Triple
T5981455
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Donepezil |
E133125
|
entity |
| Predicate | proteinBinding |
P37224
|
FINISHED |
| Object | high |
—
|
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: high | Statement: [Donepezil, proteinBinding, high]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: proteinBinding Context triple: [Donepezil, proteinBinding, high]
-
A.
hasProteinBinding
chosen
Indicates that one entity is capable of physically binding to or interacting specifically with a protein.
-
B.
regulatoryInteraction
Indicates a relationship where one entity modulates, controls, or influences the activity, expression, or function of another entity through regulatory mechanisms.
-
C.
binding
Indicates that one entity physically or chemically attaches, adheres, or forms a stable association with another entity.
-
D.
proteinContent
Indicates the amount or proportion of protein present in a given entity or substance.
-
E.
typicalProtein
Indicates that one entity is a representative or characteristic example of a particular protein type or class.
- 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_69c0086f45e8819098f73dd16d45ec9d |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04a6921b081908a6f6323d5c7a062 |
completed | March 22, 2026, 8 p.m. |
| PD | Predicate disambiguation | batch_69c049de98648190962b14fd341c93da |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:04 p.m.