Triple
T701606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2014–2016 West Africa Ebola outbreak |
E14009
|
entity |
| Predicate | publicHealthEmergencyOfInternationalConcern |
P18426
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [2014–2016 West Africa Ebola outbreak, publicHealthEmergencyOfInternationalConcern, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: publicHealthEmergencyOfInternationalConcern Context triple: [2014–2016 West Africa Ebola outbreak, publicHealthEmergencyOfInternationalConcern, true]
-
A.
coordinatesInternationalHealth
Indicates organizing and aligning health-related activities, policies, or responses across multiple countries or international bodies.
-
B.
covid19Impact
Indicates the effect, consequences, or influence that COVID-19 has on a given entity, condition, or situation.
-
C.
usedWorldwide
Indicates that something is utilized or applied across many countries or regions around the world.
-
D.
affectedCountry
Indicates that a particular country is impacted or influenced by an event, action, or condition.
-
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.