Triple
T10213295
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rangeela |
E242381
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Urmila Matondkar |
E856678
|
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: Urmila Matondkar | Statement: [Rangeela, castMember, Urmila Matondkar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Urmila Matondkar Context triple: [Rangeela, castMember, Urmila Matondkar]
-
A.
Urmila Matondkar
chosen
Urmila Matondkar is an Indian film actress known for her acclaimed performances in Hindi cinema, particularly in the 1990s and early 2000s, and for her later work as a television personality and politician.
-
B.
Rani Mukerji
Rani Mukerji is an acclaimed Indian film actress known for her versatile performances in numerous successful Hindi movies since the late 1990s.
-
C.
Smita Patil
Smita Patil was a critically acclaimed Indian actress known for her powerful performances in parallel cinema during the 1970s and 1980s.
-
D.
Karisma Kapoor
Karisma Kapoor is an acclaimed Indian film actress best known for her leading roles in popular Hindi movies of the 1990s and early 2000s.
-
E.
Dimple Kapadia
Dimple Kapadia is a renowned Indian film actress known for her work in Hindi cinema since the 1970s, acclaimed for both mainstream and critically lauded roles.
- 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_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_69d79490a3c48190a58bff2f63e5873d |
completed | April 9, 2026, 11:59 a.m. |
Created at: April 6, 2026, 11:03 a.m.