Triple
T16528237
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ravi Basrur |
E401493
|
entity |
| Predicate | composedForFilm |
P29643
|
FINISHED |
| Object | Ugramm |
E1218757
|
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: Ugramm | Statement: [Ravi Basrur, composedForFilm, Ugramm]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ugramm Context triple: [Ravi Basrur, composedForFilm, Ugramm]
-
A.
Ugramm
chosen
Ugramm is a Kannada-language action film known for its gritty storytelling, intense performances, and impactful music.
-
B.
Umka
Umka is a suburban settlement of Belgrade, Serbia, situated within the municipality of Čukarica along the right bank of the Sava River.
-
C.
Umang Lai
Umang Lai are forest deities in the Meitei religion of Sanamahism, revered as guardians of sacred groves and natural spaces in Manipur.
-
D.
Humraaz
Humraaz is a 2002 Hindi-language romantic thriller film directed by Abbas–Mustan, known for its twist-filled plot involving love, betrayal, and a high-stakes murder conspiracy.
-
E.
Gaghan
Gaghan is the surname of Stephen Gaghan, an American screenwriter and director known for works like "Traffic" and "Syriana."
- 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_6a0067a913388190afebe40fcc42e731 |
completed | May 10, 2026, 11:10 a.m. |
Created at: April 10, 2026, 5:14 a.m.