Triple
T7729183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wernicke area |
E175206
|
entity |
| Predicate | damageAssociatedWith |
P78831
|
FINISHED |
| Object | fluent aphasia |
—
|
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: fluent aphasia | Statement: [Wernicke area, damageAssociatedWith, fluent aphasia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: damageAssociatedWith Context triple: [Wernicke area, damageAssociatedWith, fluent aphasia]
-
A.
damageTo
Indicates a relationship where one entity causes harm, loss, or deterioration to another entity.
-
B.
sufferedDamageTo
Indicates that one entity has experienced harm, loss, or deterioration affecting another entity or one of its parts.
-
C.
damagedBy
Indicates that one entity has caused harm, impairment, or deterioration to another entity.
-
D.
damageAdjusted
Indicates that the amount of damage has been modified from its original value, typically to account for mitigating or amplifying factors.
-
E.
damagedIn
Indicates that an entity has suffered harm, impairment, or destruction as a result of a specified event, process, or condition.
- 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_69c6995e912c81909a49a2657103f786 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c7074eca4c8190bd51fd1b450729e8 |
completed | March 27, 2026, 10:40 p.m. |
| PD | Predicate disambiguation | batch_69c7016a6cf88190b53bf4b958f0f302 |
completed | March 27, 2026, 10:15 p.m. |
| PDg | Predicate description generation | batch_69c7074cd1f081908d5e8951660e7271 |
completed | March 27, 2026, 10:40 p.m. |
Created at: March 27, 2026, 4:06 p.m.