Triple
T11074641
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruxience |
E261831
|
entity |
| Predicate | hasPharmacologicalTargetCell |
P37016
|
FINISHED |
| Object | CD20-positive B lymphocytes |
—
|
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: CD20-positive B lymphocytes | Statement: [Ruxience, hasPharmacologicalTargetCell, CD20-positive B lymphocytes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPharmacologicalTargetCell Context triple: [Ruxience, hasPharmacologicalTargetCell, CD20-positive B lymphocytes]
-
A.
hasMolecularTarget
chosen
Indicates that one entity (such as a drug or compound) is directed toward, binds to, or specifically interacts with a particular molecular target (such as a protein, receptor, or gene).
-
B.
hasPharmacologicalEffect
Indicates that one entity produces a specific pharmacological effect or action on another entity.
-
C.
targetCellEntryReceptor
Indicates that a receptor mediates or enables the entry of a target cell (or agent) into another cell.
-
D.
regulatoryTarget
Indicates that one entity is subject to control, influence, or governance by another entity under a regulatory or rule-based framework.
-
E.
hasDrugBankID
Indicates that an entity is associated with a specific identifier from the DrugBank database.
- 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_69d6aa9983c08190b0ef61603b69feac |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7994efb608190a81bc8c4d16ddbd0 |
completed | April 9, 2026, 12:19 p.m. |
| PD | Predicate disambiguation | batch_69d74415403c81909778bcd829e8832e |
completed | April 9, 2026, 6:15 a.m. |
Created at: April 8, 2026, 9:26 p.m.