Triple
T18180335
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Fetterman |
E435262
|
entity |
| Predicate | hasMedicalEvent |
P71300
|
FINISHED |
| Object | ischemic stroke in 2022 |
—
|
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: ischemic stroke in 2022 | Statement: [John Fetterman, hasMedicalEvent, ischemic stroke in 2022]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMedicalEvent Context triple: [John Fetterman, hasMedicalEvent, ischemic stroke in 2022]
-
A.
medicalEvent
chosen
Indicates that a specific health-related occurrence or clinical incident has taken place involving one or more entities.
-
B.
hadCondition
Indicates that an entity experienced or was diagnosed with a particular medical or health-related condition.
-
C.
hasMedicalCenter
Indicates that an entity possesses, hosts, or is associated with a medical center facility.
-
D.
hasHistoryOf
Indicates that an entity has a documented prior occurrence or background of a specified condition, event, or state.
-
E.
hasPatient
Indicates that an action, event, or process involves a specific entity as the one undergoing or receiving its effects (the patient).
- 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_69d8b90c7ec081909b4694ccecb449c6 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4dffa75a081908dad0dcbd736172d |
completed | April 19, 2026, 2 p.m. |
| PD | Predicate disambiguation | batch_69e4331e92408190ad607ba4956a3897 |
completed | April 19, 2026, 1:42 a.m. |
Created at: April 10, 2026, 10:31 a.m.