Triple
T10778041
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Goodluck Jonathan administration in Nigeria |
E254245
|
entity |
| Predicate | majorChallenge |
P6371
|
FINISHED |
| Object | corruption allegations |
—
|
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: corruption allegations | Statement: [Goodluck Jonathan administration in Nigeria, majorChallenge, corruption allegations]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: majorChallenge Context triple: [Goodluck Jonathan administration in Nigeria, majorChallenge, corruption allegations]
-
A.
majorIssue
chosen
Indicates that something is a primary or most significant problem, concern, or obstacle in a given context.
-
B.
challengeType
Indicates the specific category or kind of challenge associated with an action or relationship between entities.
-
C.
currentChallenges
Indicates the difficulties or obstacles an entity is presently facing or dealing with.
-
D.
major
Indicates that one entity is the primary field of academic specialization or main area of study for another entity.
-
E.
majorFor
Indicates that an academic program, field of study, or specialization is the primary major associated with a particular student or degree.
- 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_69d6aa609f008190a294200aefcb7bd5 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d732c18c3c819089d49e3e4585049e |
completed | April 9, 2026, 5:01 a.m. |
| PD | Predicate disambiguation | batch_69d6f31455648190b5c24690487b1b54 |
completed | April 9, 2026, 12:30 a.m. |
Created at: April 8, 2026, 9:16 p.m.