Triple
T10803757
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Basu |
E254908
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Sujata Basu
Sujata Basu is a notable individual associated with the surname Basu, recognized as a distinguished bearer of that name.
|
E889107
|
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: Sujata Basu | Statement: [Basu, hasNotableBearer, Sujata Basu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sujata Basu Context triple: [Basu, hasNotableBearer, Sujata Basu]
-
A.
Sujata Ray
Sujata Ray was the wife of renowned Bengali writer and satirist Sukumar Ray and a member of the prominent Ray family of Kolkata.
-
B.
Sumita Sanyal
Sumita Sanyal was an Indian film actress best known for her work in Bengali and Hindi cinema during the 1960s and 1970s, including notable roles opposite stars like Uttam Kumar and Amitabh Bachchan.
-
C.
Usha Kundu
Usha Kundu is a physician and philanthropist whose contributions to healthcare and medical education led to a medical college being named in her honor.
-
D.
Kamalini Chatterjee
Kamalini Chatterjee is the daughter of acclaimed Indian filmmaker and actress Aparna Sen.
-
E.
Ruma Bose
Ruma Bose is an entrepreneur, investor, and author known for her leadership roles in social impact ventures and global business initiatives.
- 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: Sujata Basu Triple: [Basu, hasNotableBearer, Sujata Basu]
Generated description
Sujata Basu is a notable individual associated with the surname Basu, recognized as a distinguished bearer of that name.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sujata Basu Target entity description: Sujata Basu is a notable individual associated with the surname Basu, recognized as a distinguished bearer of that name.
-
A.
Sujata Ray
Sujata Ray was the wife of renowned Bengali writer and satirist Sukumar Ray and a member of the prominent Ray family of Kolkata.
-
B.
Sumita Sanyal
Sumita Sanyal was an Indian film actress best known for her work in Bengali and Hindi cinema during the 1960s and 1970s, including notable roles opposite stars like Uttam Kumar and Amitabh Bachchan.
-
C.
Usha Kundu
Usha Kundu is a physician and philanthropist whose contributions to healthcare and medical education led to a medical college being named in her honor.
-
D.
Kamalini Chatterjee
Kamalini Chatterjee is the daughter of acclaimed Indian filmmaker and actress Aparna Sen.
-
E.
Ruma Bose
Ruma Bose is an entrepreneur, investor, and author known for her leadership roles in social impact ventures and global business initiatives.
- 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_69d6aa61c15c8190a1839550c56e75e1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d73370e7388190885b104fc883456e |
completed | April 9, 2026, 5:04 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69deb0d849888190be46616ecc97c2b1 |
completed | April 14, 2026, 9:25 p.m. |
| NEDg | Description generation | batch_69dec2534728819095b3693120772da9 |
completed | April 14, 2026, 10:40 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69dec79b1b548190a74312284f98551c |
completed | April 14, 2026, 11:02 p.m. |
Created at: April 8, 2026, 9:18 p.m.