Triple
T7073223
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mahesh Babu |
E164750
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Mahesh Babu |
E164750
|
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: Mahesh Babu | Statement: [Mahesh Babu, name, Mahesh Babu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mahesh Babu Context triple: [Mahesh Babu, name, Mahesh Babu]
-
A.
Mahesh Babu
chosen
Mahesh Babu is a leading Indian actor and producer best known for his work in Telugu cinema, where he is celebrated for his charismatic screen presence and numerous blockbuster films.
-
B.
Pawan Kalyan
Pawan Kalyan is a prominent Indian film actor, producer, and politician best known for his work in Telugu cinema and his charismatic screen presence.
-
C.
Prabhas
Prabhas is an Indian film actor best known for his leading role in the blockbuster "Baahubali" series, which brought him international fame.
-
D.
Ram Charan
Ram Charan is a prominent Indian film actor and producer best known for his leading roles in Telugu cinema and for being one of the highest-paid actors in the industry.
-
E.
Venkatesh Daggubati
Venkatesh Daggubati is a prominent Indian film actor best known for his work in Telugu cinema, where he has had a successful career spanning several decades.
- 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_69c6887b96548190a8a9b3ac8adf4119 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e4cb76548190bd98876f8ba925b7 |
completed | March 27, 2026, 8:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8682696a48190aec021bd00c6f633 |
completed | March 28, 2026, 11:45 p.m. |
Created at: March 27, 2026, 2:39 p.m.