Triple
T480735
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nollywood |
E9159
|
entity |
| Predicate | hasSubIndustry |
P747
|
FINISHED |
| Object | Yoruba-language film 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: Yoruba-language film industry | Statement: [Nollywood, hasSubIndustry, Yoruba-language film industry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSubIndustry Context triple: [Nollywood, hasSubIndustry, Yoruba-language film industry]
-
A.
hasPrincipalIndustry
Indicates that an entity’s main or primary industry of operation is the specified industry.
-
B.
hasSubdiscipline
Indicates that one discipline includes another, more specialized field of study as a subordinate branch.
-
C.
hasBusinessDivision
Indicates that an organization includes or is composed of a specific business division as a subordinate unit.
-
D.
hasSubdivision
chosen
Indicates that one entity is divided into and contains another entity as one of its constituent parts or administrative units.
-
E.
hasSubsidiaryTitle
Indicates that an entity has an additional or secondary title formally associated with its main title.
- 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_69a2e7ff81708190b0507a24a997232c |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2f058ebe48190aaa0a829b21f75fa |
completed | Feb. 28, 2026, 1:40 p.m. |
| PD | Predicate disambiguation | batch_69a2edf321288190b5d560f75782c2cb |
completed | Feb. 28, 2026, 1:30 p.m. |
Created at: Feb. 28, 2026, 1:12 p.m.