Triple
T3162941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shahrukh Mirza |
E66143
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Shahrukh |
E66143
|
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: Shahrukh | Statement: [Shahrukh Mirza, givenName, Shahrukh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shahrukh Context triple: [Shahrukh Mirza, givenName, Shahrukh]
-
A.
Shah Rukh Khan
Shah Rukh Khan is a hugely influential Indian film actor and producer, often called the "King of Bollywood," known for his prolific career in Hindi cinema and global cultural impact.
-
B.
Salman Khan
Salman Khan is an American educator and entrepreneur best known as the founder of the online learning platform Khan Academy.
-
C.
Aamir Khan
Aamir Khan is a renowned Indian film actor, director, and producer known for his critically acclaimed and socially impactful movies in Bollywood.
-
D.
Shahrukh Mirza
chosen
Shahrukh Mirza was a 15th-century Timurid ruler who consolidated and governed much of Iran and Central Asia, fostering a flourishing of Persian culture, arts, and architecture.
-
E.
Akshaye Khanna
Akshaye Khanna is an Indian film actor known for his versatile performances in Hindi cinema across both commercial hits and critically acclaimed dramas.
- 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_69ad85850c1481908a9e9c6242238de2 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada61a4b8481908897a8d39d94c2f4 |
completed | March 8, 2026, 4:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b24b4e09388190b41da913f677ffb7 |
completed | March 12, 2026, 5:12 a.m. |
Created at: March 8, 2026, 3:06 p.m.