Triple
T29233857
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chancellor of the University of Nairobi |
E741141
|
entity |
| Predicate | isAcademicOfficeIn |
P166772
|
FINISHED |
| Object | Kenya |
—
|
NE NERFINISHED |
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: Kenya | Statement: [Chancellor of the University of Nairobi, isAcademicOfficeIn, Kenya]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isAcademicOfficeIn Context triple: [Chancellor of the University of Nairobi, isAcademicOfficeIn, Kenya]
-
A.
hasAcademicOffice
Indicates that an entity maintains or occupies an official academic office or workspace associated with an educational or research institution.
-
B.
hasAcademicFunction
Indicates that an entity serves a specific academic role, duty, or function within an educational or scholarly context.
-
C.
hasAcademicFunctionFor
Indicates that one entity performs or fulfills an academic role, duty, or function on behalf of or in relation to another entity.
-
D.
isProfessorshipIn
Indicates that a professorship position is associated with or belongs to a specific academic field, department, or institution.
-
E.
hasAcademicStaff
Indicates that an institution or organization employs or is associated with one or more academic staff members.
- 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_69f0911dd6fc819097d1abb287016489 |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69f6646167dc819085194ef9f5d96d23 |
completed | May 2, 2026, 8:53 p.m. |
| PD | Predicate disambiguation | batch_69f660f2e3708190ab658652bcfc04d0 |
completed | May 2, 2026, 8:39 p.m. |
| PDg | Predicate description generation | batch_69f661e9cb308190a56e25dc17df248e |
completed | May 2, 2026, 8:43 p.m. |
Created at: April 28, 2026, 12:28 p.m.