Triple
T701608
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2014–2016 West Africa Ebola outbreak |
E14009
|
entity |
| Predicate | totalCasesApproximate |
P1437
|
FINISHED |
| Object | 28600 |
—
|
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: 28600 | Statement: [2014–2016 West Africa Ebola outbreak, totalCasesApproximate, 28600]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalCasesApproximate Context triple: [2014–2016 West Africa Ebola outbreak, totalCasesApproximate, 28600]
-
A.
hasNumberOfCasesApprox
chosen
Indicates that an entity is associated with an approximate (not exact) count of cases.
-
B.
hasPopulationApproximate
Indicates that an entity has an estimated or approximate population size, rather than an exact count.
-
C.
numberOfCases
Indicates the total count of individual instances, occurrences, or records associated with a particular situation, condition, or category.
-
D.
numberOfPrisonersApproximate
Indicates an approximate count of prisoners associated with an entity or situation, rather than an exact number.
-
E.
approximateAudienceSize
Indicates an estimated number of individuals or entities that are expected to be reached or affected in a given 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_69a493494ec48190ae6751683625a9ba |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4a544e3608190ac315c7aa9f88e7e |
completed | March 1, 2026, 8:44 p.m. |
| PD | Predicate disambiguation | batch_69a4a4ec8c748190b198492a0eea4445 |
completed | March 1, 2026, 8:43 p.m. |
Created at: March 1, 2026, 7:36 p.m.