Triple
T22379587
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scoob! |
E553235
|
entity |
| Predicate | producer |
P490
|
FINISHED |
| Object | Pam Coats |
—
|
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: Pam Coats | Statement: [Scoob!, producer, Pam Coats]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pam Coats Context triple: [Scoob!, producer, Pam Coats]
-
A.
Pam Coats
chosen
Pam Coats is an American film producer best known for her work at Walt Disney Feature Animation, including producing the animated classic "Mulan."
-
B.
Pamela Frank
Pamela Frank is the second wife of singer and civil rights activist Harry Belafonte, known primarily for her long-term marriage to the entertainer.
-
C.
Pamela Frank
Pamela Frank is an acclaimed American violinist renowned for her expressive performances and influential teaching career.
-
D.
Pamela Jenkins
Pamela Jenkins is a fictional character from the Saw horror film franchise, appearing in the movie "Saw VI."
-
E.
Lori Nelson
Lori Nelson was an American film and television actress best known for her roles in 1950s Hollywood productions, including Westerns and science fiction films.
- 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.