Triple
T29332602
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guru Sishyan (1988 film) |
E743818
|
entity |
| Predicate | characterPlayedByPrabhu |
P193941
|
FINISHED |
| Object | Babu |
—
|
NE NERFINISHED |
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: Babu | Statement: [Guru Sishyan (1988 film), characterPlayedByPrabhu, Babu]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterPlayedByPrabhu Context triple: [Guru Sishyan (1988 film), characterPlayedByPrabhu, Babu]
-
A.
mainActorForCharacterPrabhu
Indicates that the referenced person is the primary actor who portrays the character Prabhu.
-
B.
characterPlayedByShrutiHaasan
Indicates that a specific character is portrayed or acted by Shruti Haasan.
-
C.
leadActorForCharacterDevdhar
Indicates that the subject is the primary actor who portrays the character Devdhar.
-
D.
leadActorForCharacterKaran
Indicates that the specified person is the lead actor portraying the character named Karan.
-
E.
leadActorForCharacterRahul
Indicates that a person is the lead actor portraying the character named Rahul in a specific production.
- F. None of above. chosen
Provenance (4 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_69f09126cfcc8190899b16fbf3c2bf7b |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69fd5bf69acc819092a01e4259785dc3 |
completed | May 8, 2026, 3:43 a.m. |
| PD | Predicate disambiguation | batch_69fd59b3f4ac8190a7f9dd3142da6e09 |
completed | May 8, 2026, 3:34 a.m. |
| PDg | Predicate description generation | batch_69fd5bf49288819098a12202411cba4f |
completed | May 8, 2026, 3:43 a.m. |
Created at: April 28, 2026, 1:30 p.m.