Triple
T7694283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shah Rukh Khan |
E174331
|
entity |
| Predicate | coOwnerWith |
P3498
|
FINISHED |
| Object | Juhi Chawla |
E646366
|
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: Juhi Chawla | Statement: [Shah Rukh Khan, coOwnerWith, Juhi Chawla]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Juhi Chawla Context triple: [Shah Rukh Khan, coOwnerWith, Juhi Chawla]
-
A.
Juhi Chawla
chosen
Juhi Chawla is a popular Indian actress and film producer known for her work in Hindi cinema since the late 1980s.
-
B.
Shriya Saran
Shriya Saran is an Indian actress and model known for her work in Telugu, Tamil, and Hindi cinema, appearing in numerous commercially successful and critically acclaimed films.
-
C.
Kajal Aggarwal
Kajal Aggarwal is a popular Indian actress best known for her leading roles in Telugu and Tamil cinema, as well as appearances in Hindi films.
-
D.
Riya Sen
Riya Sen is an Indian actress and model known for her work in Hindi, Bengali, and other regional films, as well as for her prominent presence in Indian popular culture and fashion.
-
E.
Ramya Krishnan
Ramya Krishnan is an acclaimed Indian actress known for her powerful and versatile performances across Tamil, Telugu, and other South Indian film industries.
- 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_69c6995966348190939e6c37ba272c06 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c702459f988190bf7087bf51d5317f |
completed | March 27, 2026, 10:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8b4fda81881908144cebdd2696e63 |
completed | March 29, 2026, 5:13 a.m. |
Created at: March 27, 2026, 4:02 p.m.