Triple
T35515702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ibogun campus |
E1026410
|
entity |
| Predicate | hasFaculties |
P60595
|
FINISHED |
| Object | specific faculties of Olabisi Onabanjo University |
—
|
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: specific faculties of Olabisi Onabanjo University | Statement: [Ibogun campus, hasFaculties, specific faculties of Olabisi Onabanjo University]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFaculties Context triple: [Ibogun campus, hasFaculties, specific faculties of Olabisi Onabanjo University]
-
A.
hasFaculty
Indicates that an institution or department possesses or is associated with one or more faculty members.
-
B.
hasFacultyIn
Indicates that an institution or organization has faculty members associated with or working in a particular department, field, or academic unit.
-
C.
hasUniversityFaculty
chosen
Indicates that a university or academic institution employs or is associated with one or more faculty members.
-
D.
numberOfFaculties
Indicates the total count of faculties associated with a given entity.
-
E.
hasFacultyType
Indicates that a faculty member or academic unit is associated with a specific category or type of faculty (e.g., full-time, adjunct, visiting).
- 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_69f76dfe78b081908e2b14cb88dd8c00 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69fe00dad1708190b6522476bebb43af |
completed | May 8, 2026, 3:27 p.m. |
| PD | Predicate disambiguation | batch_69fdfc3717f48190bb50ac2919c8ef95 |
completed | May 8, 2026, 3:07 p.m. |
Created at: May 3, 2026, 4:04 p.m.