Triple
T22755594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rekha |
E562834
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Rekha |
—
|
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: Rekha | Statement: [Rekha, name, Rekha]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rekha Context triple: [Rekha, name, Rekha]
-
A.
Rekha
chosen
Rekha is a celebrated Indian film actress renowned for her versatile performances and enduring impact on Hindi cinema.
-
B.
Raakhee
Raakhee is a renowned Indian film actress known for her acclaimed performances in Hindi and Bengali cinema from the late 1960s through the 1980s.
-
C.
Uma Maheswari
Uma Maheswari is a Hindu goddess venerated as the consort of Lord Shiva and a local deity associated with the town of Sirkazhi in Tamil Nadu, India.
-
D.
Nadira Babbar
Nadira Babbar is an Indian theatre director and actress known for her work in Hindi cinema and on stage, including a role in the film "Bride and Prejudice."
-
E.
Rupa Mehra
Rupa Mehra is a central matriarchal figure in Vikram Seth’s novel "A Suitable Boy," best known for her determined quest to find an ideal husband for her daughter Lata.
- 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_69e24551ec7881909a9c924dbea155f6 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f179bd22588190ac724a656194f5b9 |
completed | April 29, 2026, 3:23 a.m. |
Created at: April 17, 2026, 3:25 p.m.