Triple
T6969656
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SIIMA Awards |
E161569
|
entity |
| Predicate | industry |
P71
|
FINISHED |
| Object |
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.
|
E637876
|
NE FINISHED |
How this triple was built (4 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: Mollywood | Statement: [SIIMA Awards, industry, Mollywood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mollywood Context triple: [SIIMA Awards, industry, Mollywood]
-
A.
Pollywood
Pollywood is the regional film industry based in the Indian state of Punjab, producing Punjabi-language movies and entertainment content.
-
B.
Kollywood
Kollywood is the Tamil-language film industry based in Chennai, India, known for its prolific output of commercial and artistic cinema.
-
C.
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.
-
D.
Lollywood
Lollywood is the Pakistani film industry based in Lahore, historically known for producing Punjabi- and Urdu-language movies.
-
E.
Tollywood
Tollywood is the Bengali-language film industry based primarily in Kolkata, India, known for its rich artistic and literary cinematic tradition.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Mollywood Triple: [SIIMA Awards, industry, Mollywood]
Generated description
Mollywood is the Malayalam-language film industry based in the Indian state of Kerala, known for its content-driven cinema and strong storytelling traditions.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mollywood Target entity description: Mollywood is the Malayalam-language film industry based in the Indian state of Kerala, known for its content-driven cinema and strong storytelling traditions.
-
A.
Pollywood
Pollywood is the regional film industry based in the Indian state of Punjab, producing Punjabi-language movies and entertainment content.
-
B.
Kollywood
Kollywood is the Tamil-language film industry based in Chennai, India, known for its prolific output of commercial and artistic cinema.
-
C.
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.
-
D.
Lollywood
Lollywood is the Pakistani film industry based in Lahore, historically known for producing Punjabi- and Urdu-language movies.
-
E.
Tollywood
Tollywood is the Bengali-language film industry based primarily in Kolkata, India, known for its rich artistic and literary cinematic tradition.
- F. None of above. chosen
Provenance (5 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_69c68853cff881908439d488924a8283 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6db1649288190a52c7dab57b3c7dc |
completed | March 27, 2026, 7:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c78833914881909aa2ef993b89c7ab |
completed | March 28, 2026, 7:50 a.m. |
| NEDg | Description generation | batch_69c789a4a38c8190aee4beecf7c75d48 |
completed | March 28, 2026, 7:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c78a11266081908dc24f62ae3fd118 |
completed | March 28, 2026, 7:58 a.m. |
Created at: March 27, 2026, 2:30 p.m.