Triple
T5358967
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Black Mass (2015 film) |
E102772
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Dakota Johnson |
E201583
|
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: Dakota Johnson | Statement: [Black Mass (2015 film), castMember, Dakota Johnson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dakota Johnson Context triple: [Black Mass (2015 film), castMember, Dakota Johnson]
-
A.
Dakota Johnson
chosen
Dakota Johnson is an American actress best known for starring as Anastasia Steele in the film adaptation of the erotic romance novel "Fifty Shades of Grey" and its sequels.
-
B.
Ruby Rose
Ruby Rose is an Australian model, DJ, and actress known for her androgynous style and roles in action films and television series such as "Orange Is the New Black."
-
C.
Rooney Mara
Rooney Mara is an American actress known for her acclaimed performances in films such as "The Girl with the Dragon Tattoo" and "Carol."
-
D.
Suki Waterhouse
Suki Waterhouse is an English model, actress, and singer known for her fashion work, film roles, and music career.
-
E.
Beanie Feldstein
Beanie Feldstein is an American actress known for her comedic and dramatic roles in films such as "Booksmart" and "Lady Bird," as well as on Broadway.
- 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_69bd43d8f7248190b64c140734b5c9a8 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd8631ca2c8190856258bf340f6e5d |
completed | March 20, 2026, 5:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf21e955a8819094a0b12e42e2d6a6 |
completed | March 21, 2026, 10:55 p.m. |
Created at: March 20, 2026, 2:02 p.m.