Triple
T22103074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Special 26 |
E546216
|
entity |
| Predicate | leadCharacter |
P1668
|
FINISHED |
| Object |
Waseem Khan
Waseem Khan is the central mastermind thief character in the Hindi heist film "Special 26," known for orchestrating elaborate cons while posing as a government officer.
|
E1522393
|
NE FINISHED |
How this triple was built (4 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: Waseem Khan | Statement: [Special 26, leadCharacter, Waseem Khan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Waseem Khan Context triple: [Special 26, leadCharacter, Waseem Khan]
-
A.
Arshad Khan
Arshad Khan was a victim killed during the 2011 U.S. special forces raid in Abbottabad, Pakistan, that targeted Osama bin Laden.
-
B.
Vaseem Khan
Vaseem Khan is a British crime fiction author best known for his Baby Ganesh Agency series set in India and his Malabar House historical crime novels.
-
C.
Shehzad Khan
Shehzad Khan is an Indian film and television actor best known for his supporting and comic roles in Hindi cinema, including the classic romance "Qayamat Se Qayamat Tak."
-
D.
Zayed Khan
Zayed Khan is an Indian film actor best known for his roles in early-2000s Bollywood movies such as "Main Hoon Na."
-
E.
Saleem Raza
Saleem Raza was a Pakistani film actor known for his character roles in classic Lollywood movies of the 1950s and 1960s.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Waseem Khan Triple: [Special 26, leadCharacter, Waseem Khan]
Generated description
Waseem Khan is the central mastermind thief character in the Hindi heist film "Special 26," known for orchestrating elaborate cons while posing as a government officer.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Waseem Khan Target entity description: Waseem Khan is the central mastermind thief character in the Hindi heist film "Special 26," known for orchestrating elaborate cons while posing as a government officer.
-
A.
Arshad Khan
Arshad Khan was a victim killed during the 2011 U.S. special forces raid in Abbottabad, Pakistan, that targeted Osama bin Laden.
-
B.
Vaseem Khan
Vaseem Khan is a British crime fiction author best known for his Baby Ganesh Agency series set in India and his Malabar House historical crime novels.
-
C.
Shehzad Khan
Shehzad Khan is an Indian film and television actor best known for his supporting and comic roles in Hindi cinema, including the classic romance "Qayamat Se Qayamat Tak."
-
D.
Zayed Khan
Zayed Khan is an Indian film actor best known for his roles in early-2000s Bollywood movies such as "Main Hoon Na."
-
E.
Saleem Raza
Saleem Raza was a Pakistani film actor known for his character roles in classic Lollywood movies of the 1950s and 1960s.
- F. None of above. chosen
Provenance (5 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_69e11e378dc08190896d6a51597afd5a |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f129175a7881909549883f23c53dca |
completed | April 28, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a9eb01c30819095f1f9a4d36b647d |
completed | May 18, 2026, 5:08 a.m. |
| NEDg | Description generation | batch_6a0a9f9fbc348190a95d9bf588312b24 |
completed | May 18, 2026, 5:12 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a9ffa8f308190beaa350f4e8b1e54 |
completed | May 18, 2026, 5:13 a.m. |
Created at: April 16, 2026, 8:30 p.m.