study, or intervention) is directed toward, intended to affect, or primarily concerned with a specified disease.
-
C.
diagnosedWith
Indicates that a subject has been identified, typically by a medical professional, as having a particular disease or medical condition.
-
D.
containsMedicalDistrict
Indicates that one administrative or geographic area includes a designated medical district within its boundaries.
-
E.
pathologyFeature
Indicates that one entity is a pathological characteristic, sign, or abnormal finding associated with another entity in a medical or biological context.
- 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_69a49487a9c48190ba9b05348fd1b53f |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4bf875cf48190b6781d41097ee39b |
completed | March 1, 2026, 10:36 p.m. |
| PD | Predicate disambiguation | batch_69a4bb6c977c8190a2bf3e8b67a59beb |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:47 p.m.