Triple
T17593547
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bose family |
E428505
|
entity |
| Predicate | hasNotableMember |
P304
|
FINISHED |
| Object | Sugata Bose |
—
|
NE NERFINISHED |
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: Sugata Bose | Statement: [Bose family, hasNotableMember, Sugata Bose]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sugata Bose Context triple: [Bose family, hasNotableMember, Sugata Bose]
-
A.
Sugata Bose
chosen
Sugata Bose is an Indian historian and politician known for his scholarship on modern South Asian history and his tenure as a member of the Indian Parliament.
-
B.
Premangshu Bose
Premangshu Bose is an actor known for his role in the Indian film "Nayak."
-
C.
Kumar Bose
Kumar Bose is a renowned Indian tabla maestro known for his virtuosity and contributions to Hindustani classical music.
-
D.
Basu Bhattacharya
Basu Bhattacharya was an influential Indian filmmaker known for his introspective, realist films that helped shape the parallel cinema movement, particularly through nuanced explorations of middle-class marriage and relationships.
-
E.
Bibhuti Bhushan Bose
Bibhuti Bhushan Bose was an Indian physicist known for his contributions to theoretical physics and his role in advancing scientific research and education in India.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889e1030481909950e140c63255b9 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e469e89acc81908e52138ad4f452c6 |
completed | April 19, 2026, 5:36 a.m. |
Created at: April 10, 2026, 5:51 a.m.