Triple
T19858035
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Butembo |
E477184
|
entity |
| Predicate | healthContext |
P109970
|
FINISHED |
| Object | affected by Ebola outbreaks in North Kivu region |
—
|
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: affected by Ebola outbreaks in North Kivu region | Statement: [Butembo, healthContext, affected by Ebola outbreaks in North Kivu region]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: healthContext Context triple: [Butembo, healthContext, affected by Ebola outbreaks in North Kivu region]
-
A.
healthTheme
chosen
Indicates that the subject is associated with, focuses on, or is characterized by a particular health-related topic or theme.
-
B.
healthIndicator
Indicates a measure or signal that reflects the health status or condition of an entity.
-
C.
healthRationale
Indicates the reasoning or justification behind an assessment, decision, or action related to health.
-
D.
healthProxy
Indicates that one entity is authorized to make health-related or medical decisions on behalf of another entity.
-
E.
healthEffect
Indicates the impact or consequence that one entity has on the health or well-being of another.
- 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_69d8e51e7d948190aedbcd6c30361c39 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6586dbbf0819089e7157d416aeaaf |
completed | April 20, 2026, 4:46 p.m. |
| PD | Predicate disambiguation | batch_69e537e21d2881909b1be82f02b99d40 |
completed | April 19, 2026, 8:15 p.m. |
Created at: April 10, 2026, 1:51 p.m.