Triple
T12947532
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stuck in Love |
E309807
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Kristen Bell |
E173081
|
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: Kristen Bell | Statement: [Stuck in Love, stars, Kristen Bell]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kristen Bell Context triple: [Stuck in Love, stars, Kristen Bell]
-
A.
Kristen Bell
chosen
Kristen Bell is an American actress and singer best known for her roles in the TV series "Veronica Mars" and "The Good Place," as well as her voice work in Disney animated films.
-
B.
Zooey Deschanel
Zooey Deschanel is an American actress, singer, and songwriter known for her quirky, offbeat roles in films like "500 Days of Summer" and the TV series "New Girl."
-
C.
Kate Bosworth
Kate Bosworth is an American actress best known for her roles in films such as "Blue Crush" and "Superman Returns."
-
D.
Danielle Panabaker
Danielle Panabaker is an American actress best known for her role as Caitlin Snow/Killer Frost in the Arrowverse television series "The Flash."
-
E.
June Tripp
June Tripp was a British actress best known for her leading role in Alfred Hitchcock’s silent thriller "The Lodger: A Story of the London Fog."
- 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_69d7bdfb57a88190836b743e2825feca |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d97e1c67b8819094e5243267f93ce2 |
completed | April 10, 2026, 10:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6af75bc04819098d98c47fca48ac9 |
completed | May 3, 2026, 2:14 a.m. |
Created at: April 9, 2026, 5:43 p.m.