Triple
T22914380
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kavita Krishnamurti |
E568688
|
entity |
| Predicate | notableCollaboration |
P8554
|
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: [Kavita Krishnamurti, notableCollaboration, Anu Malik]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anu Malik Context triple: [Kavita Krishnamurti, notableCollaboration, 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.
Ranvir Shorey
Ranvir Shorey is an Indian actor known for his versatile performances in Hindi films and television, often in offbeat and critically acclaimed roles.
-
C.
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.
-
D.
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.
-
E.
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.
- 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_69e2458d90c88190a58cead4e781ca6a |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18078077881908b8124cc22110c6e |
completed | April 29, 2026, 3:52 a.m. |
Created at: April 17, 2026, 3:42 p.m.