Triple
T10807308
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mihir Bose |
E255001
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Mihir Bose |
E255001
|
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: Mihir Bose | Statement: [Mihir Bose, name, Mihir Bose]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mihir Bose Context triple: [Mihir Bose, name, Mihir Bose]
-
A.
Mihir Bose
chosen
Mihir Bose is a British-Indian journalist and author best known for his work as a sports writer and commentator, particularly on cricket and the politics of sport.
-
B.
Hiranmay Bose
Hiranmay Bose is an Indian sports journalist and author known for his extensive writing and commentary on cricket and football.
-
C.
Anil Chatterjee
Anil Chatterjee was an Indian actor known for his prominent roles in Bengali cinema, particularly in the films of Satyajit Ray and other leading directors of his time.
-
D.
Sanjit Bhattacharya
Sanjit Bhattacharya is a British actor known for his work in film and television and for being married to writer-comedian Meera Syal.
-
E.
Kamal Bose
Kamal Bose was a renowned Indian cinematographer celebrated for his work on classic Hindi films, particularly in collaboration with directors like Bimal Roy.
- 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_69d6aa61c15c8190a1839550c56e75e1 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d733b506488190921e6a1f4168dd9e |
completed | April 9, 2026, 5:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de8513fe0881909d6833c85aac03a8 |
completed | April 14, 2026, 6:19 p.m. |
Created at: April 8, 2026, 9:18 p.m.