Triple
T7164476
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Prabhas |
E167031
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Mr. Perfect |
E639745
|
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: Mr. Perfect | Statement: [Prabhas, notableWork, Mr. Perfect]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mr. Perfect Context triple: [Prabhas, notableWork, Mr. Perfect]
-
A.
Mr. Perfect
chosen
Mr. Perfect is a 2011 Telugu romantic comedy film starring Prabhas and Kajal Aggarwal, known for its themes of compromise in relationships and its commercial success in Indian cinema.
-
B.
Handsome Dan
Handsome Dan is the live bulldog mascot and enduring symbol of Yale University's athletic teams and school spirit.
-
C.
Mr. Man
"Mr. Man" is a song by Alicia Keys from her debut studio album "Songs in A Minor."
-
D.
Mr. G
Mr. G is the nickname of Gustaf V, who was King of Sweden from 1907 to 1950 and one of the longest-reigning Swedish monarchs.
-
E.
Mr. Nice
Mr. Nice is a 2010 biographical crime film in which Rhys Ifans portrays real-life Welsh drug smuggler Howard Marks.
- 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_69c68888c10c819095e0383020225758 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e83168a08190937ff46797d94f3e |
completed | March 27, 2026, 8:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7adcc145c8190ba65831ed891a225 |
completed | March 28, 2026, 10:30 a.m. |
Created at: March 27, 2026, 2:47 p.m.