Triple
T9780635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Busy Philipps |
E237359
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Busy Philipps |
E237359
|
NE FINISHED |
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: Busy Philipps | Statement: [Busy Philipps, name, Busy Philipps]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Busy Philipps Context triple: [Busy Philipps, name, Busy Philipps]
-
A.
Busy Philipps
chosen
Busy Philipps is an American actress and television host known for her roles in series like "Freaks and Geeks," "Dawson’s Creek," and "Cougar Town," as well as for her outspoken, comedic presence in pop culture.
-
B.
Maya Erskine
Maya Erskine is an American actress, writer, and comedian best known for co-creating and starring in the cringe-comedy series "PEN15."
-
C.
Molly Gordon
Molly Gordon is an American actress and director known for her roles in films like "Booksmart" and "Good Boys" and the TV series "The Bear."
-
D.
Sara Haines
Sara Haines is an American television host and journalist best known as a co-host of the daytime talk show "The View" and for her work on various ABC programs.
-
E.
Ellie Kemper
Ellie Kemper is an American actress and comedian best known for her roles in the sitcoms "The Office" and "Unbreakable Kimmy Schmidt."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca84d975a08190aab25b02a89bdab3 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cda1b0b15881909ef52d0156148c59 |
completed | April 1, 2026, 10:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1bd2e5f4c81908a3c132df6440947 |
completed | April 5, 2026, 1:38 a.m. |
Created at: March 30, 2026, 8:27 p.m.