Triple
T5117738
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kalunguta |
E115378
|
entity |
| Predicate | epidemicStartInArea |
P62633
|
FINISHED |
| Object | 2018 |
—
|
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: 2018 | Statement: [Kalunguta, epidemicStartInArea, 2018]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: epidemicStartInArea Context triple: [Kalunguta, epidemicStartInArea, 2018]
-
A.
epidemicScale
Indicates that an event, condition, or phenomenon occurs with such widespread prevalence and rapid spread that it reaches an epidemic level in scale.
-
B.
epidemicSpreadFrom
Indicates that an epidemic originates in one location or population and then spreads to another location or population.
-
C.
epidemicType
Indicates the classification of an epidemic according to its nature, pattern, or mode of spread.
-
D.
epidemiologicalStatus
Indicates the health-related condition or disease state of an entity within an epidemiological context, such as being infected, susceptible, recovered, or exposed.
-
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_69bd4441d1648190a54a533895041987 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7d5a23908190a24e79d1b29d6fcf |
completed | March 20, 2026, 5:01 p.m. |
| PD | Predicate disambiguation | batch_69bd77aa68b88190a50dd736a72d2901 |
completed | March 20, 2026, 4:36 p.m. |
| PDg | Predicate description generation | batch_69bd7d5906d88190b805977e5a05767a |
completed | March 20, 2026, 5:01 p.m. |
Created at: March 20, 2026, 1:41 p.m.