Triple
T2308999
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reforming the Unreformable: Lessons from Nigeria |
E51906
|
entity |
| Predicate | drawsLessonsFor |
P1470
|
FINISHED |
| Object | governance |
—
|
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: governance | Statement: [Reforming the Unreformable: Lessons from Nigeria, drawsLessonsFor, governance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: drawsLessonsFor Context triple: [Reforming the Unreformable: Lessons from Nigeria, drawsLessonsFor, governance]
-
A.
drawsLesson
chosen
Indicates that one entity derives or infers a lesson or conclusion from another entity or situation.
-
B.
learn
Indicates that an entity acquires knowledge, skills, or understanding from another entity, source, or experience.
-
C.
lesson
Indicates that one entity provides or conducts an instructional session or teaching activity for another entity.
-
D.
teachesAbout
Indicates that one entity provides instruction or information to another entity on a particular subject or topic.
-
E.
earnOn
Indicates that one entity gains income, profit, or returns as a result of another entity or activity.
- 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_69a88b0bb30c81908ded03b006d29387 |
completed | March 4, 2026, 7:42 p.m. |
| NER | Named-entity recognition | batch_69abce1f4f0c8190a714e4dcb8449f7e |
completed | March 7, 2026, 7:05 a.m. |
| PD | Predicate disambiguation | batch_69abc58ce2a081908ce2f0cadd92e9f8 |
completed | March 7, 2026, 6:28 a.m. |
Created at: March 4, 2026, 7:49 p.m.