Triple
T12756448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adedeji |
E304871
|
entity |
| Predicate | notableInFieldOfBearer |
P106126
|
FINISHED |
| Object | Nigerian music industry |
—
|
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: Nigerian music industry | Statement: [Adedeji, notableInFieldOfBearer, Nigerian music industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableInFieldOfBearer Context triple: [Adedeji, notableInFieldOfBearer, Nigerian music industry]
-
A.
fieldOfNotableBearer
chosen
Indicates the professional or activity domain in which a notable bearer of a name, title, or identifier is recognized.
-
B.
notableBearerNationality
Indicates that the subject has a notable bearer whose nationality is the specified object.
-
C.
notableBearerFullName
Indicates that a full personal name is that of a notable or well-known bearer associated with the referenced entity.
-
D.
notableBearerDescription
Indicates that the subject has a notable bearer, with the object providing a textual description of that bearer’s significance or identity.
-
E.
hasNotableFieldOfBearers
Indicates that the entities share a significant or distinguished area of activity, expertise, or achievement associated with their bearers.
- 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_69d7bdf1fcd081909ffb0e0d6fa3a07d |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96d8b57b88190b29b8fdca415c81c |
completed | April 10, 2026, 9:37 p.m. |
| PD | Predicate disambiguation | batch_69d96406e97c8190b79081039847115c |
completed | April 10, 2026, 8:56 p.m. |
Created at: April 9, 2026, 5:27 p.m.