Triple
T2523495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CBD |
E55577
|
entity |
| Predicate | possibleSideEffect |
P39645
|
FINISHED |
| Object | drowsiness |
—
|
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: drowsiness | Statement: [CBD, possibleSideEffect, drowsiness]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: possibleSideEffect Context triple: [CBD, possibleSideEffect, drowsiness]
-
A.
hasSeriousSideEffect
Indicates that an entity (such as a treatment, drug, or intervention) causes or is associated with a significant or severe adverse effect on another entity (typically a patient or biological system).
-
B.
hasCommonSideEffect
Indicates that two or more treatments, drugs, or interventions share at least one side effect in common.
-
C.
sideEffectManagement
Indicates the relationship in which an action or intervention is used to monitor, reduce, or control the side effects caused by another action, treatment, or condition.
-
D.
hasCommonAdverseEffect
Indicates that two or more entities share at least one adverse effect that occurs in response to them.
-
E.
hasConsequence
Indicates that one event, action, or condition leads to or results in another as its outcome or effect.
- 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_69ab49e4749c8190813311efd1630f1b |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd23a0a548190b44393e0f823f7a9 |
completed | March 7, 2026, 7:22 a.m. |
| PD | Predicate disambiguation | batch_69abd0c144b0819092f32a13c1d127e5 |
completed | March 7, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69abd1487e0c8190b90dcf30586ad4cd |
completed | March 7, 2026, 7:18 a.m. |
Created at: March 6, 2026, 9:46 p.m.