Triple
T10482768
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aziz Ansari |
E247212
|
entity |
| Predicate | hasSibling |
P363
|
FINISHED |
| Object |
Aniz Ansari
Aniz Ansari is the brother of comedian and actor Aziz Ansari and a member of his extended family circle occasionally referenced in media.
|
E864835
|
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: Aniz Ansari | Statement: [Aziz Ansari, hasSibling, Aniz Ansari]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aniz Ansari Context triple: [Aziz Ansari, hasSibling, Aniz Ansari]
-
A.
Tanvi Azmi
Tanvi Azmi is an acclaimed Indian film and television actress known for her powerful character roles and multiple award-winning performances across Hindi cinema and TV.
-
B.
Anisa George
Anisa George is an actress known for her role in the critically acclaimed drama film "Rachel Getting Married."
-
C.
Mina Anwar
Mina Anwar is a British actress and singer best known for her comedic and character roles in television, film, and theatre.
-
D.
Moneeza Hashmi
Moneeza Hashmi is a Pakistani television producer and media professional known for her contributions to public broadcasting and cultural programming.
-
E.
Salma Lakhani
Salma Lakhani is a Canadian businesswoman and philanthropist who became the first Muslim and first South Asian to serve as a lieutenant governor in Canada.
- 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: Aniz Ansari Triple: [Aziz Ansari, hasSibling, Aniz Ansari]
Generated description
Aniz Ansari is the brother of comedian and actor Aziz Ansari and a member of his extended family circle occasionally referenced in media.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Aniz Ansari Target entity description: Aniz Ansari is the brother of comedian and actor Aziz Ansari and a member of his extended family circle occasionally referenced in media.
-
A.
Tanvi Azmi
Tanvi Azmi is an acclaimed Indian film and television actress known for her powerful character roles and multiple award-winning performances across Hindi cinema and TV.
-
B.
Anisa George
Anisa George is an actress known for her role in the critically acclaimed drama film "Rachel Getting Married."
-
C.
Mina Anwar
Mina Anwar is a British actress and singer best known for her comedic and character roles in television, film, and theatre.
-
D.
Moneeza Hashmi
Moneeza Hashmi is a Pakistani television producer and media professional known for her contributions to public broadcasting and cultural programming.
-
E.
Salma Lakhani
Salma Lakhani is a Canadian businesswoman and philanthropist who became the first Muslim and first South Asian to serve as a lieutenant governor in Canada.
- 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_69d381c309b88190af78aa681cf6a4c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d5095d21c08190a0b2f3e57fabb1d8 |
completed | April 7, 2026, 1:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d8a03336988190bc1e61126fe576be |
completed | April 10, 2026, 7:01 a.m. |
| NEDg | Description generation | batch_69d8a166404881909c28141fefea2936 |
completed | April 10, 2026, 7:06 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d8a2c550ac81908444c6abfe14698a |
completed | April 10, 2026, 7:12 a.m. |
Created at: April 6, 2026, 12:22 p.m.