Triple
T18234635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | cimetidine |
E436638
|
entity |
| Predicate | hasDrugInteractionEffect |
P103195
|
FINISHED |
| Object | increases plasma concentration of warfarin |
—
|
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: increases plasma concentration of warfarin | Statement: [cimetidine, hasDrugInteractionEffect, increases plasma concentration of warfarin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDrugInteractionEffect Context triple: [cimetidine, hasDrugInteractionEffect, increases plasma concentration of warfarin]
-
A.
hasCommonAdverseEffect
Indicates that two or more entities share at least one adverse effect that occurs in response to them.
-
B.
hasPharmacologicalEffect
Indicates that one entity produces a specific pharmacological effect or action on another entity.
-
C.
hasInteractionEffects
chosen
Indicates that one entity’s presence, action, or state alters, influences, or modifies the behavior, effect, or outcome associated with another entity.
-
D.
usesDrug
Indicates that an entity consumes, administers, or otherwise makes use of a specified drug.
-
E.
hasDrug
Indicates that an entity possesses, is treated with, or is associated with a particular drug.
- 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_69d8b9103a8081908bbb0836fef10efd |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4f4b5ce608190b6fba518256607da |
completed | April 19, 2026, 3:28 p.m. |
| PD | Predicate disambiguation | batch_69e4332336cc8190808b9c70c888ba65 |
completed | April 19, 2026, 1:42 a.m. |
Created at: April 10, 2026, 10:33 a.m.