Triple
T24026255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chicago College of Osteopathic Medicine |
E594966
|
entity |
| Predicate | professionalProgram |
P154911
|
FINISHED |
| Object | osteopathic medical program |
—
|
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: osteopathic medical program | Statement: [Chicago College of Osteopathic Medicine, professionalProgram, osteopathic medical program]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: professionalProgram Context triple: [Chicago College of Osteopathic Medicine, professionalProgram, osteopathic medical program]
-
A.
professional
Indicates that one entity has a formal, occupation-related role, service, or expertise in relation to another entity.
-
B.
professionalClass
Indicates that an entity belongs to, or is categorized within, a particular professional or occupational class.
-
C.
professionalCategory
Indicates the classification of an entity according to its professional field, role, or occupational domain.
-
D.
professionalBase
Indicates that one entity serves as the primary professional location, organization, or base of operations for another entity.
-
E.
professionalSince
Indicates the point in time when an entity began its professional activity or career in a given role or field.
- 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_69e288be2c288190a3a46006945557f7 |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1d76ada608190a9b63d07c2fa90d4 |
completed | April 29, 2026, 10:03 a.m. |
| PD | Predicate disambiguation | batch_69f17639d23c8190bed93434e2f9230a |
completed | April 29, 2026, 3:08 a.m. |
| PDg | Predicate description generation | batch_69f17c28b684819084eea522126463f8 |
completed | April 29, 2026, 3:34 a.m. |
Created at: April 17, 2026, 9:53 p.m.