Triple
T15426434
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ian Khama |
E369522
|
entity |
| Predicate | numberOfTermAsPresident |
P152
|
FINISHED |
| Object | 4th President of Botswana |
—
|
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: 4th President of Botswana | Statement: [Ian Khama, numberOfTermAsPresident, 4th President of Botswana]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfTermAsPresident Context triple: [Ian Khama, numberOfTermAsPresident, 4th President of Botswana]
-
A.
termCountAsPresident
chosen
Indicates the number of terms an individual has served in the role of president.
-
B.
presidentialTerm
Indicates the period of time during which an individual officially serves as president of a country or organization.
-
C.
numberOfTimesInOffice
Indicates the count of separate terms or periods an entity has held a particular office or position.
-
D.
numberOfTermInOffice
Indicates the specific ordinal count of how many terms an entity has served in a particular office or position.
-
E.
numberOfPresidents
Indicates the total count of individuals who have held the position of president for a given entity or within a specified context.
- 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_69d85a1849f48190bf898068b2806fae |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03ec1fb288190a3625b8e4f487dd1 |
completed | April 16, 2026, 1:43 a.m. |
| PD | Predicate disambiguation | batch_69ded27f45548190a6d2b1b85cb47444 |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:20 a.m.