Triple
T22379595
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scoob! |
E553235
|
entity |
| Predicate | storyBy |
P1955
|
FINISHED |
| Object | Jonathon E. Stewart |
—
|
NE NERFINISHED |
How this triple was built (2 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: Jonathon E. Stewart | Statement: [Scoob!, storyBy, Jonathon E. Stewart]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jonathon E. Stewart Context triple: [Scoob!, storyBy, Jonathon E. Stewart]
-
A.
Jonathan E. Stewart
chosen
Jonathan E. Stewart is a screenwriter and story artist best known for his work on animated feature films, including contributing to the story of Pixar’s "Cars 3."
-
B.
Matthew B. Roberts
Matthew B. Roberts is a television writer and producer best known for his leading creative role on the historical drama series "Outlander."
-
C.
Joshua L. Pearson
Joshua L. Pearson is a film editor best known for his work on the acclaimed music documentary "Summer of Soul (...Or, When the Revolution Could Not Be Televised)."
-
D.
Matthew E. Peters
Matthew E. Peters is a computer scientist and researcher known for co-developing the ELMo deep contextualized word representation model in natural language processing.
-
E.
Matthew E. Peters
Matthew E. Peters is a computer scientist and natural language processing researcher known for his work on large-scale language models and document-level architectures such as Longformer.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e4c03248190a26a5060ea6973ee |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f1582afe6c819093940f9d817c64a8 |
completed | April 29, 2026, 1 a.m. |
Created at: April 16, 2026, 8:45 p.m.