Triple
T3276947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moana |
E68779
|
entity |
| Predicate | storyBy |
P1955
|
FINISHED |
| Object |
Aaron Kandell
Aaron Kandell is an American screenwriter best known for co-writing Disney's animated feature film "Moana."
|
E450968
|
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: Aaron Kandell | Statement: [Moana, storyBy, Aaron Kandell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Aaron Kandell Context triple: [Moana, storyBy, Aaron Kandell]
-
A.
Nathan Juran
Nathan Juran was an Academy Award–winning art director and later film director known for his work on classic Hollywood productions and science fiction and fantasy films.
-
B.
Jonathan Kaplan
Jonathan Kaplan is an American film and television director best known for his work on the acclaimed 1988 courtroom drama "The Accused."
-
C.
Ian Kahn
Ian Kahn is an American actor best known for playing George Washington on the television series "Turn: Washington's Spies."
-
D.
Don Katz
Don Katz is an American entrepreneur and author best known as the founder of the audiobook and spoken-word entertainment company Audible.
-
E.
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.
- 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: Aaron Kandell Triple: [Moana, storyBy, Aaron Kandell]
Generated description
Aaron Kandell is an American screenwriter best known for co-writing Disney's animated feature film "Moana."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Aaron Kandell Target entity description: Aaron Kandell is an American screenwriter best known for co-writing Disney's animated feature film "Moana."
-
A.
Nathan Juran
Nathan Juran was an Academy Award–winning art director and later film director known for his work on classic Hollywood productions and science fiction and fantasy films.
-
B.
Jonathan Kaplan
Jonathan Kaplan is an American film and television director best known for his work on the acclaimed 1988 courtroom drama "The Accused."
-
C.
Ian Kahn
Ian Kahn is an American actor best known for playing George Washington on the television series "Turn: Washington's Spies."
-
D.
Don Katz
Don Katz is an American entrepreneur and author best known as the founder of the audiobook and spoken-word entertainment company Audible.
-
E.
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.
- 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_69ad859b54f881909bf530d549caf2fd |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb0128f08819084644f3c8fda2596 |
completed | March 8, 2026, 5:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdacbe49ac81908f014539a1147a4f |
completed | March 20, 2026, 8:23 p.m. |
| NEDg | Description generation | batch_69bdb31f41248190977721cc67040df7 |
completed | March 20, 2026, 8:50 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bdb37017d081908d33528dc0d98fff |
completed | March 20, 2026, 8:52 p.m. |
Created at: March 8, 2026, 3:10 p.m.