Triple
T19576101
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sardari Begum |
E489862
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Amrish Puri |
—
|
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: Amrish Puri | Statement: [Sardari Begum, castMember, Amrish Puri]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amrish Puri Context triple: [Sardari Begum, castMember, Amrish Puri]
-
A.
Amrish Puri
chosen
Amrish Puri was a renowned Indian actor best known for his powerful villainous roles in Hindi cinema and for playing the iconic antagonist Mola Ram in the film "Indiana Jones and the Temple of Doom."
-
B.
Sooraj Pancholi
Sooraj Pancholi is an Indian film actor known for his Bollywood debut in the romantic action film "Hero" (2015) and for being the son of actors Aditya Pancholi and Zarina Wahab.
-
C.
Paresh Rawal
Paresh Rawal is a renowned Indian actor and comedian celebrated for his versatile performances in Hindi cinema and theatre.
-
D.
Amit Phalke
Amit Phalke is an actor known for his role in the Indian film "Mammo."
-
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 (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_69d8e8dd9374819098e36349b3211663 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e64024c5b08190bbff6df633857874 |
completed | April 20, 2026, 3:03 p.m. |
Created at: April 10, 2026, 1:42 p.m.