Triple
T12778324
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yemi Osinbajo |
E305435
|
entity |
| Predicate | termInOfficeCount |
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: [Yemi Osinbajo, termInOfficeCount, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: termInOfficeCount Context triple: [Yemi Osinbajo, termInOfficeCount, 2]
-
A.
numberOfTermInOffice
Indicates the specific ordinal count of how many terms an entity has served in a particular office or position.
-
B.
termInOffice
Indicates the period during which an individual officially holds a particular office or position.
-
C.
numberOfTimesInOffice
chosen
Indicates the count of separate terms or periods an entity has held a particular office or position.
-
D.
termInOfficeContext
Indicates that one entity’s tenure or period of holding an office, role, or position is being specified or contextualized in relation to another entity or timeframe.
-
E.
termCountAsPresident
Indicates the number of terms an individual has served in the role of president.
- 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_69d7bdf2b43c819098ae5aa68e61ea58 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e595e008190b42dff3012d17d66 |
completed | April 10, 2026, 9:40 p.m. |
| PD | Predicate disambiguation | batch_69d9640ba0688190973e4e7ec8d4a8e0 |
completed | April 10, 2026, 8:56 p.m. |
Created at: April 9, 2026, 5:29 p.m.