Triple
T21945234
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Golmaal Again |
E541915
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object | Brijendra Kala |
—
|
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: Brijendra Kala | Statement: [Golmaal Again, hasCastMember, Brijendra Kala]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brijendra Kala Context triple: [Golmaal Again, hasCastMember, Brijendra Kala]
-
A.
Brijendra Kala
chosen
Brijendra Kala is an Indian character actor known for his subtle, realistic performances in Hindi films and television.
-
B.
Madan Lal Khurana
Madan Lal Khurana was an Indian politician from the Bharatiya Janata Party who played a key role in Delhi’s political landscape and also served in various national-level positions.
-
C.
Akhilendra Mishra
Akhilendra Mishra is an Indian film and television actor known for his character roles in Hindi cinema and TV, including notable performances in movies like Lagaan and Sarfarosh.
-
D.
Yogendra Shukla
Yogendra Shukla was an Indian freedom fighter and revolutionary leader associated with the independence movement against British colonial rule.
-
E.
Jitendra Malik
Jitendra Malik is a prominent computer scientist known for his influential work in computer vision and machine learning, and for mentoring leading researchers in the field.
- 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.