Triple
T26491065
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fellow of the American College of Physicians (FACP) designation |
E669155
|
entity |
| Predicate | professionalGroup |
P100365
|
FINISHED |
| Object | internists |
—
|
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: internists | Statement: [Fellow of the American College of Physicians (FACP) designation, professionalGroup, internists]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionalGroup Context triple: [Fellow of the American College of Physicians (FACP) designation, professionalGroup, internists]
-
A.
professionalCategory
Indicates the classification of an entity according to its professional field, role, or occupational domain.
-
B.
professionalBody
Indicates that an entity is a formal organization that represents, regulates, or supports members of a particular profession.
-
C.
professionalSector
Indicates the industry or field in which an entity conducts its professional or occupational activities.
-
D.
professionalClass
chosen
Indicates that an entity belongs to, or is categorized within, a particular professional or occupational class.
-
E.
professionalProgram
Indicates that an entity is enrolled in, associated with, or part of a specialized professional education or training program.
- 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_69eeb319007081909642b414b114b35a |
completed | April 27, 2026, 12:51 a.m. |
| NER | Named-entity recognition | batch_69f650c70d7c819093d9a0f005f7c8d5 |
completed | May 2, 2026, 7:30 p.m. |
| PD | Predicate disambiguation | batch_69f64cab1f648190a2a9460690d18a37 |
completed | May 2, 2026, 7:12 p.m. |
Created at: April 27, 2026, 1:04 a.m.