Triple
T19273679
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zubeidaa |
E481991
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Surekha Sikri |
—
|
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: Surekha Sikri | Statement: [Zubeidaa, castMember, Surekha Sikri]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Surekha Sikri Context triple: [Zubeidaa, castMember, Surekha Sikri]
-
A.
Surekha Sikri
chosen
Surekha Sikri was a renowned Indian theatre, film, and television actress celebrated for her powerful character roles and multiple National Film Awards.
-
B.
Sarika Thakur
Sarika Thakur, known mononymously as Sarika, is an Indian actress and former child star recognized for her work in Hindi cinema and television.
-
C.
Neetu Singh
Neetu Singh is a renowned Indian actress best known for her work in Hindi films of the 1970s and 1980s and for her later return to cinema alongside her husband Rishi Kapoor.
-
D.
Sushma Kharakwal
Sushma Kharakwal is an Indian politician who has served as the mayor of Lucknow, the capital city of Uttar Pradesh.
-
E.
Sandhini Agarwal
Sandhini Agarwal is an AI researcher known for her work at OpenAI on safety, policy, and the development and deployment of large-scale models such as CLIP.
- 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_69d8e8ce54cc8190998418ff1f66ef28 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fbba7758819081c1c78667c59c5e |
completed | April 20, 2026, 10:11 a.m. |
Created at: April 10, 2026, 1:29 p.m.