Triple
T21945160
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Border |
E541914
|
entity |
| Predicate | leadActor |
P1507
|
FINISHED |
| Object | Suniel Shetty |
—
|
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: Suniel Shetty | Statement: [Border, leadActor, Suniel Shetty]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Suniel Shetty Context triple: [Border, leadActor, Suniel Shetty]
-
A.
Suniel Shetty
chosen
Suniel Shetty is an Indian film actor and producer best known for his action and comedy roles in numerous Bollywood movies since the 1990s.
-
B.
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.
-
C.
Sahib Devan
Sahib Devan is another name for Mata Sahib Kaur, a revered Sikh figure honored as the spiritual mother of the Khalsa.
-
D.
Pankaj Kapur
Pankaj Kapur is an acclaimed Indian actor and director known for his powerful performances in film, television, and theatre.
-
E.
Deepak Kapur
Deepak Kapur is a computer scientist known for his influential work in automated reasoning and term rewriting systems.
- 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.