Triple
T9524685
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lewy bodies |
E229729
|
entity |
| Predicate | proteinComponent |
P84494
|
FINISHED |
| Object | misfolded alpha-synuclein |
—
|
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: misfolded alpha-synuclein | Statement: [Lewy bodies, proteinComponent, misfolded alpha-synuclein]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: proteinComponent Context triple: [Lewy bodies, proteinComponent, misfolded alpha-synuclein]
-
A.
proteinContent
Indicates the amount or proportion of protein present in a given entity or substance.
-
B.
typicalProtein
Indicates that one entity is a representative or characteristic example of a particular protein type or class.
-
C.
hasProteinBinding
Indicates that one entity is capable of physically binding to or interacting specifically with a protein.
-
D.
cellularComponent
Indicates the relationship between a biological entity and the specific cellular location or structure in which it is physically present or functions.
-
E.
mainProtein
chosen
Indicates that one protein is the primary or central protein in relation to another entity, such as a complex, pathway, or interaction context.
- 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_69ca847870a881909d8d751a7d29da39 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9899f99481908d374528716027f8 |
completed | April 1, 2026, 10:13 p.m. |
| PD | Predicate disambiguation | batch_69cca56a3d088190bdc16670678fb6c6 |
completed | April 1, 2026, 4:56 a.m. |
Created at: March 30, 2026, 7:59 p.m.