Triple
T21889375
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruth Fisher |
E540495
|
entity |
| Predicate | relative |
P37
|
FINISHED |
| Object | Maya Fisher |
—
|
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 Fisher | Statement: [Ruth Fisher, relative, Maya Fisher]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maya Fisher Context triple: [Ruth Fisher, relative, Maya Fisher]
-
A.
Maya Fisher
chosen
Maya Fisher is a minor character from the television series "Six Feet Under," known as the young daughter of main character Nate Fisher.
-
B.
Maya Bennett
Maya Bennett is the daughter of neonatal surgeon Dr. Naomi Bennett in the television series "Private Practice."
-
C.
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."
-
D.
Maya Corwin
Maya Corwin is the daughter of American wildlife biologist and television host Jeff Corwin.
-
E.
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.
- 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_69e0c47a95908190ae3e19b716accb3d |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f118ef2b648190bbd78f6b3958d2ee |
completed | April 28, 2026, 8:30 p.m. |
Created at: April 16, 2026, 7:06 p.m.