Triple
T8516559
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Andrew Form |
E201585
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object | Jordana Brewster |
E259266
|
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: Jordana Brewster | Statement: [Andrew Form, spouse, Jordana Brewster]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jordana Brewster Context triple: [Andrew Form, spouse, Jordana Brewster]
-
A.
Jordana Brewster
chosen
Jordana Brewster is a Panamanian-American actress best known for her role as Mia Toretto in the Fast & Furious film franchise.
-
B.
Sanaa Lathan
Sanaa Lathan is an American actress known for her work in film, television, and voice acting, including prominent roles in movies like "Love & Basketball" and "Brown Sugar."
-
C.
Michelle Rodriguez
Michelle Rodriguez is an American actress best known for her tough, action-oriented roles, particularly as Letty Ortiz in the Fast & Furious film franchise.
-
D.
Nicole Beharie
Nicole Beharie is an American actress known for her powerful performances in film and television, including notable roles in projects like "Shame," "42," and the series "Sleepy Hollow."
-
E.
Kate Bosworth
Kate Bosworth is an American actress best known for her roles in films such as "Blue Crush" and "Superman Returns."
- 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_69ca8321bb44819081b74df0b710276d |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe62550908190af882019d68a904a |
completed | March 31, 2026, 3:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce4e65419481909e787066fd069565 |
completed | April 2, 2026, 11:09 a.m. |
Created at: March 30, 2026, 6:15 p.m.