Triple
T5637278
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Film i Väst |
E100278
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | Trollywood |
E102492
|
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: Trollywood | Statement: [Film i Väst, alsoKnownAs, Trollywood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Trollywood Context triple: [Film i Väst, alsoKnownAs, Trollywood]
-
A.
Trollywood
chosen
Trollywood is the film industry nickname for Trollhättan, Sweden, known for its prominent movie studios and frequent use as a filming location.
-
B.
Lollywood
Lollywood is the Pakistani film industry based in Lahore, historically known for producing Punjabi- and Urdu-language movies.
-
C.
Wellywood
Wellywood is a playful nickname for Wellington, New Zealand, referencing its prominent film industry and association with director Peter Jackson.
-
D.
Tulu cinema
Tulu cinema is the regional film industry that produces movies in the Tulu language, primarily serving audiences in the coastal Karnataka region of India.
-
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_69c00824643c81909ffdb888a2d35189 |
completed | March 22, 2026, 3:17 p.m. |
| NER | Named-entity recognition | batch_69c022809f708190ab859aa446683b10 |
completed | March 22, 2026, 5:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c04d70e5d88190b869d54911bc8689 |
completed | March 22, 2026, 8:13 p.m. |
Created at: March 22, 2026, 3:41 p.m.