Triple
T1172200
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vumerity |
E24937
|
entity |
| Predicate | hasTherapeuticGoal |
P24604
|
FINISHED |
| Object | reduce relapse rate in multiple sclerosis |
—
|
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: reduce relapse rate in multiple sclerosis | Statement: [Vumerity, hasTherapeuticGoal, reduce relapse rate in multiple sclerosis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTherapeuticGoal Context triple: [Vumerity, hasTherapeuticGoal, reduce relapse rate in multiple sclerosis]
-
A.
hasManagementGoal
Indicates that an entity is associated with a specific management objective or target it is intended to achieve or support.
-
B.
hasPrimaryGoal
Indicates that an entity’s main or most important objective is the specified goal.
-
C.
hasPolicyGoal
Indicates that an entity is associated with, or aims to achieve, a specific policy objective or target.
-
D.
hasConservationGoal
Indicates that an entity is associated with or aims to achieve a specific conservation-related objective or target.
-
E.
hasTargetDisease
Indicates that an entity (such as a treatment, study, or intervention) is directed toward, intended to affect, or primarily concerned with a specified disease.
- F. None of above. chosen
Provenance (4 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_69a494082a7c819095004f423f294a64 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bceb3f188190b8b767380fe5986f |
completed | March 1, 2026, 10:25 p.m. |
| PD | Predicate disambiguation | batch_69a4bb5656948190b0b1d5446ad06005 |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bbd7ff1881908c943ecdfea59e81 |
completed | March 1, 2026, 10:21 p.m. |
Created at: March 1, 2026, 7:45 p.m.