Triple
T21944386
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haasil |
E541897
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object | Ashutosh Rana |
—
|
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: Ashutosh Rana | Statement: [Haasil, hasCastMember, Ashutosh Rana]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ashutosh Rana Context triple: [Haasil, hasCastMember, Ashutosh Rana]
-
A.
Ashutosh Rana
chosen
Ashutosh Rana is an acclaimed Indian film and television actor known for his intense character roles and powerful villainous performances in Hindi and regional cinema.
-
B.
Gautam Saha
Gautam Saha is a relatively obscure individual about whom no widely known public information is available.
-
C.
Aashish Chaudhary
Aashish Chaudhary is an Indian actor and former model known for his work in Bollywood films and Hindi television.
-
D.
Gautam Kumar
Gautam Kumar is known as the son of legendary Indian Bengali actor Uttam Kumar.
-
E.
Shashank Manohar
Shashank Manohar is an Indian cricket administrator and lawyer who has served as president of the BCCI and later became a leading reformist figure in global cricket governance.
- 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_69e0c47e2e5c81909a7f74ce3de50911 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1242688988190a7b8f033c49368de |
completed | April 28, 2026, 9:18 p.m. |
Created at: April 16, 2026, 7:56 p.m.