Triple
T6904277
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lara Pulver |
E159568
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Raza Jaffrey |
E140752
|
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: Raza Jaffrey | Statement: [Lara Pulver, spouse, Raza Jaffrey]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Raza Jaffrey Context triple: [Lara Pulver, spouse, Raza Jaffrey]
-
A.
Raza Jaffrey
chosen
Raza Jaffrey is a British actor and singer known for his roles in television series such as "Smash," "Homeland," and "Spooks" (MI-5).
-
B.
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.
-
C.
Arif Masood
Arif Masood is a Pakistani architect best known for designing the iconic Pakistan Monument in Islamabad.
-
D.
Rizwan Manji
Rizwan Manji is a Canadian actor and comedian known for his character roles in television series such as "Schitt's Creek," "Outsourced," and "Perfect Harmony."
-
E.
Zaab Sethna
Zaab Sethna is a public relations and communications professional best known as the husband of actress Gina Bellman.
- 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_69c6883822e0819091e321526f20ae0a |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6d989c13081908a2e346cde9e3a50 |
completed | March 27, 2026, 7:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c748f6640481908b74903a47e1eb18 |
completed | March 28, 2026, 3:20 a.m. |
Created at: March 27, 2026, 2:25 p.m.