Triple
T17093103
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reston ebolavirus |
E414771
|
entity |
| Predicate | pathogenicityInNonhumanPrimates |
P41034
|
FINISHED |
| Object | high |
—
|
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: high | Statement: [Reston ebolavirus, pathogenicityInNonhumanPrimates, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pathogenicityInNonhumanPrimates Context triple: [Reston ebolavirus, pathogenicityInNonhumanPrimates, high]
-
A.
pathogenicityToHumans
Indicates that an entity has the capacity to cause disease or harmful health effects in humans.
-
B.
pathogenicity
chosen
Indicates that one entity has the capacity to cause disease or harmful pathological effects in another entity.
-
C.
isPathogenOf
Indicates that one entity is a disease-causing agent (pathogen) that infects or causes illness in another entity.
-
D.
pathogenicMechanism
Indicates the specific biological process or mechanism through which an agent causes disease or pathological effects in a host.
-
E.
pathogenType
Indicates the specific kind or category of pathogen associated with or responsible for an entity or condition.
- 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_69d886cfc8e88190b05ba466edd35591 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3dbfabf548190a0d37bab3d4ef2fa |
completed | April 18, 2026, 7:31 p.m. |
| PD | Predicate disambiguation | batch_69e35d67b14481909fcdbdeaa5c34785 |
completed | April 18, 2026, 10:31 a.m. |
Created at: April 10, 2026, 5:35 a.m.