Triple
T6953607
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eleanor McCoy |
E161186
|
entity |
| Predicate | hasRelative |
P367
|
FINISHED |
| Object | Sanaa Lathan |
E31721
|
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: Sanaa Lathan | Statement: [Eleanor McCoy, hasRelative, Sanaa Lathan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sanaa Lathan Context triple: [Eleanor McCoy, hasRelative, Sanaa Lathan]
-
A.
Sanaa Lathan
chosen
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."
-
B.
Jordana Brewster
Jordana Brewster is a Panamanian-American actress best known for her role as Mia Toretto in the Fast & Furious film franchise.
-
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 Ari Parker
Nicole Ari Parker is an American actress known for her roles in film and television, including prominent performances in romantic comedies and dramas.
-
E.
Gabrielle Union
Gabrielle Union is an American actress, author, and producer known for her roles in films like "Bring It On" and "Bad Boys II" as well as the TV series "Being Mary Jane."
- 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_69c68852a9a0819097797e31d492e273 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dacca12481908942ba793a104cc3 |
completed | March 27, 2026, 7:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7eeb79b9c819084738c207aa5c62d |
completed | March 28, 2026, 3:07 p.m. |
Created at: March 27, 2026, 2:29 p.m.