Triple
T21251024
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dracula: Dead and Loving It |
E523743
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Amy Yasbeck |
—
|
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: Amy Yasbeck | Statement: [Dracula: Dead and Loving It, starring, Amy Yasbeck]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amy Yasbeck Context triple: [Dracula: Dead and Loving It, starring, Amy Yasbeck]
-
A.
Amy Yasbeck
chosen
Amy Yasbeck is an American actress best known for her comedic roles in films like "Problem Child" and for her work on television.
-
B.
Amy Landecker
Amy Landecker is an American actress best known for her role as Sarah Pfefferman on the television series "Transparent."
-
C.
Ariela Barer
Ariela Barer is an American actor best known for roles in series like Marvel’s Runaways and the drama show Rebel.
-
D.
Rachel Margulies
Rachel Margulies is an individual notable enough to be specifically cited as a bearer of the Margulies surname.
-
E.
Lisa Edelstein
Lisa Edelstein is an American actress and writer best known for her role as Dr. Lisa Cuddy on the television series "House" and for prominent performances in various film and TV dramas and comedies.
- 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_69e0b5146c108190adc9adb73e90abff |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7359e90d881909b3153f1e7213c5c |
completed | April 21, 2026, 8:30 a.m. |
Created at: April 16, 2026, 3:56 p.m.