Triple
T161887
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Frederick Banting |
E3304
|
entity |
| Predicate | medicalSpecialty |
P466
|
FINISHED |
| Object | orthopedic surgery |
—
|
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: orthopedic surgery | Statement: [Frederick Banting, medicalSpecialty, orthopedic surgery]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: medicalSpecialty Context triple: [Frederick Banting, medicalSpecialty, orthopedic surgery]
-
A.
hasSpecialty
chosen
Indicates that an entity possesses a particular area of expertise, focus, or professional specialization.
-
B.
diagnosedWith
Indicates that a subject has been identified, typically by a medical professional, as having a particular disease or medical condition.
-
C.
isTeachingHospitalFor
Indicates that one institution serves as a clinical training site or educational facility for another, typically a medical school or health education program.
-
D.
servesAsPrimaryTeachingHospitalFor
Indicates that one institution functions as the main clinical training and teaching site for another institution, typically a medical school or academic program.
-
E.
subjectOccupation
Indicates that the subject holds or performs a particular job, profession, or role as their occupation.
- 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_69a2527757ec819090b8becb2cf1a862 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a2585877648190a2ec320182a69343 |
completed | Feb. 28, 2026, 2:52 a.m. |
| PD | Predicate disambiguation | batch_69a256623704819089d9eeefe05858ce |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:31 a.m.