Triple
T28929280
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thomas Boni Yayi |
E733735
|
entity |
| Predicate | termCountAsPresidentOfBenin |
P49077
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Thomas Boni Yayi, termCountAsPresidentOfBenin, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: termCountAsPresidentOfBenin Context triple: [Thomas Boni Yayi, termCountAsPresidentOfBenin, 2]
-
A.
termCountAsPresident
Indicates the number of terms an individual has served in the role of president.
-
B.
numberOfTimesInOffice
chosen
Indicates the count of separate terms or periods an entity has held a particular office or position.
-
C.
numberOfTermInOffice
Indicates the specific ordinal count of how many terms an entity has served in a particular office or position.
-
D.
succeededInOfficeAsECPresidentBy
Indicates that one entity was followed in the role of European Commission President by another entity.
-
E.
timePeriodOfUseAsPresidentialOffice
Indicates the span of time during which a particular place or building was used as a presidential office.
- 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_69f05b0b49b08190b8994b339c7980f6 |
completed | April 28, 2026, 7 a.m. |
| NER | Named-entity recognition | batch_69f65b51b63c8190aa4f80f17f587aeb |
completed | May 2, 2026, 8:15 p.m. |
| PD | Predicate disambiguation | batch_69f659d02f1c8190831758ac52bb54e4 |
completed | May 2, 2026, 8:08 p.m. |
Created at: April 28, 2026, 8:26 a.m.