Triple
T8154122
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sanjeev Bhaskar |
E190402
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Sanjeev Bhaskar |
E190402
|
NE FINISHED |
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: Sanjeev Bhaskar | Statement: [Sanjeev Bhaskar, name, Sanjeev Bhaskar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sanjeev Bhaskar Context triple: [Sanjeev Bhaskar, name, Sanjeev Bhaskar]
-
A.
Sanjeev Bhaskar
chosen
Sanjeev Bhaskar is a British comedian, actor, and writer best known for his work on the sketch show "Goodness Gracious Me" and the sitcom "The Kumars at No. 42."
-
B.
Sanjay Sen
Sanjay Sen is known primarily as the husband of acclaimed Indian filmmaker and actress Aparna Sen.
-
C.
Anupam Kher
Anupam Kher is an acclaimed Indian actor known for his extensive work in Hindi cinema and notable roles in international films.
-
D.
Anupam Tripathi
Anupam Tripathi is an Indian actor best known internationally for his breakout role as Ali Abdul in the South Korean Netflix series "Squid Game."
-
E.
Vikas Khanna
Vikas Khanna is an acclaimed Indian chef, restaurateur, cookbook author, and filmmaker known for his Michelin-starred cooking and appearances on culinary television shows.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca82bfeb6481909d07b91b5cf69f59 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb44d566b08190a6bb672f9c368806 |
completed | March 31, 2026, 3:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ccbf0518688190b519fbe1823b95c2 |
completed | April 1, 2026, 6:45 a.m. |
Created at: March 30, 2026, 5:37 p.m.