Triple
T18399770
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rishi Markandeya |
E449960
|
entity |
| Predicate | hasAttribute |
P274
|
FINISHED |
| Object | chiranjeevi |
—
|
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: chiranjeevi | Statement: [Rishi Markandeya, hasAttribute, chiranjeevi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: chiranjeevi Context triple: [Rishi Markandeya, hasAttribute, chiranjeevi]
-
A.
Chiranjeevi
chosen
Chiranjeevi is a legendary Indian film actor and former politician, widely regarded as one of the biggest and most influential stars in Telugu cinema.
-
B.
Krishnam Raju
Krishnam Raju was a prominent Indian film actor and politician, widely known as the "Rebel Star" of Telugu cinema.
-
C.
N. T. Rama Rao
N. T. Rama Rao was a legendary Indian film actor, filmmaker, and politician who became one of Telugu cinema’s biggest icons and served as the Chief Minister of Andhra Pradesh.
-
D.
N. T. Rama Rao Jr.
N. T. Rama Rao Jr. is a prominent Indian film actor known for his leading roles in Telugu cinema and his dynamic performances in action and drama films.
-
E.
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9fab8a8819086a9ddc0871715e0 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e518499b1481909c5de786c48faeba |
completed | April 19, 2026, 6 p.m. |
Created at: April 10, 2026, 10:46 a.m.