Triple
T19157446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Propecia |
E468960
|
entity |
| Predicate | effectOnPSA |
P123084
|
FINISHED |
| Object | reduces serum PSA levels |
—
|
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: reduces serum PSA levels | Statement: [Propecia, effectOnPSA, reduces serum PSA levels]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: effectOnPSA Context triple: [Propecia, effectOnPSA, reduces serum PSA levels]
-
A.
effectOnUser
Indicates how an action, event, or condition influences or impacts a user.
-
B.
effectOnSystem
Indicates the influence, change, or impact that one entity, action, or condition has on the state or behavior of a system.
-
C.
hasEffectIn
chosen
Indicates that one entity produces, causes, or exerts an effect within a specified context, system, or environment.
-
D.
effectOnLens
Indicates the influence or impact that one entity has on the properties, behavior, or performance of a lens.
-
E.
measuredEffect
Indicates that an action or process has produced a specific, quantified outcome or impact on something.
- 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_69d8dd084ff48190ac0f8c46ee722629 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5eeba91a081909c04d61d6117da06 |
completed | April 20, 2026, 9:15 a.m. |
| PD | Predicate disambiguation | batch_69e4b9b83d6881908e6271c620f74100 |
completed | April 19, 2026, 11:17 a.m. |
Created at: April 10, 2026, 12:06 p.m.