Triple
T20819965
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aasif Mandvi |
E512549
|
entity |
| Predicate | stageName |
P7872
|
FINISHED |
| Object | Aasif Mandvi |
—
|
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: Aasif Mandvi | Statement: [Aasif Mandvi, stageName, Aasif Mandvi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aasif Mandvi Context triple: [Aasif Mandvi, stageName, Aasif Mandvi]
-
A.
Aasif Mandvi
chosen
Aasif Mandvi is a British-American actor, comedian, and writer best known for his work as a correspondent on The Daily Show and for roles in film, television, and theater.
-
B.
Azim Surani
Azim Surani is a British developmental biologist renowned for his pioneering work on mammalian germ cell development and genomic imprinting.
-
C.
Vaseem Khan
Vaseem Khan is a British crime fiction author best known for his Baby Ganesh Agency series set in India and his Malabar House historical crime novels.
-
D.
Sacha Dhawan
Sacha Dhawan is a British actor known for his versatile television and film roles, including his acclaimed portrayal of the Master in the long-running sci-fi series Doctor Who.
-
E.
Manish Bhasin
Manish Bhasin is a British sports journalist and television presenter best known for his long-running work on BBC football coverage.
- 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_69e0b4ce39108190a6e8e5df4f1c8dc5 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2f6a65481909a0df78616e185e4 |
completed | April 21, 2026, 12:21 a.m. |
Created at: April 16, 2026, 12:41 p.m.