Triple
T37668007
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elizabeth Blackwell |
E937873
|
entity |
| Predicate | medicalDegreeFrom |
P61478
|
FINISHED |
| Object | Geneva Medical College |
—
|
NE NERFINISHED |
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: Geneva Medical College | Statement: [Elizabeth Blackwell, medicalDegreeFrom, Geneva Medical College]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: medicalDegreeFrom Context triple: [Elizabeth Blackwell, medicalDegreeFrom, Geneva Medical College]
-
A.
medicalDegree
Indicates that an individual has obtained a formal medical qualification or degree from an accredited institution.
-
B.
medicalQualificationFrom
chosen
Indicates that a person or medical professional obtained their medical qualification or degree from a specified institution or source.
-
C.
isDoctorOf
Indicates that one entity serves as the medical doctor responsible for the care or treatment of another entity.
-
D.
medicalSchool
Indicates that one entity serves as the medical school where the other entity received medical education or training.
-
E.
medicalTraining
Indicates that one entity has received or is undergoing professional education or instruction in the field of medicine from another entity or institution.
- 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_69f76ed6df7c8190b018e5baea716ceb |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fba9e27d0c81908342e28f016d221a |
completed | May 6, 2026, 8:51 p.m. |
| PD | Predicate disambiguation | batch_69fba887821c8190ae93ef1dd389e9c8 |
completed | May 6, 2026, 8:45 p.m. |
Created at: May 3, 2026, 4:18 p.m.