Triple
T20351217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mrigayaa |
E496014
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object | Anil Chatterjee |
—
|
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: Anil Chatterjee | Statement: [Mrigayaa, hasCastMember, Anil Chatterjee]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anil Chatterjee Context triple: [Mrigayaa, hasCastMember, Anil Chatterjee]
-
A.
Anil Chatterjee
chosen
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.
-
B.
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.
-
C.
Abhijit Bose
Abhijit Bose is an Indian technology executive best known for serving as the head of WhatsApp in India.
-
D.
Laxman Narasimhan
Laxman Narasimhan is an Indian-American business executive best known as the chief executive officer of Starbucks and former CEO of Reckitt Benckiser.
-
E.
Subrata Mitra
Subrata Mitra was an acclaimed Indian cinematographer best known for his pioneering visual work on Satyajit Ray’s films, which helped define the look of parallel cinema.
- 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_69e0b4a3320881909495ae8bc30bc2dc |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6784f8ff48190a070888786f6a989 |
completed | April 20, 2026, 7:02 p.m. |
Created at: April 16, 2026, 11:24 a.m.