Triple
T6969094
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Film Nagar, Hyderabad |
E161557
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Tollywood |
E31774
|
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: Tollywood | Statement: [Film Nagar, Hyderabad, associatedWith, Tollywood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tollywood Context triple: [Film Nagar, Hyderabad, associatedWith, Tollywood]
-
A.
Tollywood
Tollywood is the Bengali-language film industry based primarily in Kolkata, India, known for its rich artistic and literary cinematic tradition.
-
B.
Pollywood
Pollywood is the regional film industry based in the Indian state of Punjab, producing Punjabi-language movies and entertainment content.
-
C.
Tollywood film industry
chosen
The Tollywood film industry is the segment of Indian cinema that produces movies in the Telugu language, primarily based in Hyderabad.
-
D.
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.
-
E.
Lollywood
Lollywood is the Pakistani film industry based in Lahore, historically known for producing Punjabi- and Urdu-language movies.
- 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_69c68853cff881908439d488924a8283 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6db152b2081909271493a5d1469fb |
completed | March 27, 2026, 7:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c76a04a77c8190959056a68a349f6e |
completed | March 28, 2026, 5:41 a.m. |
Created at: March 27, 2026, 2:30 p.m.