Triple
T2190442
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tylenol |
E49847
|
entity |
| Predicate | drugInteractionWarning |
P23156
|
FINISHED |
| Object | alcohol increases risk of liver toxicity |
—
|
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: alcohol increases risk of liver toxicity | Statement: [Tylenol, drugInteractionWarning, alcohol increases risk of liver toxicity]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: drugInteractionWarning Context triple: [Tylenol, drugInteractionWarning, alcohol increases risk of liver toxicity]
-
A.
hasCommonAdverseEffect
Indicates that two or more entities share at least one adverse effect that occurs in response to them.
-
B.
hasNotableDrug
Indicates that an entity is associated with a drug that is considered notable or significant in some recognized context.
-
C.
hasContraindication
chosen
Indicates that one entity (such as a treatment, drug, or procedure) should not be used or performed in the presence of another entity (such as a condition, factor, or co-medication) because it may cause harm or adverse effects.
-
D.
protectedDrugClassesInclude
Indicates that the specified set of protected drug classes includes the referenced drug class or classes.
-
E.
drugClass
Indicates that one entity is classified as a particular pharmacological or therapeutic category of drugs in relation to another entity.
- 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_69a88aaba3c48190b351cab9b26989ff |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abbf9e99f08190892d34485c8f2f25 |
completed | March 7, 2026, 6:03 a.m. |
| PD | Predicate disambiguation | batch_69abbda32d1881909d1fd83a751fb21c |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:46 p.m.