Triple
T7660183
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bhojpuri cinema |
E173484
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Bhojiwood |
E173484
|
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: Bhojiwood | Statement: [Bhojpuri cinema, alsoKnownAs, Bhojiwood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bhojiwood Context triple: [Bhojpuri cinema, alsoKnownAs, Bhojiwood]
-
A.
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.
-
B.
Pollywood
Pollywood is the regional film industry based in the Indian state of Punjab, producing Punjabi-language movies and entertainment content.
-
C.
Bhojpuri cinema
chosen
Bhojpuri cinema is the film industry that produces movies in the Bhojpuri language, primarily catering to audiences in the Bhojpuri-speaking regions of India and Nepal.
-
D.
Chhattisgarhi cinema
Chhattisgarhi cinema is the regional film industry that produces movies in the Chhattisgarhi language, reflecting the culture and stories of the Indian state of Chhattisgarh.
-
E.
Nollywood
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.
- 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_69c69955517c819085bc715b96d304d2 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c701a47a5c8190867e39f552c86787 |
completed | March 27, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c89b14b6848190892a262903d78b79 |
completed | March 29, 2026, 3:23 a.m. |
Created at: March 27, 2026, 3:59 p.m.