Triple
T16193655
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Girls5eva |
E393004
|
entity |
| Predicate | portrayedBy |
P1507
|
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: [Girls5eva, portrayedBy, Busy Philipps]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Busy Philipps Context triple: [Girls5eva, portrayedBy, 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.
Aidy Bryant
Aidy Bryant is an American actress and comedian best known for her work on "Saturday Night Live" and the Hulu series "Shrill."
-
C.
Maya Erskine
Maya Erskine is an American actress, writer, and comedian best known for co-creating and starring in the cringe-comedy series "PEN15."
-
D.
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."
-
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 (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_69d87f1e49ac8190a311b54d32990576 |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e222d74d788190a637fdb9b4f184b9 |
completed | April 17, 2026, 12:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffff0bfd08819083afc4bea1b99aad |
completed | May 10, 2026, 3:44 a.m. |
Created at: April 10, 2026, 5:02 a.m.