Triple
T10468622
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bride and Prejudice |
E246867
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Aishwarya Rai |
E746029
|
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: Aishwarya Rai | Statement: [Bride and Prejudice, castMember, Aishwarya Rai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aishwarya Rai Context triple: [Bride and Prejudice, castMember, Aishwarya Rai]
-
A.
Aishwarya Rai
chosen
Aishwarya Rai is an acclaimed Indian actress and former Miss World, renowned for her work in Bollywood and international cinema as well as her iconic beauty.
-
B.
Aishwarya Rajesh
Aishwarya Rajesh is an Indian actress known for her acclaimed performances in Tamil cinema, particularly in realistic, character-driven roles.
-
C.
Preity Zinta
Preity Zinta is an Indian film actress and entrepreneur best known for her work in Hindi cinema, including acclaimed performances in films like "Kal Ho Naa Ho," "Dil Chahta Hai," and "Veer-Zaara."
-
D.
Lara Dutta
Lara Dutta is an Indian actress, model, and former Miss Universe (2000) known for her work in Bollywood films.
-
E.
Karisma Kapoor
Karisma Kapoor is an acclaimed Indian film actress best known for her leading roles in popular Hindi movies of the 1990s and early 2000s.
- 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_69d381c16c248190a2fe5b471e584e9c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5092ef810819093a4d1df83aeac09 |
completed | April 7, 2026, 1:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d94b047b588190a116f4fbd4cdbc35 |
completed | April 10, 2026, 7:09 p.m. |
Created at: April 6, 2026, 12:20 p.m.