Triple
T4260402
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ephedra |
E96088
|
entity |
| Predicate | pharmacologicalEffect |
P24600
|
FINISHED |
| Object | sympathomimetic |
—
|
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: sympathomimetic | Statement: [Ephedra, pharmacologicalEffect, sympathomimetic]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pharmacologicalEffect Context triple: [Ephedra, pharmacologicalEffect, sympathomimetic]
-
A.
healthEffect
Indicates the impact or consequence that one entity has on the health or well-being of another.
-
B.
hasPharmacologicClass
chosen
Indicates that a drug or medicinal product belongs to a specific pharmacologic class based on its mechanism of action or therapeutic effect.
-
C.
foodEffect
Indicates how consuming a particular food influences or changes another entity, such as an organism, condition, or process.
-
D.
medicinalUse
Indicates that one entity is used as a treatment or remedy for a disease, condition, or health-related purpose affecting another entity.
-
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_69b3454095ac81909c2494f7ff294af1 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b34f8103b48190934a810faafa6cb7 |
completed | March 12, 2026, 11:42 p.m. |
| PD | Predicate disambiguation | batch_69b347f73e008190a908a48ef389945a |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:06 p.m.