Triple
T23030495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Botrytis cinerea |
E573441
|
entity |
| Predicate | pathogenicLifestyle |
P150726
|
FINISHED |
| Object | necrotrophic |
—
|
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: necrotrophic | Statement: [Botrytis cinerea, pathogenicLifestyle, necrotrophic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pathogenicLifestyle Context triple: [Botrytis cinerea, pathogenicLifestyle, necrotrophic]
-
A.
pathogenicity
Indicates that one entity has the capacity to cause disease or harmful pathological effects in another entity.
-
B.
pathogenicMember
Indicates that an entity is a member of a group or set that is characterized as pathogenic (capable of causing disease).
-
C.
pathogenicityToHumans
Indicates that an entity has the capacity to cause disease or harmful health effects in humans.
-
D.
becomesPathogenicUnder
Indicates that an entity transitions into a disease-causing or harmful state when exposed to a specified condition or factor.
-
E.
pathogenicMechanism
Indicates the specific biological process or mechanism through which an agent causes disease or pathological effects in a host.
- 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_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. |
| PDg | Predicate description generation | batch_69ef538b29c081908fa56ee35a1dcee7 |
completed | April 27, 2026, 12:16 p.m. |
Created at: April 17, 2026, 3:53 p.m.