Triple
T20351750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harano Sur |
E496027
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object |
Nirmal Ghosh
Nirmal Ghosh was an Indian actor known for his roles in Bengali cinema.
|
E1470566
|
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: Nirmal Ghosh | Statement: [Harano Sur, hasCastMember, Nirmal Ghosh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nirmal Ghosh Context triple: [Harano Sur, hasCastMember, Nirmal Ghosh]
-
A.
Goutam Ghose
Goutam Ghose is an acclaimed Indian filmmaker and cinematographer known for his socially conscious and visually poetic works in Bengali and parallel cinema.
-
B.
Jnanesh Mukherjee
Jnanesh Mukherjee is an Indian actor known for his work in Bengali cinema and television.
-
C.
Maitreesh Ghatak
Maitreesh Ghatak is an Indian economist known for his work in development economics, public economics, and microeconomic theory, and for his influential academic contributions and teaching.
-
D.
Nabendu Ghosh
Nabendu Ghosh was an Indian screenwriter and author known for his influential work in classic Hindi cinema, collaborating with prominent directors like Bimal Roy.
-
E.
Upendra Kaul
Upendra Kaul is a prominent Indian cardiologist known for his contributions to interventional cardiology and cardiovascular research.
- 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: Nirmal Ghosh Triple: [Harano Sur, hasCastMember, Nirmal Ghosh]
Generated description
Nirmal Ghosh was an Indian actor known for his roles in Bengali cinema.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nirmal Ghosh Target entity description: Nirmal Ghosh was an Indian actor known for his roles in Bengali cinema.
-
A.
Goutam Ghose
Goutam Ghose is an acclaimed Indian filmmaker and cinematographer known for his socially conscious and visually poetic works in Bengali and parallel cinema.
-
B.
Jnanesh Mukherjee
Jnanesh Mukherjee is an Indian actor known for his work in Bengali cinema and television.
-
C.
Maitreesh Ghatak
Maitreesh Ghatak is an Indian economist known for his work in development economics, public economics, and microeconomic theory, and for his influential academic contributions and teaching.
-
D.
Nabendu Ghosh
Nabendu Ghosh was an Indian screenwriter and author known for his influential work in classic Hindi cinema, collaborating with prominent directors like Bimal Roy.
-
E.
Upendra Kaul
Upendra Kaul is a prominent Indian cardiologist known for his contributions to interventional cardiology and cardiovascular research.
- 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_69e0b4a3f7f48190b37f354574028ca6 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67850ace48190b19aff5780fef7e8 |
completed | April 20, 2026, 7:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0975571fdc8190a38cddffe8d578cd |
completed | May 17, 2026, 7:59 a.m. |
| NEDg | Description generation | batch_6a0976b69ee88190a99e965c77221bbc |
completed | May 17, 2026, 8:05 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a09775da35081909628417a2859655d |
completed | May 17, 2026, 8:07 a.m. |
Created at: April 16, 2026, 11:24 a.m.