Triple
T11535391
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Randhir Kapoor |
E273533
|
entity |
| Predicate | child |
P120
|
FINISHED |
| Object | Karisma Kapoor |
E262551
|
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: Karisma Kapoor | Statement: [Randhir Kapoor, child, Karisma Kapoor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karisma Kapoor Context triple: [Randhir Kapoor, child, Karisma Kapoor]
-
A.
Karisma Kapoor
chosen
Karisma Kapoor is an acclaimed Indian film actress best known for her leading roles in popular Hindi movies of the 1990s and early 2000s.
-
B.
Seema Kapoor
Seema Kapoor is an Indian television and film actress and director, known for her work in Hindi entertainment and her marriage to the late actor Om Puri.
-
C.
Sridevi
Sridevi was a legendary Indian actress celebrated for her versatile performances across Tamil, Hindi, and other regional cinemas, and is widely regarded as one of the greatest and most influential actresses in Indian film history.
-
D.
Rani Mukerji
Rani Mukerji is an acclaimed Indian film actress known for her versatile performances in numerous successful Hindi movies since the late 1990s.
-
E.
Neha Kapur
Neha Kapur is an Indian model, former Miss India Universe 2006, and fashion entrepreneur.
- 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_69d6aae3fbec8190a14632a5df2538b6 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8839b4bb48190b748ec4119f36c11 |
completed | April 10, 2026, 4:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e7137a09608190801af2125e2e8095 |
completed | April 21, 2026, 6:04 a.m. |
Created at: April 8, 2026, 9:37 p.m.