Triple
T2887384
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cotoneaster |
E59536
|
entity |
| Predicate | associatedRisk |
P15871
|
FINISHED |
| Object | berries mildly toxic if ingested in quantity |
—
|
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: berries mildly toxic if ingested in quantity | Statement: [Cotoneaster, associatedRisk, berries mildly toxic if ingested in quantity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedRisk Context triple: [Cotoneaster, associatedRisk, berries mildly toxic if ingested in quantity]
-
A.
riskType
chosen
Indicates the category or nature of risk associated with an entity, event, or relationship.
-
B.
riskAddressed
Indicates that a particular risk has been identified and is being mitigated, managed, or otherwise handled by an associated action, control, or measure.
-
C.
riskBasis
Indicates the underlying factor, condition, or rationale that forms the basis for assessing or assigning risk in a given context.
-
D.
riskLevel
Indicates the degree of potential harm, loss, or adverse outcome associated with a particular situation, action, or entity.
-
E.
riskFeature
Indicates that one entity possesses or exhibits a characteristic, condition, or attribute that increases the likelihood or severity of a negative outcome for another entity or situation.
- 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_69ab4ac739188190a112f42a5a69c951 |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abe047aa7c8190a0ed570c13f3a1a2 |
completed | March 7, 2026, 8:22 a.m. |
| PD | Predicate disambiguation | batch_69abdd15cbf08190bf7fea5ea516848a |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:03 p.m.