Triple
T7772440
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Razzak |
E179102
|
entity |
| Predicate | workedIn |
P1527
|
FINISHED |
| Object | Dhallywood |
E541778
|
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: Dhallywood | Statement: [Razzak, workedIn, Dhallywood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dhallywood Context triple: [Razzak, workedIn, Dhallywood]
-
A.
Dhallywood
chosen
Dhallywood is the Bangladeshi Bengali-language film industry centered in Dhaka, known for producing mainstream commercial cinema.
-
B.
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.
-
C.
Lollywood
Lollywood is the Pakistani film industry based in Lahore, historically known for producing Punjabi- and Urdu-language movies.
-
D.
Kollywood
Kollywood is the Tamil-language film industry based in Chennai, India, known for its prolific output of commercial and artistic cinema.
-
E.
Pollywood
Pollywood is the regional film industry based in the Indian state of Punjab, producing Punjabi-language movies and entertainment content.
- 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_69c69f30602c819082ab52cd4af5c592 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c7046048688190a6cbc64e82b58eca |
completed | March 27, 2026, 10:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8c7ee407881908e591d216c504b24 |
completed | March 29, 2026, 6:34 a.m. |
Created at: March 27, 2026, 4:11 p.m.