Triple
T15401149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ghosh |
E368317
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object |
Ghoshal
Ghoshal is an Indian surname, often a variant of Ghosh, commonly found among Bengali-speaking communities.
|
E1155058
|
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: Ghoshal | Statement: [Ghosh, hasVariant, Ghoshal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ghoshal Context triple: [Ghosh, hasVariant, Ghoshal]
-
A.
Abhijit Vinayak Banerjee
Abhijit Vinayak Banerjee is an Indian-American economist and Nobel laureate renowned for his experimental approach to alleviating global poverty.
-
B.
Deepak Nayar
Deepak Nayar is a film producer known for his work on independent and genre films, including the horror-comedy "Tucker & Dale vs. Evil."
-
C.
Vas Narasimhan
Vas Narasimhan is an American physician-executive known for leading major strategic and innovation-driven transformations in the global pharmaceutical industry.
-
D.
Anil Gupta
Anil Gupta is a British television writer and producer best known for his work on acclaimed comedy series such as "The Office" and "Citizen Khan."
-
E.
Kris Gopalakrishnan
Kris Gopalakrishnan is an Indian billionaire businessman and co-founder of Infosys, one of the country’s largest IT services companies.
- 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: Ghoshal Triple: [Ghosh, hasVariant, Ghoshal]
Generated description
Ghoshal is an Indian surname, often a variant of Ghosh, commonly found among Bengali-speaking communities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ghoshal Target entity description: Ghoshal is an Indian surname, often a variant of Ghosh, commonly found among Bengali-speaking communities.
-
A.
Abhijit Vinayak Banerjee
Abhijit Vinayak Banerjee is an Indian-American economist and Nobel laureate renowned for his experimental approach to alleviating global poverty.
-
B.
Deepak Nayar
Deepak Nayar is a film producer known for his work on independent and genre films, including the horror-comedy "Tucker & Dale vs. Evil."
-
C.
Vas Narasimhan
Vas Narasimhan is an American physician-executive known for leading major strategic and innovation-driven transformations in the global pharmaceutical industry.
-
D.
Anil Gupta
Anil Gupta is a British television writer and producer best known for his work on acclaimed comedy series such as "The Office" and "Citizen Khan."
-
E.
Kris Gopalakrishnan
Kris Gopalakrishnan is an Indian billionaire businessman and co-founder of Infosys, one of the country’s largest IT services companies.
- 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_69d85a16c68c819099c1b547fbc87b32 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e8d89e08190b7cae778d89fb5e1 |
completed | April 16, 2026, 1:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff13567e3481908eb6293c6af35f3a |
completed | May 9, 2026, 10:58 a.m. |
| NEDg | Description generation | batch_69ff144af00481909191a2d33874c195 |
completed | May 9, 2026, 11:02 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff15ae7c9c81909fd0894e48e5b5b1 |
completed | May 9, 2026, 11:08 a.m. |
Created at: April 10, 2026, 3:19 a.m.