Triple
T903509
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2018–2020 Kivu Ebola epidemic in the Democratic Republic of the Congo |
E19496
|
entity |
| Predicate | caseFatalityRate |
P20197
|
FINISHED |
| Object | about 66 percent |
—
|
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: about 66 percent | Statement: [2018–2020 Kivu Ebola epidemic in the Democratic Republic of the Congo, caseFatalityRate, about 66 percent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: caseFatalityRate Context triple: [2018–2020 Kivu Ebola epidemic in the Democratic Republic of the Congo, caseFatalityRate, about 66 percent]
-
A.
mortalityRate
Indicates the proportion of individuals in a defined population that die within a specified time period.
-
B.
fatalitiesCategory
Indicates the classification of deaths associated with an event, incident, or condition into a specific category or severity level.
-
C.
deathTollEstimate
Indicates an estimated number of deaths attributed to a particular event, cause, or period.
-
D.
deathToll
Indicates the number of deaths resulting from a particular event, situation, or cause.
-
E.
causeOfDeath
Indicates the specific factor, event, or condition that directly resulted in an entity’s death.
- 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_69a4939e889c8190ac148b3ac1a7f90b |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ad58334881908df191140b786780 |
completed | March 1, 2026, 9:19 p.m. |
| PD | Predicate disambiguation | batch_69a4aa98caec8190bbcc38320090f058 |
completed | March 1, 2026, 9:07 p.m. |
| PDg | Predicate description generation | batch_69a4ab60fea8819098ce3269181897d1 |
completed | March 1, 2026, 9:10 p.m. |
Created at: March 1, 2026, 7:39 p.m.