Triple
T21057568
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | fingolimod |
E518759
|
entity |
| Predicate | targetsReceptor |
P142668
|
FINISHED |
| Object | S1P1 receptor |
—
|
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: S1P1 receptor | Statement: [fingolimod, targetsReceptor, S1P1 receptor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: targetsReceptor Context triple: [fingolimod, targetsReceptor, S1P1 receptor]
-
A.
targetCellEntryReceptor
Indicates that a receptor mediates or enables the entry of a target cell (or agent) into another cell.
-
B.
targetsGene
Indicates that one entity is directed toward, acts upon, or is intended to affect a specific gene.
-
C.
targetsUseCase
Indicates that one entity is aimed at or designed to address a particular use case associated with another entity.
-
D.
regulatoryTarget
Indicates that one entity is subject to control, influence, or governance by another entity under a regulatory or rule-based framework.
-
E.
receptorSystem
Indicates that one entity functions as a receptor system through which another entity receives, processes, or responds to signals or stimuli.
- 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_69e0b5053ac48190921529544959e906 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fd81434c8190aedfddf937f82322 |
completed | April 21, 2026, 4:30 a.m. |
| PD | Predicate disambiguation | batch_69e5dbf9d71881908cd85dfc37db93ca |
completed | April 20, 2026, 7:55 a.m. |
| PDg | Predicate description generation | batch_69e5e2e03d88819086f8b641656ad8b0 |
completed | April 20, 2026, 8:25 a.m. |
Created at: April 16, 2026, 2:37 p.m.