Triple
T19514171
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maya Forbes |
E488234
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Maya Forbes |
—
|
NE NERFINISHED |
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: Maya Forbes | Statement: [Maya Forbes, name, Maya Forbes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maya Forbes Context triple: [Maya Forbes, name, Maya Forbes]
-
A.
Maya Forbes
chosen
Maya Forbes is an American screenwriter, director, and producer known for films such as "Infinitely Polar Bear" and for her work on television series like "The Larry Sanders Show."
-
B.
Maya Fisher
Maya Fisher is a minor character from the television series "Six Feet Under," known as the young daughter of main character Nate Fisher.
-
C.
Maya Wilkes
Maya Wilkes is a central character on the sitcom "Girlfriends," known for her sharp wit, strong opinions, and journey balancing friendship, family, and career.
-
D.
Maya Bishop
Maya Bishop is a driven and skilled firefighter and former Olympic athlete who serves as a central protagonist and eventual captain on the television drama "Station 19."
-
E.
Maya Imhoof
Maya Imhoof is a film producer best known for her work on the acclaimed Swiss drama "The Boat Is Full."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8da8bec819081f400199491ccc3 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6359a7070819099d925447c80bf23 |
completed | April 20, 2026, 2:18 p.m. |
Created at: April 10, 2026, 1:40 p.m.