Triple
T30520918
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | conduction aphasia |
E776691
|
entity |
| Predicate | hasTypicalImpairment |
P72526
|
FINISHED |
| Object | repetition of words |
—
|
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: repetition of words | Statement: [conduction aphasia, hasTypicalImpairment, repetition of words]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypicalImpairment Context triple: [conduction aphasia, hasTypicalImpairment, repetition of words]
-
A.
hasImpairmentStatus
Indicates that an entity possesses a particular condition of functional limitation, disability, or impairment status.
-
B.
hasImpairmentConcerns
Indicates that one entity has concerns or issues related to an impairment affecting another entity or itself.
-
C.
hasImpairmentListing
Indicates that an entity is associated with a specific recognized category or listing of impairments.
-
D.
canImpair
Indicates that one entity has the potential or ability to weaken, damage, or reduce the normal function, quality, or effectiveness of another entity.
-
E.
hasTypicalConditions
chosen
Indicates that something is associated with conditions or circumstances that are commonly or normally present for it.
- 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_69f2249b23c4819087fa85496d92f43f |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fe08d2b2e48190ac7be6d62d4a44a3 |
completed | May 8, 2026, 4:01 p.m. |
| PD | Predicate disambiguation | batch_69fe06cd3af08190ae25de0dc0cdd573 |
completed | May 8, 2026, 3:52 p.m. |
Created at: April 29, 2026, 8:17 p.m.