Triple
T14717730
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Panafrican Film and Television Festival of Ouagadougou |
E345726
|
entity |
| Predicate | hasIndustryComponent |
P53279
|
FINISHED |
| Object | film market |
—
|
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: film market | Statement: [Panafrican Film and Television Festival of Ouagadougou, hasIndustryComponent, film market]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasIndustryComponent Context triple: [Panafrican Film and Television Festival of Ouagadougou, hasIndustryComponent, film market]
-
A.
containsIndustry
chosen
Indicates that one entity includes or encompasses a particular industry within its scope, structure, or operations.
-
B.
isPartOfIndustry
Indicates that one entity belongs to, operates within, or is categorized under a particular industry sector.
-
C.
hasPrincipalIndustry
Indicates that an entity’s main or primary industry of operation is the specified industry.
-
D.
hasIndustrialCompany
Indicates that one entity possesses, controls, or is associated with an industrial company.
-
E.
hasIndustrialSector
Indicates that an entity is associated with, operates in, or belongs to a particular industrial sector or branch of economic 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_69d822e5911c8190ba589f957dbd9ba7 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb98688f48190b2b19ce7aa06a6db |
completed | April 14, 2026, 10:02 p.m. |
| PD | Predicate disambiguation | batch_69de657e174481909da0437556334a04 |
completed | April 14, 2026, 4:04 p.m. |
Created at: April 10, 2026, 1:29 a.m.