Triple
T10043115
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ethiopian film industry |
E205343
|
entity |
| Predicate | influencedBy |
P9
|
FINISHED |
| Object | Nollywood |
E9159
|
NE 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: Nollywood | Statement: [Ethiopian film industry, influencedBy, Nollywood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nollywood Context triple: [Ethiopian film industry, influencedBy, Nollywood]
-
A.
Nollywood
chosen
Nollywood is Nigeria’s prolific film industry, renowned as one of the largest movie producers in the world and a major cultural force across Africa.
-
B.
Kollywood
Kollywood is the Tamil-language film industry based in Chennai, India, known for its prolific output of commercial and artistic cinema.
-
C.
Lollywood
Lollywood is the Pakistani film industry based in Lahore, historically known for producing Punjabi- and Urdu-language movies.
-
D.
Pollywood
Pollywood is the regional film industry based in the Indian state of Punjab, producing Punjabi-language movies and entertainment content.
-
E.
Mollywood
Mollywood is the Malayalam-language film industry based in the Indian state of Kerala, known for its content-driven cinema and strong storytelling traditions.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69ca834f70e88190b2d74828b7767ec1 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cdcf60a2208190846e57b5f5649307 |
completed | April 2, 2026, 2:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d28278235c8190a5e833a9586bd3f5 |
completed | April 5, 2026, 3:40 p.m. |
Created at: March 30, 2026, 8:55 p.m.