Triple
T2330626
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Turning Red |
E48392
|
entity |
| Predicate | cinematographyBy |
P1953
|
FINISHED |
| Object |
Jonathan Pytko
Jonathan Pytko is a cinematographer best known for his work on the Pixar animated film "Turning Red."
|
E256026
|
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: Jonathan Pytko | Statement: [Turning Red, cinematographyBy, Jonathan Pytko]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jonathan Pytko Context triple: [Turning Red, cinematographyBy, Jonathan Pytko]
-
A.
Jonathan Teplitzky
Jonathan Teplitzky is an Australian film director known for character-driven dramas such as "The Railway Man" and "Burning Man."
-
B.
Andrew Goczkowski
Andrew Goczkowski is an American local government leader serving as the mayor of Des Plaines, Illinois.
-
C.
Alex Prus
Alex Prus is a former professional soccer referee who officiated at the highest levels of Major League Soccer in the United States.
-
D.
Adam Tarnowski
Adam Tarnowski was a Polish diplomat who served as foreign minister for the Polish government-in-exile during World War II.
-
E.
John Wolyniec
John Wolyniec is a former American professional soccer forward best known for his time with the New York/New Jersey MetroStars and New York Red Bulls in Major League Soccer.
- 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: Jonathan Pytko Triple: [Turning Red, cinematographyBy, Jonathan Pytko]
Generated description
Jonathan Pytko is a cinematographer best known for his work on the Pixar animated film "Turning Red."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jonathan Pytko Target entity description: Jonathan Pytko is a cinematographer best known for his work on the Pixar animated film "Turning Red."
-
A.
Jonathan Teplitzky
Jonathan Teplitzky is an Australian film director known for character-driven dramas such as "The Railway Man" and "Burning Man."
-
B.
Andrew Goczkowski
Andrew Goczkowski is an American local government leader serving as the mayor of Des Plaines, Illinois.
-
C.
Alex Prus
Alex Prus is a former professional soccer referee who officiated at the highest levels of Major League Soccer in the United States.
-
D.
Adam Tarnowski
Adam Tarnowski was a Polish diplomat who served as foreign minister for the Polish government-in-exile during World War II.
-
E.
John Wolyniec
John Wolyniec is a former American professional soccer forward best known for his time with the New York/New Jersey MetroStars and New York Red Bulls in Major League Soccer.
- 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_69a88aa308a88190b0b86c011fda7fce |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abc669956881908b8d9784d6a06acf |
completed | March 7, 2026, 6:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae8974ab8c81908ec2bddcc882cf42 |
completed | March 9, 2026, 8:48 a.m. |
| NEDg | Description generation | batch_69ae8a084b388190a6d79df8d94b236d |
completed | March 9, 2026, 8:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae8a895b6c8190bfd064742e3cc4f8 |
completed | March 9, 2026, 8:53 a.m. |
Created at: March 4, 2026, 7:50 p.m.