Triple
T1103730
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leqembi |
E25439
|
entity |
| Predicate | hasBiomarkerTarget |
P2130
|
FINISHED |
| Object | amyloid-beta protofibrils |
—
|
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: amyloid-beta protofibrils | Statement: [Leqembi, hasBiomarkerTarget, amyloid-beta protofibrils]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBiomarkerTarget Context triple: [Leqembi, hasBiomarkerTarget, amyloid-beta protofibrils]
-
A.
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.
-
B.
diagnosticBiomarker
Indicates that one entity serves as a measurable biological indicator used to detect, confirm, or help diagnose a condition or disease in another entity.
-
C.
hasMarker
chosen
Indicates that one entity possesses, is associated with, or is identified by a specific marker.
-
D.
hasTarget
Indicates that one entity is directed toward, aimed at, or intended to affect another specific entity as its target.
-
E.
hasTissue
Indicates that one entity possesses, contains, or is associated with a specific tissue of another entity.
- 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_69a49428d4448190b3b36991ceae87ce |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4b9c375848190baec4d534f489616 |
completed | March 1, 2026, 10:12 p.m. |
| PD | Predicate disambiguation | batch_69a4b7472c848190b0643872f67084a2 |
completed | March 1, 2026, 10:01 p.m. |
Created at: March 1, 2026, 7:43 p.m.