Triple
T5321070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jaylah |
E121673
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Sofia Boutella |
E48820
|
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: Sofia Boutella | Statement: [Jaylah, portrayedBy, Sofia Boutella]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sofia Boutella Context triple: [Jaylah, portrayedBy, Sofia Boutella]
-
A.
Sofia Boutella
chosen
Sofia Boutella is an Algerian-French dancer and actress known for her dynamic action roles in films such as "Kingsman: The Secret Service," "Star Trek Beyond," and "The Mummy."
-
B.
Bérénice Marlohe
Bérénice Marlohe is a French actress best known internationally for her role as Sévérine in the James Bond film "Skyfall."
-
C.
Safy Boutella
Safy Boutella is an Algerian musician and composer known for his influential role in modern Algerian music and film scores.
-
D.
Lea Seydoux
Léa Seydoux is a French actress known for her roles in films such as "Blue Is the Warmest Colour," multiple James Bond movies, and various international arthouse and blockbuster productions.
-
E.
Ana de Armas
Ana de Armas is a Cuban-Spanish actress known for her breakout roles in films such as "Blade Runner 2049," "Knives Out," and "Blonde."
- 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_69bd463d956c819088105c3db802c017 |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd85761ec48190879210af116ed8b9 |
completed | March 20, 2026, 5:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf18a4dff48190bce18f0106c7a8e7 |
completed | March 21, 2026, 10:16 p.m. |
Created at: March 20, 2026, 1:59 p.m.