Triple
T14240159
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Boy with the Topknot |
E352982
|
entity |
| Predicate | executiveProducer |
P7225
|
FINISHED |
| Object | Sathnam Sanghera |
E1087580
|
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: Sathnam Sanghera | Statement: [The Boy with the Topknot, executiveProducer, Sathnam Sanghera]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sathnam Sanghera Context triple: [The Boy with the Topknot, executiveProducer, Sathnam Sanghera]
-
A.
Sathnam Sanghera
chosen
Sathnam Sanghera is a British journalist and author known for his memoir "The Boy with the Topknot" and his writing on British Sikh identity, class, and the legacy of empire.
-
B.
Zaab Sethna
Zaab Sethna is a public relations and communications professional best known as the husband of actress Gina Bellman.
-
C.
Raza Jaffrey
Raza Jaffrey is a British actor and singer known for his roles in television series such as "Smash," "Homeland," and "Spooks" (MI-5).
-
D.
Roshan Sethi
Roshan Sethi is a physician-turned-screenwriter and television producer best known for co-creating the medical drama series "The Resident."
-
E.
Asheem Chandna
Asheem Chandna is a prominent venture capitalist known for investing in and advising leading enterprise technology and cybersecurity startups.
- 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_69d8278adc7c8190a9218d69bce3c4e6 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de62432fb48190b153805b85c4f2d2 |
completed | April 14, 2026, 3:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd3253fc2c8190ba2da6fe6a910d85 |
completed | May 8, 2026, 12:46 a.m. |
Created at: April 10, 2026, 1:08 a.m.