Triple
T14104755
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gina Torres |
E339476
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Gina Torres |
E339476
|
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: Gina Torres | Statement: [Gina Torres, name, Gina Torres]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gina Torres Context triple: [Gina Torres, name, Gina Torres]
-
A.
Gina Torres
chosen
Gina Torres is an American actress known for her roles in television series such as "Suits," "Firefly," and "Hannibal."
-
B.
Celeste Van Dien
Celeste Van Dien is the daughter of American actress Catherine Oxenberg and actor Casper Van Dien.
-
C.
Amy Acker
Amy Acker is an American actress best known for her roles in television series such as "Angel," "Person of Interest," and "Dollhouse."
-
D.
Carla Gugino
Carla Gugino is an American actress known for her versatile film and television roles, including prominent performances in projects like "Spy Kids," "Sin City," and "The Haunting of Hill House."
-
E.
Melissa Cobb
Melissa Cobb is an American film producer best known for her work on major animated features, including the Kung Fu Panda franchise.
- 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_69d81c69b5c8819094aa1abf18302908 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de5fbd02888190bf07fd6d8769b61c |
completed | April 14, 2026, 3:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcf7de2af88190b9ed8cfeece74e50 |
completed | May 7, 2026, 8:36 p.m. |
Created at: April 9, 2026, 10:22 p.m.