Triple
T1529162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bengali cinema |
E32401
|
entity |
| Predicate | hasNotableActor |
P17435
|
FINISHED |
| Object |
Uttam Kumar
Uttam Kumar was a legendary Indian actor and cultural icon, widely regarded as the greatest star of Bengali cinema.
|
E175228
|
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: Uttam Kumar | Statement: [Bengali cinema, hasNotableActor, Uttam Kumar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uttam Kumar Context triple: [Bengali cinema, hasNotableActor, Uttam Kumar]
-
A.
Suchitra Sen
Suchitra Sen was a legendary Indian film actress renowned for her powerful performances in Bengali cinema and as the first Indian actress to receive an international film award.
-
B.
Aparna Sen
Aparna Sen is an acclaimed Indian filmmaker, screenwriter, and actress known for her pioneering and nuanced work in Bengali cinema.
-
C.
Deepak Kapur
Deepak Kapur is a computer scientist known for his influential work in automated reasoning and term rewriting systems.
-
D.
Anupam Kher
Anupam Kher is an acclaimed Indian actor known for his extensive work in Hindi cinema and notable roles in international films.
-
E.
Anupam Tripathi
Anupam Tripathi is an Indian actor best known internationally for his breakout role as Ali Abdul in the South Korean Netflix series "Squid Game."
- 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: Uttam Kumar Triple: [Bengali cinema, hasNotableActor, Uttam Kumar]
Generated description
Uttam Kumar was a legendary Indian actor and cultural icon, widely regarded as the greatest star of Bengali cinema.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Uttam Kumar Target entity description: Uttam Kumar was a legendary Indian actor and cultural icon, widely regarded as the greatest star of Bengali cinema.
-
A.
Suchitra Sen
Suchitra Sen was a legendary Indian film actress renowned for her powerful performances in Bengali cinema and as the first Indian actress to receive an international film award.
-
B.
Aparna Sen
Aparna Sen is an acclaimed Indian filmmaker, screenwriter, and actress known for her pioneering and nuanced work in Bengali cinema.
-
C.
Deepak Kapur
Deepak Kapur is a computer scientist known for his influential work in automated reasoning and term rewriting systems.
-
D.
Anupam Kher
Anupam Kher is an acclaimed Indian actor known for his extensive work in Hindi cinema and notable roles in international films.
-
E.
Anupam Tripathi
Anupam Tripathi is an Indian actor best known internationally for his breakout role as Ali Abdul in the South Korean Netflix series "Squid Game."
- 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_69a885ea86308190998f6bc14bb91f8e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abb1dfd1a48190804ca5f0fb6f5985 |
completed | March 7, 2026, 5:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad309188508190840af75bfa357bfb |
completed | March 8, 2026, 8:17 a.m. |
| NEDg | Description generation | batch_69ad30ed5c80819083ee4ed2da7ad932 |
completed | March 8, 2026, 8:18 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad31664b3881908f20d8a588fe4742 |
completed | March 8, 2026, 8:20 a.m. |
Created at: March 4, 2026, 7:26 p.m.