Triple
T21944323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Qarib Qarib Singlle |
E541896
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object | Kamal Pandey |
—
|
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: Kamal Pandey | Statement: [Qarib Qarib Singlle, writer, Kamal Pandey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kamal Pandey Context triple: [Qarib Qarib Singlle, writer, Kamal Pandey]
-
A.
Shashi Kaushik
Shashi Kaushik is known as the wife of the late Indian actor, director, and producer Satish Kaushik.
-
B.
Kamal Mehra
Kamal Mehra is an Indian actor known for his role in the 1962 adventure film "Tarzan Goes to India."
-
C.
Raj Kamal
Raj Kamal is a character in the Indian film "Rangeela," around whom part of the movie’s romantic and dramatic narrative revolves.
-
D.
Pran Kapoor
Pran Kapoor is a mild-mannered, scholarly English lecturer in Vikram Seth’s novel "A Suitable Boy," known for his steady, responsible nature and marriage into the Mehra family.
-
E.
Robin Bhatt
chosen
Robin Bhatt is an Indian screenwriter known for his work on numerous successful Bollywood films, particularly in the romance and drama genres.
- 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_69e0c47e2e5c81909a7f74ce3de50911 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1242688988190a7b8f033c49368de |
completed | April 28, 2026, 9:18 p.m. |
Created at: April 16, 2026, 7:56 p.m.