Triple
T9076075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wernicke's aphasia |
E217486
|
entity |
| Predicate | hasRepetition |
P87065
|
FINISHED |
| Object | impaired |
—
|
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: impaired | Statement: [Wernicke's aphasia, hasRepetition, impaired]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRepetition Context triple: [Wernicke's aphasia, hasRepetition, impaired]
-
A.
usesRepetition
Indicates that one entity employs repeated elements, actions, or patterns as a deliberate feature or technique in relation to another entity or context.
-
B.
repetitionOf
Indicates that one entity is a repeated occurrence or instance of another entity, preserving the same content or pattern.
-
C.
repetitionPattern
Indicates a recurring structure or sequence in which an action, event, or element is repeated over time or across instances.
-
D.
repetitionCount
Indicates the number of times a particular event, action, or pattern is repeated within a given context.
-
E.
causesRepetitionOf
Indicates that one entity brings about or is responsible for the recurrence or repeated occurrence of another entity or event.
- F. None of above. chosen
Provenance (4 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_69ca83d6c14c8190bc056d927f00a2a2 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc95c53274819099b3b3047bfe8cc8 |
completed | April 1, 2026, 3:49 a.m. |
| PD | Predicate disambiguation | batch_69cc65fa79bc81908b46f05c8bba920f |
completed | April 1, 2026, 12:25 a.m. |
| PDg | Predicate description generation | batch_69cc6a3c78388190a7436acc0e44ff55 |
completed | April 1, 2026, 12:43 a.m. |
Created at: March 30, 2026, 7:12 p.m.