Triple
T2644546
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dumbo (2019 film) |
E62952
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Eva Green |
E263424
|
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: Eva Green | Statement: [Dumbo (2019 film), stars, Eva Green]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eva Green Context triple: [Dumbo (2019 film), stars, Eva Green]
-
A.
Eva Green
chosen
Eva Green is a French actress known for her dark, intense performances in film and television, including prominent roles in projects like "Casino Royale" and "Penny Dreadful."
-
B.
Carmen Ejogo
Carmen Ejogo is a British actress and singer known for her versatile film and television roles, including her acclaimed portrayal of Coretta Scott King in the historical drama "Selma."
-
C.
Gemma Arterton
Gemma Arterton is an English actress known for her roles in films such as "St Trinian's," "Quantum of Solace," and "Prince of Persia: The Sands of Time."
-
D.
Bérénice Marlohe
Bérénice Marlohe is a French actress best known internationally for her role as Sévérine in the James Bond film "Skyfall."
-
E.
Rachel Weisz
Rachel Weisz is an Academy Award–winning British actress known for her versatile performances in films such as "The Constant Gardener," "The Mummy," and "The Favourite."
- 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_69ab4c3f2dcc819082df80f5e032f690 |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abd917192081908e7a2cf780a17b83 |
completed | March 7, 2026, 7:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afa050cc408190b2fa81a0f2da06eb |
completed | March 10, 2026, 4:38 a.m. |
Created at: March 6, 2026, 9:53 p.m.