Triple
T7772409
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Razzak |
E179102
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Rangbaaz
Rangbaaz is a Bangladeshi film that helped establish actor Razzak as a major star in the country’s cinema.
|
E687718
|
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: Rangbaaz | Statement: [Razzak, notableWork, Rangbaaz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rangbaaz Context triple: [Razzak, notableWork, Rangbaaz]
-
A.
Zarganar
Zarganar is a prominent Burmese comedian, actor, and dissident known for his sharp political satire and repeated imprisonments under Myanmar’s military regimes.
-
B.
Phillauri
Phillauri is a 2017 Indian romantic comedy-drama film that blends elements of fantasy and reincarnation, featuring a ghost bride entangled in a modern-day Punjabi wedding.
-
C.
Rajkahini
Rajkahini is a Bengali period drama film set against the backdrop of the 1947 Partition of Bengal, known for its ensemble cast of women and its exploration of displacement, violence, and identity.
-
D.
Badal
Badal is a Barcelona Metro station that serves the area near Camp Nou stadium in Barcelona, Spain.
-
E.
Andaz
Andaz is a luxury boutique hotel brand known for its contemporary design, locally inspired experiences, and personalized service.
- 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: Rangbaaz Triple: [Razzak, notableWork, Rangbaaz]
Generated description
Rangbaaz is a Bangladeshi film that helped establish actor Razzak as a major star in the country’s cinema.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rangbaaz Target entity description: Rangbaaz is a Bangladeshi film that helped establish actor Razzak as a major star in the country’s cinema.
-
A.
Zarganar
Zarganar is a prominent Burmese comedian, actor, and dissident known for his sharp political satire and repeated imprisonments under Myanmar’s military regimes.
-
B.
Phillauri
Phillauri is a 2017 Indian romantic comedy-drama film that blends elements of fantasy and reincarnation, featuring a ghost bride entangled in a modern-day Punjabi wedding.
-
C.
Rajkahini
Rajkahini is a Bengali period drama film set against the backdrop of the 1947 Partition of Bengal, known for its ensemble cast of women and its exploration of displacement, violence, and identity.
-
D.
Badal
Badal is a Barcelona Metro station that serves the area near Camp Nou stadium in Barcelona, Spain.
-
E.
Andaz
Andaz is a luxury boutique hotel brand known for its contemporary design, locally inspired experiences, and personalized service.
- 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_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. |
| NEDg | Description generation | batch_69c8c8b84f88819086ecd371b62e2b5b |
completed | March 29, 2026, 6:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c8c917a1308190ab2c8e70d6ed8c0e |
completed | March 29, 2026, 6:39 a.m. |
Created at: March 27, 2026, 4:11 p.m.