Triple
T29888855
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kapsabet Teacher Training College |
E759089
|
entity |
| Predicate | formerStudentBecame |
P130041
|
FINISHED |
| Object | President of Kenya |
—
|
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: President of Kenya | Statement: [Kapsabet Teacher Training College, formerStudentBecame, President of Kenya]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: formerStudentBecame Context triple: [Kapsabet Teacher Training College, formerStudentBecame, President of Kenya]
-
A.
formerHighSchool
Indicates that one entity previously attended or was enrolled at the other entity as their high school.
-
B.
hasAlumnusWhoBecame
chosen
Indicates that an institution has at least one alumnus who later attained or assumed a specified role, position, or status.
-
C.
previousTeacher
Indicates that one entity formerly served as a teacher or instructor of the other entity in the past.
-
D.
formerMemberSchool
Indicates that an entity was previously a member of a particular school but is no longer affiliated as a member.
-
E.
formerlyHadEducationalInstitution
Indicates that an entity once hosted or contained a particular educational institution, but that institution is no longer present or active there.
- 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_69f2245de2f48190a481404896b56254 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69feaa483fcc81909d8a46b38a8717bf |
completed | May 9, 2026, 3:30 a.m. |
| PD | Predicate disambiguation | batch_69fea8c9d45c81908ccc8619e5fefac1 |
completed | May 9, 2026, 3:23 a.m. |
Created at: April 29, 2026, 6:01 p.m.