Triple
T14403523
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dr. John Prentice |
E357132
|
entity |
| Predicate | professionSpecialization |
P103653
|
FINISHED |
| Object | medicine |
—
|
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: medicine | Statement: [Dr. John Prentice, professionSpecialization, medicine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionSpecialization Context triple: [Dr. John Prentice, professionSpecialization, medicine]
-
A.
professionalCategory
chosen
Indicates the classification of an entity according to its professional field, role, or occupational domain.
-
B.
positionSpecialization
Indicates that one position is a more specialized or focused variant of another, broader position.
-
C.
professionalScope
Indicates the range of activities, responsibilities, or roles that fall within a person’s or organization’s recognized professional duties or expertise.
-
D.
professionServed
Indicates that an entity has performed work or provided services in a particular profession or occupational role.
-
E.
professionalBody
Indicates that an entity is a formal organization that represents, regulates, or supports members of a particular profession.
- 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_69d827927c988190ad98bb0360981783 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de90860ae481908e175decda8624d5 |
completed | April 14, 2026, 7:07 p.m. |
| PD | Predicate disambiguation | batch_69de2aa024c48190805df6a9d63deb10 |
completed | April 14, 2026, 11:53 a.m. |
Created at: April 10, 2026, 1:17 a.m.