Triple
T9524784
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | levodopa |
E229731
|
entity |
| Predicate | drugInteraction |
P68633
|
FINISHED |
| Object | nonselective MAO 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: nonselective MAO inhibitors | Statement: [levodopa, drugInteraction, nonselective MAO inhibitors]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: drugInteraction Context triple: [levodopa, drugInteraction, nonselective MAO inhibitors]
-
A.
relatedDrug
chosen
Indicates that one drug has a specified relationship or association with another drug, such as interaction, similarity, or therapeutic linkage.
-
B.
usesDrug
Indicates that an entity consumes, administers, or otherwise makes use of a specified drug.
-
C.
evaluatesDrug
Indicates that an entity assesses or judges the properties, effectiveness, or impact of a drug.
-
D.
featuresDrug
Indicates that something (such as a product, treatment, or context) includes, involves, or prominently uses a particular drug.
-
E.
hasCommonAdverseEffect
Indicates that two or more entities share at least one adverse effect that occurs in response to them.
- 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_69ca847870a881909d8d751a7d29da39 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9899f99481908d374528716027f8 |
completed | April 1, 2026, 10:13 p.m. |
| PD | Predicate disambiguation | batch_69cca56a3d088190bdc16670678fb6c6 |
completed | April 1, 2026, 4:56 a.m. |
Created at: March 30, 2026, 7:59 p.m.