Triple
T2142909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coco |
E46999
|
entity |
| Predicate | storyBy |
P1955
|
FINISHED |
| Object |
Jason Katz
Jason Katz is an American screenwriter and story artist best known for his work on Pixar animated films.
|
E335498
|
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: Jason Katz | Statement: [Coco, storyBy, Jason Katz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jason Katz Context triple: [Coco, storyBy, Jason Katz]
-
A.
Don Katz
Don Katz is an American entrepreneur and author best known as the founder of the audiobook and spoken-word entertainment company Audible.
-
B.
Michael Kagan
Michael Kagan is an Israeli technologist and entrepreneur best known as the co-founder and longtime chief technology officer of high-performance networking company Mellanox Technologies.
-
C.
Jason Blumenthal
Jason Blumenthal is an American film producer known for his work on a variety of Hollywood feature films, including the comedy-drama "Troop Zero."
-
D.
Jay Rabinowitz
Jay Rabinowitz is a film editor known for his work on numerous feature films, including the science-fiction thriller "The Adjustment Bureau."
-
E.
Josh Goldstein
Josh Goldstein is a screenwriter best known for co-writing the story for Disney’s adventure film "Jungle Cruise."
- 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: Jason Katz Triple: [Coco, storyBy, Jason Katz]
Generated description
Jason Katz is an American screenwriter and story artist best known for his work on Pixar animated films.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jason Katz Target entity description: Jason Katz is an American screenwriter and story artist best known for his work on Pixar animated films.
-
A.
Don Katz
Don Katz is an American entrepreneur and author best known as the founder of the audiobook and spoken-word entertainment company Audible.
-
B.
Michael Kagan
Michael Kagan is an Israeli technologist and entrepreneur best known as the co-founder and longtime chief technology officer of high-performance networking company Mellanox Technologies.
-
C.
Jason Blumenthal
Jason Blumenthal is an American film producer known for his work on a variety of Hollywood feature films, including the comedy-drama "Troop Zero."
-
D.
Jay Rabinowitz
Jay Rabinowitz is a film editor known for his work on numerous feature films, including the science-fiction thriller "The Adjustment Bureau."
-
E.
Josh Goldstein
Josh Goldstein is a screenwriter best known for co-writing the story for Disney’s adventure film "Jungle Cruise."
- 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_69a88a1933e0819094f18426ed74180f |
completed | March 4, 2026, 7:38 p.m. |
| NER | Named-entity recognition | batch_69abbe206db0819095772af5358dca55 |
completed | March 7, 2026, 5:56 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b24a9a49e481908f1916cbff31d908 |
completed | March 12, 2026, 5:09 a.m. |
| NEDg | Description generation | batch_69b24c5154008190aaaf07333de85370 |
completed | March 12, 2026, 5:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b24cf888288190b02782467c932862 |
completed | March 12, 2026, 5:19 a.m. |
Created at: March 4, 2026, 7:44 p.m.