Triple
T32861648
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | default mode network |
E840532
|
entity |
| Predicate | abnormalIn |
P56306
|
FINISHED |
| Object | Alzheimer's disease |
—
|
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: Alzheimer's disease | Statement: [default mode network, abnormalIn, Alzheimer's disease]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: abnormalIn Context triple: [default mode network, abnormalIn, Alzheimer's disease]
-
A.
hasUnusualMorphology
Indicates that an entity possesses structural or anatomical features that deviate significantly from what is typical or expected for its kind.
-
B.
notTypically
Indicates that the referenced situation, behavior, or relationship does not usually or normally occur under standard or expected conditions.
-
C.
hasAberrationCharacteristics
chosen
Indicates that an entity exhibits traits or properties that deviate from what is considered normal, standard, or expected.
-
D.
correctsAberration
Indicates that one entity counteracts, fixes, or compensates for an error, flaw, or deviation present in another entity.
-
E.
aberrationTypeAddressed
Indicates the specific type of aberration or deviation that is being addressed or corrected by an action or process.
- 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_69f34942465c819099b3fb47f9044f58 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6ceb729fc81908ef924d90de729f1 |
completed | May 3, 2026, 4:27 a.m. |
| PD | Predicate disambiguation | batch_69f6cc177b288190904f3f23cb856d8b |
completed | May 3, 2026, 4:16 a.m. |
Created at: May 1, 2026, 1:17 a.m.