Triple
T19648434
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maya Erskine |
E471741
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Maya Erskine |
—
|
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: Maya Erskine | Statement: [Maya Erskine, name, Maya Erskine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maya Erskine Context triple: [Maya Erskine, name, Maya Erskine]
-
A.
Maya Erskine
chosen
Maya Erskine is an American actress, writer, and comedian best known for co-creating and starring in the cringe-comedy series "PEN15."
-
B.
Laura Marano
Laura Marano is an American actress and singer best known for starring as Ally Dawson on the Disney Channel series "Austin & Ally."
-
C.
Alycia Debnam-Carey
Alycia Debnam-Carey is an Australian actress best known for her prominent roles in television series such as Fear the Walking Dead and The 100.
-
D.
Kaitlin Olson
Kaitlin Olson is an American actress and comedian best known for playing Dee Reynolds on the long-running sitcom "It's Always Sunny in Philadelphia."
-
E.
Natalie Bridges
Natalie Bridges is the wife of New Zealand politician and former National Party leader Simon Bridges.
- 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_69d8e51395348190ac1416d46dfc6db0 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e64126cea88190a1a6929f46de4686 |
completed | April 20, 2026, 3:07 p.m. |
Created at: April 10, 2026, 1:44 p.m.