Triple
T20417575
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shaadi No. 1 |
E500751
|
entity |
| Predicate | musicBy |
P1952
|
FINISHED |
| Object | Anu Malik |
—
|
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: Anu Malik | Statement: [Shaadi No. 1, musicBy, Anu Malik]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anu Malik Context triple: [Shaadi No. 1, musicBy, Anu Malik]
-
A.
Anu Malik
chosen
Anu Malik is an Indian music director and composer known for his prolific work in Bollywood films and several popular film soundtracks.
-
B.
Shubham Saraf
Shubham Saraf is a British actor known for his role in the TV crime drama "Criminal: UK" and performances across film, television, and theatre.
-
C.
Naman Goyal
Naman Goyal is a computer scientist and AI researcher known for his contributions to large-scale natural language processing models and representation learning at Meta AI.
-
D.
Anurag Behar
Anurag Behar is an Indian educationist and social sector leader best known for heading the Azim Premji Foundation and contributing to large-scale education reform in India.
-
E.
Shekhar Suman
Shekhar Suman is an Indian actor, television host, and comedian known for his work in Hindi films and popular TV shows.
- 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_69e0b4a935588190b9446a99b37ced44 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67a44ecf48190ba5a3872af500dc8 |
completed | April 20, 2026, 7:11 p.m. |
Created at: April 16, 2026, 11:30 a.m.