Triple
T4936776
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mabalako |
E110829
|
entity |
| Predicate | epidemiologicalRisk |
P60495
|
FINISHED |
| Object | Ebola hotspot |
—
|
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 hotspot | Statement: [Mabalako, epidemiologicalRisk, Ebola hotspot]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: epidemiologicalRisk Context triple: [Mabalako, epidemiologicalRisk, Ebola hotspot]
-
A.
epidemiologicalRole
Indicates the role or function an entity plays within an epidemiological context, such as its part in the occurrence, transmission, or control of disease.
-
B.
epidemiology
Indicates the study and analysis of how diseases or health-related conditions are distributed and spread within populations, and the factors influencing these patterns.
-
C.
epidemiologicalStatus
Indicates the health-related condition or disease state of an entity within an epidemiological context, such as being infected, susceptible, recovered, or exposed.
-
D.
riskFactorTypeStudied
Indicates that a particular type of risk factor is the subject of study or analysis in a given context.
-
E.
epidemicImpact
Indicates the extent and nature of how an epidemic affects entities, such as populations, regions, or systems.
- 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_69bd4415eee08190bdce70276e56a5b4 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd7085b1dc819099408f6503f0210f |
completed | March 20, 2026, 4:06 p.m. |
| PD | Predicate disambiguation | batch_69bd6c389b9881908ad7fb1c5393c1b1 |
completed | March 20, 2026, 3:48 p.m. |
| PDg | Predicate description generation | batch_69bd6ff731188190a9903602122d4ff9 |
completed | March 20, 2026, 4:04 p.m. |
Created at: March 20, 2026, 1:30 p.m.