Triple
T1172152
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tysabri |
E24936
|
entity |
| Predicate | belongsToRegimenType |
P24598
|
FINISHED |
| Object | disease-modifying therapy for 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: disease-modifying therapy for multiple sclerosis | Statement: [Tysabri, belongsToRegimenType, disease-modifying therapy for multiple sclerosis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: belongsToRegimenType Context triple: [Tysabri, belongsToRegimenType, disease-modifying therapy for multiple sclerosis]
-
A.
regulatoryType
Indicates the specific kind or category of regulatory control, rule, or oversight that applies in the given relationship.
-
B.
belongsToProgram
Indicates that an entity is a member of, or is associated with, a specific program.
-
C.
hasRecordType
Indicates that an entity is associated with or classified under a specific type or category of record.
-
D.
associatedWithRegime
Indicates a relationship where an entity is linked or connected to a particular regime, such as a political, governmental, or ruling system.
-
E.
regulatesWith
Indicates that one entity controls, modulates, or influences the activity, state, or behavior of another entity through some regulatory mechanism or interaction.
- 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.