Triple
T37079484
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UP System |
E917802
|
entity |
| Predicate | academicStaffCount |
P26574
|
FINISHED |
| Object | large |
—
|
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: large | Statement: [UP System, academicStaffCount, large]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: academicStaffCount Context triple: [UP System, academicStaffCount, large]
-
A.
academicStaffCountApprox
chosen
Indicates an approximate number of academic staff associated with an institution or organizational unit.
-
B.
hasAcademicStaff
Indicates that an institution or organization employs or is associated with one or more academic staff members.
-
C.
numberOfFaculties
Indicates the total count of faculties associated with a given entity.
-
D.
hasScientificStaff
Indicates that an entity employs or is associated with one or more individuals serving in scientific or research-related roles.
-
E.
academicHead
Indicates that one entity serves as the chief academic leader or head of another entity, such as a department, program, 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_69f76e9771e08190a690834e3cd20654 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb34e5576881909394355c8ec6ddd2 |
completed | May 6, 2026, 12:32 p.m. |
| PD | Predicate disambiguation | batch_69fb2f6171e88190bf1e0ee6a644b6a9 |
completed | May 6, 2026, 12:09 p.m. |
Created at: May 3, 2026, 4:14 p.m.