Triple
T16183446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Raja Hindustani |
E392740
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Navneet Nishan |
E1199390
|
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: Navneet Nishan | Statement: [Raja Hindustani, starring, Navneet Nishan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Navneet Nishan Context triple: [Raja Hindustani, starring, Navneet Nishan]
-
A.
Navneet Nishan
chosen
Navneet Nishan is an Indian actress best known for her work in Hindi films and television serials during the 1990s and 2000s.
-
B.
Navneet Verma
Navneet Verma is a cinematographer known for his work on the animated fantasy film "Tinker Bell and the Legend of the NeverBeast."
-
C.
Amandeep Singh
Amandeep Singh is an actor who appeared in the 2018 biographical thriller film "Hotel Mumbai."
-
D.
Nirvikar Singh
Nirvikar Singh is an economist and academic known for his contributions to economic theory and policy, associated with leading institutions such as the Delhi School of Economics.
-
E.
Manvinder Singh Banga
Manvinder Singh Banga is an Indian business executive best known for his long career at Unilever, where he rose to senior global leadership roles.
- 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_69d87f1e49ac8190a311b54d32990576 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e2205ef39081908da383abdebc2ccc |
completed | April 17, 2026, 11:58 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0007899b408190abcb7e72bdc81e9d |
completed | May 10, 2026, 4:20 a.m. |
Created at: April 10, 2026, 5:02 a.m.