Triple
T30086598
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | angular gyrus |
E764617
|
entity |
| Predicate | lesionCanAffect |
P32707
|
FINISHED |
| Object | semantic processing |
—
|
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: semantic processing | Statement: [angular gyrus, lesionCanAffect, semantic processing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lesionCanAffect Context triple: [angular gyrus, lesionCanAffect, semantic processing]
-
A.
lesionAssociatedWithSymptom
Indicates that a particular lesion is linked to, or occurs together with, a specific symptom.
-
B.
affectsAnatomicalLocation
Indicates that one entity produces an effect on, or has an impact at, a specific anatomical location.
-
C.
affectedArea
Indicates the specific region or extent over which an event, condition, or influence has an impact.
-
D.
involvedLocation
Indicates that an event, action, or relationship takes place in, or is significantly associated with, a particular location.
-
E.
canImpair
chosen
Indicates that one entity has the potential or ability to weaken, damage, or reduce the normal function, quality, or effectiveness 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_69f22473c0fc8190a926a8051b3b378b |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f67d6d35588190bf7b5ea2889ad6b8 |
completed | May 2, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69f675ff62c48190a634bbb8896973b9 |
completed | May 2, 2026, 10:09 p.m. |
Created at: April 29, 2026, 7:04 p.m.