Triple
T701632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2014–2016 West Africa Ebola outbreak |
E14009
|
entity |
| Predicate | healthcareWorkerInfectionsApproximate |
P18431
|
FINISHED |
| Object | 800+ |
—
|
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: 800+ | Statement: [2014–2016 West Africa Ebola outbreak, healthcareWorkerInfectionsApproximate, 800+]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: healthcareWorkerInfectionsApproximate Context triple: [2014–2016 West Africa Ebola outbreak, healthcareWorkerInfectionsApproximate, 800+]
-
A.
infectsTissue
Indicates that one entity (typically a pathogen or agent) invades and establishes itself within the tissue of another entity.
-
B.
healthcareType
Indicates the category or kind of healthcare service, system, or coverage associated with an entity.
-
C.
hasMedicalCenter
Indicates that an entity possesses, hosts, or is associated with a medical center facility.
-
D.
numberOfSpecialWards
Indicates the count of wards that are designated as special within a given context or entity.
-
E.
pathogenicityToHumans
Indicates that an entity has the capacity to cause disease or harmful health effects in humans.
- 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_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. |
| PDg | Predicate description generation | batch_69a4a54235548190b46218ea18f77341 |
completed | March 1, 2026, 8:44 p.m. |
Created at: March 1, 2026, 7:36 p.m.