Triple
T23030497
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Botrytis cinerea |
E573441
|
entity |
| Predicate | causesDiseaseOn |
P51727
|
FINISHED |
| Object | grape |
—
|
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: grape | Statement: [Botrytis cinerea, causesDiseaseOn, grape]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: causesDiseaseOn Context triple: [Botrytis cinerea, causesDiseaseOn, grape]
-
A.
isPathogenOf
chosen
Indicates that one entity is a disease-causing agent (pathogen) that infects or causes illness in another entity.
-
B.
hasTargetDisease
Indicates that an entity (such as a treatment, study, or intervention) is directed toward, intended to affect, or primarily concerned with a specified disease.
-
C.
diseaseType
Indicates that one entity is classified as a specific type or category of disease in relation to another entity.
-
D.
causesDiseaseInPlants
Indicates that one entity is responsible for causing a disease or pathological condition in plants.
-
E.
basedOnDisease
Indicates that something (such as a decision, classification, or action) is determined or derived on the basis of a particular disease or disease-related information.
- 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_69e245b911188190bc3d96326c847969 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18481072c81909ab2a2c87e034433 |
completed | April 29, 2026, 4:09 a.m. |
| PD | Predicate disambiguation | batch_69ef3ba004a48190885aece88efd1f52 |
completed | April 27, 2026, 10:34 a.m. |
Created at: April 17, 2026, 3:53 p.m.