Triple
T7660246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tollywood |
E173485
|
entity |
| Predicate | hasNotableDirector |
P4744
|
FINISHED |
| Object | Goutam Ghose |
E195910
|
NE FINISHED |
How this triple was built (2 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: Goutam Ghose | Statement: [Tollywood, hasNotableDirector, Goutam Ghose]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Goutam Ghose Context triple: [Tollywood, hasNotableDirector, Goutam Ghose]
-
A.
Goutam Ghose
chosen
Goutam Ghose is an acclaimed Indian filmmaker and cinematographer known for his socially conscious and visually poetic works in Bengali and parallel cinema.
-
B.
Rabi Ghosh
Rabi Ghosh was a renowned Indian Bengali actor and comedian, celebrated for his memorable character roles in classic films and his long association with director Satyajit Ray.
-
C.
Bhudev Mukhopadhyay
Bhudev Mukhopadhyay was a 19th-century Bengali writer, educator, and social thinker known for his contributions to early modern Bengali literature and intellectual life.
-
D.
Jnanesh Mukherjee
Jnanesh Mukherjee is an Indian actor known for his work in Bengali cinema and television.
-
E.
Pradip Bose
Pradip Bose is a computer engineer and researcher known for his contributions to microprocessor architecture and performance analysis, particularly at IBM.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c69955517c819085bc715b96d304d2 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c701a47a5c8190867e39f552c86787 |
completed | March 27, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c902af947081908d09cba5e4e4e434 |
completed | March 29, 2026, 10:45 a.m. |
Created at: March 27, 2026, 3:59 p.m.