Triple
T14847300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Victor Banerjee |
E349131
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Victor Banerjee |
E349131
|
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: Victor Banerjee | Statement: [Victor Banerjee, name, Victor Banerjee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Victor Banerjee Context triple: [Victor Banerjee, name, Victor Banerjee]
-
A.
Victor Banerjee
chosen
Victor Banerjee is an Indian actor known for his acclaimed performances in both Indian and international cinema, including major roles in films by directors such as David Lean and Satyajit Ray.
-
B.
Ash Mukherjee
Ash Mukherjee is a character in the British television drama series "It's a Sin," which explores the lives of young gay men during the 1980s AIDS crisis in London.
-
C.
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.
-
D.
Sarbajit Banerjee
Sarbajit Banerjee is a chemist known for his research in materials chemistry, particularly on phase transitions and electronic properties of transition metal oxides.
-
E.
Sanjay Banerji
Sanjay Banerji is an economist and academic recognized for his scholarly contributions associated with the Delhi School of Economics.
- 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_69d822ec69008190a9232caa68836872 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded29236dc8190b7d3a37d09f9fb21 |
completed | April 14, 2026, 11:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe6502d3f081909ff6fa8722769e2e |
completed | May 8, 2026, 10:34 p.m. |
Created at: April 10, 2026, 1:53 a.m.