Triple
T21117161
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sick Note |
E520328
|
entity |
| Predicate | creator |
P184
|
FINISHED |
| Object |
Nat Saunders
Nat Saunders is a British comedy writer and producer best known for co-creating the television sitcom "Sick Note."
|
E1468117
|
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: Nat Saunders | Statement: [Sick Note, creator, Nat Saunders]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nat Saunders Context triple: [Sick Note, creator, Nat Saunders]
-
A.
William Saunders
William Saunders was a prominent 19th-century landscape architect and horticulturist known for designing notable American cemeteries and public grounds.
-
B.
Max Dennison
Max Dennison is the skeptical teenage protagonist of the Halloween-themed fantasy film "Hocus Pocus," whose actions accidentally resurrect three witches in Salem.
-
C.
Sylvan Morris
Sylvan Morris is a Jamaican audio engineer known for his influential work on classic reggae and dub recordings at the legendary Studio One label.
-
D.
Leo Harrington
Leo Harrington is an American logician and mathematician known for his influential work in mathematical logic, particularly in recursion theory and set theory.
-
E.
Sam Sanger
Sam Sanger is a notable member of the Sanger family, recognized for his prominence within that lineage.
- 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: Nat Saunders Triple: [Sick Note, creator, Nat Saunders]
Generated description
Nat Saunders is a British comedy writer and producer best known for co-creating the television sitcom "Sick Note."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nat Saunders Target entity description: Nat Saunders is a British comedy writer and producer best known for co-creating the television sitcom "Sick Note."
-
A.
William Saunders
William Saunders was a prominent 19th-century landscape architect and horticulturist known for designing notable American cemeteries and public grounds.
-
B.
Max Dennison
Max Dennison is the skeptical teenage protagonist of the Halloween-themed fantasy film "Hocus Pocus," whose actions accidentally resurrect three witches in Salem.
-
C.
Sylvan Morris
Sylvan Morris is a Jamaican audio engineer known for his influential work on classic reggae and dub recordings at the legendary Studio One label.
-
D.
Leo Harrington
Leo Harrington is an American logician and mathematician known for his influential work in mathematical logic, particularly in recursion theory and set theory.
-
E.
Sam Sanger
Sam Sanger is a notable member of the Sanger family, recognized for his prominence within that lineage.
- 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_69e0b50a623881909c0bbaf4f2c055e7 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e721078ac48190980441b6ada0e2b4 |
completed | April 21, 2026, 7:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0965e5b04c8190addaa49cb9f6ad06 |
completed | May 17, 2026, 6:53 a.m. |
| NEDg | Description generation | batch_6a0969cd36b081909e24ab92c0b2ab0a |
completed | May 17, 2026, 7:10 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a096b373290819082f45e68772a81e4 |
completed | May 17, 2026, 7:16 a.m. |
Created at: April 16, 2026, 2:55 p.m.