Triple
T2138577
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucentis |
E46708
|
entity |
| Predicate | belongsToRegulatoryCategory |
P14058
|
FINISHED |
| Object | prescription-only medicine |
—
|
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: prescription-only medicine | Statement: [Lucentis, belongsToRegulatoryCategory, prescription-only medicine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: belongsToRegulatoryCategory Context triple: [Lucentis, belongsToRegulatoryCategory, prescription-only medicine]
-
A.
regulatoryType
chosen
Indicates the specific kind or category of regulatory control, rule, or oversight that applies in the given relationship.
-
B.
subjectToRegulation
Indicates that an entity is governed, constrained, or controlled by a specific rule, law, or regulatory framework.
-
C.
regulatedIn
Indicates that one entity’s activity, expression, or occurrence is controlled, influenced, or modulated by another entity within a specific context or system.
-
D.
regulatoryDomain
Indicates that one entity defines or governs the rules, policies, or constraints under which another entity must operate.
-
E.
hasCategoryWithin
Indicates that one category is contained within or is a subcategory of another category.
- 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_69a88a174ab48190a5db20c132e5dccf |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abbf74147c81908793c3694894f94a |
completed | March 7, 2026, 6:02 a.m. |
| PD | Predicate disambiguation | batch_69abbd96a3b0819081efbfef975e1513 |
completed | March 7, 2026, 5:54 a.m. |
Created at: March 4, 2026, 7:44 p.m.