Triple
T34188217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | College of Arts and Sciences (Marywood University) |
E877021
|
entity |
| Predicate | fundingTypeOfParent |
P59
|
FINISHED |
| Object | private nonprofit |
—
|
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: private nonprofit | Statement: [College of Arts and Sciences (Marywood University), fundingTypeOfParent, private nonprofit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fundingTypeOfParent Context triple: [College of Arts and Sciences (Marywood University), fundingTypeOfParent, private nonprofit]
-
A.
funderType
Indicates the category or kind of organization or individual that provides funding in the relationship.
-
B.
fundedEntityType
Indicates the type or category of entity that receives funding in the described funding relationship.
-
C.
fundedBy
Indicates that an entity receives financial support or resources from another entity.
-
D.
fundingContext
Indicates the circumstances, purpose, or conditions under which funding is provided or used in a given relationship or action.
-
E.
fundingModel
chosen
Indicates how an entity is financially supported or sustained, such as through specific revenue sources, payment structures, or funding mechanisms.
- 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_69f349af20a4819089ac24d28f2d8112 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69f7100b3ed48190b9ed439d7ec8a7fe |
completed | May 3, 2026, 9:06 a.m. |
| PD | Predicate disambiguation | batch_69f70f3c5bfc81908585f52e196dafe5 |
completed | May 3, 2026, 9:02 a.m. |
Created at: May 1, 2026, 1:55 a.m.