Triple
T13665801
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 47 Meters Down: Uncaged |
E327115
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Sophie Nélisse |
E1056115
|
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: Sophie Nélisse | Statement: [47 Meters Down: Uncaged, stars, Sophie Nélisse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sophie Nélisse Context triple: [47 Meters Down: Uncaged, stars, Sophie Nélisse]
-
A.
Sophie Nélisse
chosen
Sophie Nélisse is a Canadian actress best known for her acclaimed lead role as Liesel Meminger in the film adaptation of "The Book Thief."
-
B.
Isabelle Nélisse
Isabelle Nélisse is a Canadian actress known for her roles in psychologically intense films and television series, often portraying complex and troubled young characters.
-
C.
Maria Riva
Maria Riva is a German-American actress and author best known as the daughter and biographer of film legend Marlene Dietrich.
-
D.
Joey King
Joey King is an American actress known for her roles in films such as "The Kissing Booth" series, "The Act," and various other television and movie projects.
-
E.
Anya Taylor-Joy
Anya Taylor-Joy is an award-winning actress known for her breakout role in "The Queen's Gambit" and performances in films such as "The Witch," "Split," and "Last Night in Soho."
- 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_69d8076d8270819092afc2f0e9c359a8 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbc623fcc88190bbad97541c040b7a |
completed | April 12, 2026, 4:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7b8c8e7988190bcd338bdeba0ae60 |
completed | May 3, 2026, 9:06 p.m. |
Created at: April 9, 2026, 9:52 p.m.