Triple
T5693942
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tabu |
E125489
|
entity |
| Predicate | relative |
P37
|
FINISHED |
| Object |
Baba Azmi
Baba Azmi is an Indian cinematographer and member of the prominent Akhtar–Azmi film family, known for his work in Hindi cinema.
|
E544145
|
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: Baba Azmi | Statement: [Tabu, relative, Baba Azmi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Baba Azmi Context triple: [Tabu, relative, Baba Azmi]
-
A.
Zaki Azmi
Zaki Azmi is a prominent Malaysian jurist and former Chief Justice of Malaysia who later served as Chief Justice of the DIFC Courts in Dubai.
-
B.
Shabana Azmi
Shabana Azmi is a renowned Indian actress acclaimed for her powerful performances in parallel cinema and mainstream Bollywood, as well as for her longstanding social and political activism.
-
C.
Nira Benegal
Nira Benegal is the wife of acclaimed Indian film director and screenwriter Shyam Benegal.
-
D.
Asaf Ali
Asaf Ali was an Indian freedom fighter, lawyer, and politician who played a prominent role in the independence movement and later served as a diplomat for independent India.
-
E.
Aruna Batalvi
Aruna Batalvi was the wife of renowned Punjabi poet Shiv Kumar Batalvi and a significant figure in his personal life and legacy.
- 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: Baba Azmi Triple: [Tabu, relative, Baba Azmi]
Generated description
Baba Azmi is an Indian cinematographer and member of the prominent Akhtar–Azmi film family, known for his work in Hindi cinema.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Baba Azmi Target entity description: Baba Azmi is an Indian cinematographer and member of the prominent Akhtar–Azmi film family, known for his work in Hindi cinema.
-
A.
Zaki Azmi
Zaki Azmi is a prominent Malaysian jurist and former Chief Justice of Malaysia who later served as Chief Justice of the DIFC Courts in Dubai.
-
B.
Shabana Azmi
Shabana Azmi is a renowned Indian actress acclaimed for her powerful performances in parallel cinema and mainstream Bollywood, as well as for her longstanding social and political activism.
-
C.
Nira Benegal
Nira Benegal is the wife of acclaimed Indian film director and screenwriter Shyam Benegal.
-
D.
Asaf Ali
Asaf Ali was an Indian freedom fighter, lawyer, and politician who played a prominent role in the independence movement and later served as a diplomat for independent India.
-
E.
Aruna Batalvi
Aruna Batalvi was the wife of renowned Punjabi poet Shiv Kumar Batalvi and a significant figure in his personal life and legacy.
- 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_69c0082bb19c8190823a4facd3cba79b |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c023e7dbe48190850b501f223614e3 |
completed | March 22, 2026, 5:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c07dd76f008190970c3b17ec8cbfd8 |
completed | March 22, 2026, 11:40 p.m. |
| NEDg | Description generation | batch_69c08cf206188190a4e5bb2649d97be9 |
completed | March 23, 2026, 12:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c08dc4d12c8190a7a245d583ef08d4 |
completed | March 23, 2026, 12:48 a.m. |
Created at: March 22, 2026, 3:44 p.m.