Triple
T4907122
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mandima Health Zone |
E109937
|
entity |
| Predicate | epidemicTypeAssociated |
P58390
|
FINISHED |
| Object | Ebola virus disease |
—
|
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: Ebola virus disease | Statement: [Mandima Health Zone, epidemicTypeAssociated, Ebola virus disease]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: epidemicTypeAssociated Context triple: [Mandima Health Zone, epidemicTypeAssociated, Ebola virus disease]
-
A.
epidemicType
chosen
Indicates the classification of an epidemic according to its nature, pattern, or mode of spread.
-
B.
associatedPandemic
Indicates a relationship where something (such as an event, condition, or entity) is linked or connected to a specific pandemic.
-
C.
epidemicScale
Indicates that an event, condition, or phenomenon occurs with such widespread prevalence and rapid spread that it reaches an epidemic level in scale.
-
D.
epidemicImpact
Indicates the extent and nature of how an epidemic affects entities, such as populations, regions, or systems.
-
E.
associatedOutbreak
Indicates that one entity (such as a case, location, or event) is linked to or involved in a particular outbreak.
- 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_69bd441180708190ba42ffb44fea533a |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6e7452d481909078a0027e2a4566 |
completed | March 20, 2026, 3:57 p.m. |
| PD | Predicate disambiguation | batch_69bd6c325e188190823836d79934e9bc |
completed | March 20, 2026, 3:48 p.m. |
Created at: March 20, 2026, 1:29 p.m.