Triple
T28822235
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Macky Sall |
E727800
|
entity |
| Predicate | predecessor (as President of Senegal) |
P34201
|
FINISHED |
| Object | Abdoulaye Wade |
—
|
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: Abdoulaye Wade | Statement: [Macky Sall, predecessor (as President of Senegal), Abdoulaye Wade]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: predecessor (as President of Senegal) Context triple: [Macky Sall, predecessor (as President of Senegal), Abdoulaye Wade]
-
A.
predecessorInOffice
chosen
Indicates that one officeholder directly held a particular position before another officeholder in an official succession.
-
B.
predecessorHeadOfStateRepresentative
Indicates that one entity previously served as the head-of-state representative immediately before the other entity in a given office or role.
-
C.
predecessorCEO
Indicates that one person previously held the position of CEO before another specific person.
-
D.
predecessor
Indicates that one entity comes before another in an ordered sequence or succession.
-
E.
predecessorAsFirstMinister
Indicates that one person previously held the position of First Minister immediately before another person.
- 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_69f0319d09088190bbf14cdf1987792a |
completed | April 28, 2026, 4:03 a.m. |
| NER | Named-entity recognition | batch_69f7675b12848190a3569cfda29c5b0e |
completed | May 3, 2026, 3:18 p.m. |
| PD | Predicate disambiguation | batch_69f762f4b59481909f70074f11825bfb |
completed | May 3, 2026, 3 p.m. |
Created at: April 28, 2026, 6:34 a.m.