Triple
T972807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alzheimer's disease |
E20980
|
entity |
| Predicate | typicallyAffectsBrainRegion |
P1586
|
FINISHED |
| Object | hippocampus |
—
|
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: hippocampus | Statement: [Alzheimer's disease, typicallyAffectsBrainRegion, hippocampus]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicallyAffectsBrainRegion Context triple: [Alzheimer's disease, typicallyAffectsBrainRegion, hippocampus]
-
A.
coreRegionOf
Indicates that one entity constitutes the central or most essential part of another entity’s structure, area, or domain.
-
B.
hasBodyRegion
Indicates that an entity possesses, includes, or is associated with a specific anatomical or bodily region.
-
C.
affectedArea
chosen
Indicates the specific region or extent over which an event, condition, or influence has an impact.
-
D.
hasRegion
Indicates that an entity includes, contains, or is associated with a specific geographic or administrative region as part of its scope or structure.
-
E.
influencesRegion
Indicates that one entity has an effect on, shapes, or alters the conditions, characteristics, or behavior of a specified region.
- 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_69a493b33d2c81909c52c369d3ca8436 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b44c38f08190997e141d424e9e04 |
completed | March 1, 2026, 9:49 p.m. |
| PD | Predicate disambiguation | batch_69a4b2a6aa2c8190aebba71320ab678f |
completed | March 1, 2026, 9:41 p.m. |
Created at: March 1, 2026, 7:40 p.m.