Triple
T16528238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ravi Basrur |
E401493
|
entity |
| Predicate | composedForFilm |
P29643
|
FINISHED |
| Object | K.G.F: Chapter 1 |
E1223655
|
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: K.G.F: Chapter 1 | Statement: [Ravi Basrur, composedForFilm, K.G.F: Chapter 1]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: K.G.F: Chapter 1 Context triple: [Ravi Basrur, composedForFilm, K.G.F: Chapter 1]
-
A.
K.G.F: Chapter 1
chosen
K.G.F: Chapter 1 is a 2018 Indian Kannada-language period action film that follows the rise of a ruthless gangster in the Kolar Gold Fields and became a major pan-Indian blockbuster.
-
B.
K.G.F: Chapter 2
K.G.F: Chapter 2 is a 2022 Indian Kannada-language action film and the sequel to K.G.F: Chapter 1, known for its high-octane storytelling, stylized violence, and massive box-office success.
-
C.
KGF
KGF is the IATA airport code for Sary-Arka Airport, which serves the city of Karaganda in Kazakhstan.
-
D.
Kabali
Kabali is a 2016 Indian Tamil-language action drama film starring Rajinikanth as an aging gangster seeking revenge and redemption.
-
E.
Kaithi
Kaithi is a 2019 Tamil-language action thriller film centered on an ex-convict’s overnight mission to save poisoned police officers while evading ruthless criminals.
- 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_69d883838abc8190bc79cb2d41733ce2 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32ed57be481908625d4c5aab0940c |
completed | April 18, 2026, 7:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a008a2590d48190b72b7d5be20c14c7 |
completed | May 10, 2026, 1:37 p.m. |
Created at: April 10, 2026, 5:14 a.m.