Triple
T3172998
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Noah Shebib |
E66396
|
entity |
| Predicate | parent |
P120
|
FINISHED |
| Object |
Ted Shebib
Ted Shebib is the father of Canadian record producer and songwriter Noah "40" Shebib.
|
E333040
|
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: Ted Shebib | Statement: [Noah Shebib, parent, Ted Shebib]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ted Shebib Context triple: [Noah Shebib, parent, Ted Shebib]
-
A.
Dan Morgenstern
Dan Morgenstern is an American jazz historian, critic, and archivist renowned for his leadership of the Institute of Jazz Studies and his Grammy-winning liner notes.
-
B.
Andrew Barto
Andrew Barto is an American computer scientist and a pioneering researcher in reinforcement learning, known for co-authoring the influential textbook "Reinforcement Learning: An Introduction."
-
C.
Jeffrey Auerbach
Jeffrey Auerbach is a film producer best known for his work on the stop-motion animated feature "Corpse Bride."
-
D.
Steven Baigelman
Steven Baigelman is an American screenwriter and producer known for his work on biographical and crime dramas in film and television.
-
E.
Stephen Schaffer
Stephen Schaffer is a film editor best known for his work on major animated features, including Pixar's acclaimed movie WALL-E.
- 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: Ted Shebib Triple: [Noah Shebib, parent, Ted Shebib]
Generated description
Ted Shebib is the father of Canadian record producer and songwriter Noah "40" Shebib.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ted Shebib Target entity description: Ted Shebib is the father of Canadian record producer and songwriter Noah "40" Shebib.
-
A.
Dan Morgenstern
Dan Morgenstern is an American jazz historian, critic, and archivist renowned for his leadership of the Institute of Jazz Studies and his Grammy-winning liner notes.
-
B.
Andrew Barto
Andrew Barto is an American computer scientist and a pioneering researcher in reinforcement learning, known for co-authoring the influential textbook "Reinforcement Learning: An Introduction."
-
C.
Jeffrey Auerbach
Jeffrey Auerbach is a film producer best known for his work on the stop-motion animated feature "Corpse Bride."
-
D.
Steven Baigelman
Steven Baigelman is an American screenwriter and producer known for his work on biographical and crime dramas in film and television.
-
E.
Stephen Schaffer
Stephen Schaffer is a film editor best known for his work on major animated features, including Pixar's acclaimed movie WALL-E.
- 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_69ad8586a34c8190944c63ec11a8de1a |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada66facf881908b9ec687d68ce91b |
completed | March 8, 2026, 4:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b235edf7708190b79605a05baf1711 |
completed | March 12, 2026, 3:41 a.m. |
| NEDg | Description generation | batch_69b236e61ae88190a76b942c6cddff41 |
completed | March 12, 2026, 3:45 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b23770ed4c8190b5d929cc95a286a0 |
completed | March 12, 2026, 3:48 a.m. |
Created at: March 8, 2026, 3:06 p.m.