Triple
T10213340
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guru (2007 film) |
E242382
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object |
Vidya Balan
Vidya Balan is an acclaimed Indian actress known for her powerful performances in Hindi cinema and for pioneering strong, female-led films in Bollywood.
|
E865246
|
NE FINISHED |
How this triple was built (4 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: Vidya Balan | Statement: [Guru (2007 film), castMember, Vidya Balan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vidya Balan Context triple: [Guru (2007 film), castMember, Vidya Balan]
-
A.
Juhi Chawla
Juhi Chawla is a popular Indian actress and film producer known for her work in Hindi cinema since the late 1980s.
-
B.
Kangana Ranaut
Kangana Ranaut is an acclaimed Indian film actress known for her powerful performances in Hindi cinema and multiple National Film Awards.
-
C.
Neha Kapur
Neha Kapur is an Indian model, former Miss India Universe 2006, and fashion entrepreneur.
-
D.
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.
-
E.
Anushka Shetty
Anushka Shetty is a prominent Indian actress best known for her leading roles in Telugu and Tamil cinema, including major historical and fantasy epics.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Vidya Balan Triple: [Guru (2007 film), castMember, Vidya Balan]
Generated description
Vidya Balan is an acclaimed Indian actress known for her powerful performances in Hindi cinema and for pioneering strong, female-led films in Bollywood.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Vidya Balan Target entity description: Vidya Balan is an acclaimed Indian actress known for her powerful performances in Hindi cinema and for pioneering strong, female-led films in Bollywood.
-
A.
Juhi Chawla
Juhi Chawla is a popular Indian actress and film producer known for her work in Hindi cinema since the late 1980s.
-
B.
Kangana Ranaut
Kangana Ranaut is an acclaimed Indian film actress known for her powerful performances in Hindi cinema and multiple National Film Awards.
-
C.
Neha Kapur
Neha Kapur is an Indian model, former Miss India Universe 2006, and fashion entrepreneur.
-
D.
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.
-
E.
Anushka Shetty
Anushka Shetty is a prominent Indian actress best known for her leading roles in Telugu and Tamil cinema, including major historical and fantasy epics.
- F. None of above. chosen
Provenance (5 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_69d381ae26c48190985abd0e25ee5d04 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d3aa24efc081909714d98943543283 |
completed | April 6, 2026, 12:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d89f25c16c8190a17dc19e3e1b197a |
completed | April 10, 2026, 6:56 a.m. |
| NEDg | Description generation | batch_69d8a2b0d8c88190a1a64bd2bbacabbe |
completed | April 10, 2026, 7:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d8a6560ddc81909d540f78a9413b3e |
completed | April 10, 2026, 7:27 a.m. |
Created at: April 6, 2026, 11:03 a.m.