Triple
T6278091
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | All That |
E140710
|
entity |
| Predicate | hasNotableAlumnus |
P51
|
FINISHED |
| Object |
Mark Saul
Mark Saul is an American actor and musician best known for his work on television, including sketch comedy and drama series roles.
|
E582597
|
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: Mark Saul | Statement: [All That, hasNotableAlumnus, Mark Saul]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mark Saul Context triple: [All That, hasNotableAlumnus, Mark Saul]
-
A.
Andrew Saks
Andrew Saks was an American businessman and retailer best known as the founder of the luxury department store Saks Fifth Avenue.
-
B.
David Sacks
David Sacks is a technology entrepreneur and investor best known as the founding COO of PayPal, founder of Geni.com and Yammer, and a prominent figure in Silicon Valley venture capital.
-
C.
Michael Sacks
Michael Sacks is an American actor best known for his role as Billy Pilgrim in the film adaptation of Kurt Vonnegut’s "Slaughterhouse-Five."
-
D.
Daniel Scharf
Daniel Scharf is a film producer best known for his work on the influential 1992 Australian drama "Romper Stomper."
-
E.
Ian Kahn
Ian Kahn is an American actor best known for playing George Washington on the television series "Turn: Washington's Spies."
- 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: Mark Saul Triple: [All That, hasNotableAlumnus, Mark Saul]
Generated description
Mark Saul is an American actor and musician best known for his work on television, including sketch comedy and drama series roles.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mark Saul Target entity description: Mark Saul is an American actor and musician best known for his work on television, including sketch comedy and drama series roles.
-
A.
Andrew Saks
Andrew Saks was an American businessman and retailer best known as the founder of the luxury department store Saks Fifth Avenue.
-
B.
David Sacks
David Sacks is a technology entrepreneur and investor best known as the founding COO of PayPal, founder of Geni.com and Yammer, and a prominent figure in Silicon Valley venture capital.
-
C.
Michael Sacks
Michael Sacks is an American actor best known for his role as Billy Pilgrim in the film adaptation of Kurt Vonnegut’s "Slaughterhouse-Five."
-
D.
Daniel Scharf
Daniel Scharf is a film producer best known for his work on the influential 1992 Australian drama "Romper Stomper."
-
E.
Ian Kahn
Ian Kahn is an American actor best known for playing George Washington on the television series "Turn: Washington's Spies."
- 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_69c008cc158881908df6ec94a911c736 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063daec108190859d1d5dbafd5b42 |
completed | March 22, 2026, 9:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c5194e9b3881908188f004c4a03a09 |
completed | March 26, 2026, 11:32 a.m. |
| NEDg | Description generation | batch_69c529a7ea1c819083f1566a35fc7c13 |
completed | March 26, 2026, 12:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c5850ff07081908a3512cf6de367cd |
completed | March 26, 2026, 7:12 p.m. |
Created at: March 22, 2026, 4:26 p.m.