Triple
T10401939
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | dabigatran |
E245168
|
entity |
| Predicate | hasDrugInteraction |
P68633
|
FINISHED |
| Object | P-glycoprotein inhibitors |
—
|
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: P-glycoprotein inhibitors | Statement: [dabigatran, hasDrugInteraction, P-glycoprotein inhibitors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDrugInteraction Context triple: [dabigatran, hasDrugInteraction, P-glycoprotein inhibitors]
-
A.
hasDrug
Indicates that an entity possesses, is treated with, or is associated with a particular drug.
-
B.
usesDrug
Indicates that an entity consumes, administers, or otherwise makes use of a specified drug.
-
C.
hasNotableDrug
Indicates that an entity is associated with a drug that is considered notable or significant in some recognized context.
-
D.
relatedDrug
chosen
Indicates that one drug has a specified relationship or association with another drug, such as interaction, similarity, or therapeutic linkage.
-
E.
hasContraindication
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.
- 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_69d381be340c8190b05998703d42d224 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9e42da08190a5383df3df6d3c18 |
completed | April 7, 2026, 11:26 a.m. |
| PD | Predicate disambiguation | batch_69d4dfb438c481908dff87c47de2f069 |
completed | April 7, 2026, 10:43 a.m. |
Created at: April 6, 2026, 12:08 p.m.