Triple
T22882704
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Drake & Josh |
E567517
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object | Megan Parker |
—
|
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: Megan Parker | Statement: [Drake & Josh, mainCharacter, Megan Parker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Megan Parker Context triple: [Drake & Josh, mainCharacter, Megan Parker]
-
A.
Megan Parker
chosen
Megan Parker is the mischievous, prank-loving younger sister character from the Nickelodeon sitcom "Drake & Josh."
-
B.
Megan Everett
Megan Everett is a writer and producer best known as the wife of Swedish actor Stellan Skarsgård.
-
C.
Megan Reynolds
Megan Reynolds is a fictional character from the film "Breathe In," involved in the drama surrounding a family's emotional and relational tensions.
-
D.
Megan Parlen
Megan Parlen is an American actress best known for her role as Mary-Beth Pepperton on the 1990s teen sitcom "Hang Time."
-
E.
Megan McCloskey
Megan McCloskey is an American journalist known for her investigative reporting, particularly on military and veterans’ issues.
- 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_69e2458a92ec81908fc1cd5f6407d2ab |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f17f5dab048190a09c725dad123472 |
completed | April 29, 2026, 3:47 a.m. |
Created at: April 17, 2026, 3:39 p.m.