Triple
T21110603
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kulbhushan Kharbanda |
E520160
|
entity |
| Predicate | hasActedIn |
P15620
|
FINISHED |
| Object | Shaan |
—
|
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: Shaan | Statement: [Kulbhushan Kharbanda, hasActedIn, Shaan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shaan Context triple: [Kulbhushan Kharbanda, hasActedIn, Shaan]
-
A.
Shaan
chosen
Shaan is a popular Indian playback singer and television host known for his melodious voice and numerous hit songs in Hindi cinema and other Indian languages.
-
B.
Shan
The Shan are a Tai ethnic group primarily inhabiting Myanmar's Shan State, known for their distinct language, Buddhist traditions, and historical Shan principalities.
-
C.
Shuheng
Shuheng is the given name of He Shuheng, an early Chinese Communist revolutionary and political figure.
-
D.
Sheng
Sheng is the primary male role type in traditional Chinese Peking opera, typically portraying dignified scholars, officials, and heroic figures.
-
E.
Sheng
Sheng is an urban Kenyan slang language that blends Swahili, English, and various local languages, widely spoken in Nairobi’s informal settlements and youth culture.
- 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_69e0b509a318819092fbbcb21d1fe603 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e72101f7308190beb202a052ff04d2 |
completed | April 21, 2026, 7:02 a.m. |
Created at: April 16, 2026, 2:54 p.m.