Triple
T6239442
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kristin (TV series) |
E139561
|
entity |
| Predicate | creator |
P184
|
FINISHED |
| Object |
John Markus
John Markus is an American television writer and producer best known for his work on sitcoms such as The Cosby Show and for creating the series Kristin.
|
E582349
|
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: John Markus | Statement: [Kristin (TV series), creator, John Markus]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: John Markus Context triple: [Kristin (TV series), creator, John Markus]
-
A.
Markus Morgenstern
Markus Morgenstern is a mathematician known for his contributions to combinatorics and graph theory.
-
B.
Markus
Markus is the given first name of the renowned abstract expressionist painter Mark Rothko.
-
C.
Markus Wolf
Markus Wolf was a prominent East German spymaster who led the foreign intelligence service of the Stasi and became one of the Cold War’s most influential intelligence chiefs.
-
D.
Jack Tornek
Jack Tornek was a character actor known for small roles in early 20th-century American films, including the 1950s horror movie "Hellgate."
-
E.
Michael Wandmacher
Michael Wandmacher is an American film and television composer known for his work on horror and action projects, including the score for "My Bloody Valentine 3D."
- 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: John Markus Triple: [Kristin (TV series), creator, John Markus]
Generated description
John Markus is an American television writer and producer best known for his work on sitcoms such as The Cosby Show and for creating the series Kristin.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: John Markus Target entity description: John Markus is an American television writer and producer best known for his work on sitcoms such as The Cosby Show and for creating the series Kristin.
-
A.
Markus Morgenstern
Markus Morgenstern is a mathematician known for his contributions to combinatorics and graph theory.
-
B.
Markus
Markus is the given first name of the renowned abstract expressionist painter Mark Rothko.
-
C.
Markus Wolf
Markus Wolf was a prominent East German spymaster who led the foreign intelligence service of the Stasi and became one of the Cold War’s most influential intelligence chiefs.
-
D.
Jack Tornek
Jack Tornek was a character actor known for small roles in early 20th-century American films, including the 1950s horror movie "Hellgate."
-
E.
Michael Wandmacher
Michael Wandmacher is an American film and television composer known for his work on horror and action projects, including the score for "My Bloody Valentine 3D."
- 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_69c008b0e7ac8190808a59573ee646f3 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063048df081909a13d16b6f6bf65d |
completed | March 22, 2026, 9:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c5190e0a6481909e5372334a851770 |
completed | March 26, 2026, 11:31 a.m. |
| NEDg | Description generation | batch_69c51b947088819089242ce511e2c639 |
completed | March 26, 2026, 11:42 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c5843fd33c8190be48371960cfbfbb |
completed | March 26, 2026, 7:08 p.m. |
Created at: March 22, 2026, 4:23 p.m.