Triple
T7230453
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BlacKkKlansman |
E154886
|
entity |
| Predicate | castMember |
P1668
|
FINISHED |
| Object | Laura Harrier |
E538156
|
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: Laura Harrier | Statement: [BlacKkKlansman, castMember, Laura Harrier]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Laura Harrier Context triple: [BlacKkKlansman, castMember, Laura Harrier]
-
A.
Laura Harrier
chosen
Laura Harrier is an American actress and model best known for her roles in films like "Spider-Man: Homecoming" and "BlacKkKlansman."
-
B.
Stephanie Hsu
Stephanie Hsu is an American actress best known for her acclaimed, genre-bending performance as Joy/Jobu Tupaki in the film "Everything Everywhere All at Once."
-
C.
Brenda Song
Brenda Song is an American actress known for her roles in Disney Channel productions like "The Suite Life of Zack & Cody" and in acclaimed films such as "The Social Network."
-
D.
Maya Van Dien
Maya Van Dien is the daughter of American actress Catherine Oxenberg and actor Casper Van Dien.
-
E.
Dichen Lachman
Dichen Lachman is a Nepalese-Australian actress and producer best known for her genre television roles in series such as Dollhouse, Agents of S.H.I.E.L.D., and The 100.
- 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_69c68811dd1c8190ac460bb39e64e1f0 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ea0d9b6c8190a0b5f0ab8d5cca19 |
completed | March 27, 2026, 8:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7cc22a39481909a2f38014260f302 |
completed | March 28, 2026, 12:40 p.m. |
Created at: March 27, 2026, 2:54 p.m.