Triple
T22433489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maj Rati Keteki |
E554554
|
entity |
| Predicate | musicBy |
P1952
|
FINISHED |
| Object | Anurag Saikia |
—
|
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: Anurag Saikia | Statement: [Maj Rati Keteki, musicBy, Anurag Saikia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anurag Saikia Context triple: [Maj Rati Keteki, musicBy, Anurag Saikia]
-
A.
Anurag Saikia
chosen
Anurag Saikia is an Indian music composer and producer known for his work on Hindi films and independent music projects.
-
B.
Gautam Saha
Gautam Saha is a relatively obscure individual about whom no widely known public information is available.
-
C.
Ashutosh Rana
Ashutosh Rana is an acclaimed Indian film and television actor known for his intense character roles and powerful villainous performances in Hindi and regional cinema.
-
D.
Samit Bhanja
Samit Bhanja was an Indian film and theatre actor known for his work in Bengali cinema during the 1960s and 1970s.
-
E.
Sourav Pal
Sourav Pal is an accomplished Indian chemist and academic known for his contributions to theoretical and computational chemistry.
- 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_69e11e5010e48190ae1e9c9db9697637 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15adce9688190992ad0ca15883931 |
completed | April 29, 2026, 1:11 a.m. |
Created at: April 16, 2026, 8:47 p.m.