Triple
T30367571
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Neal Dunn |
E772460
|
entity |
| Predicate | medical specialty |
P169125
|
FINISHED |
| Object | urology |
—
|
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: urology | Statement: [Neal Dunn, medical specialty, urology]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: medical specialty Context triple: [Neal Dunn, medical specialty, urology]
-
A.
medicalPractice
Indicates a relationship where an entity engages in or carries out the professional provision of medical care or services.
-
B.
supportsMedicalSpecialty
Indicates that one entity provides resources, infrastructure, or services that enable or facilitate the practice or development of a particular medical specialty.
-
C.
isConsideredSpecialtyOf
Indicates that one field, practice, or area of expertise is regarded as a specialized branch or subset of another broader field.
-
D.
medicalBackground
Indicates that an entity has a history of prior medical conditions, treatments, or health-related experiences relevant to its current state or context.
-
E.
medicalDegree
Indicates that an individual has obtained a formal medical qualification or degree from an accredited institution.
- 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_69f2248d71408190aec0d5c2001b1cff |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f682812b90819099d2ebb8bb2953b6 |
completed | May 2, 2026, 11:02 p.m. |
| PD | Predicate disambiguation | batch_69f678d019fc8190913662cd2f87b857 |
completed | May 2, 2026, 10:21 p.m. |
| PDg | Predicate description generation | batch_69f679496c188190ba585792f987a1f4 |
completed | May 2, 2026, 10:23 p.m. |
Created at: April 29, 2026, 7:58 p.m.