Triple
T18533520
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Minx |
E452900
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Amy Landecker |
—
|
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 Landecker | Statement: [Minx, portrayedBy, Amy Landecker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amy Landecker Context triple: [Minx, portrayedBy, Amy Landecker]
-
A.
Amy Landecker
chosen
Amy Landecker is an American actress best known for her role as Sarah Pfefferman on the television series "Transparent."
-
B.
Amy Yasbeck
Amy Yasbeck is an American actress best known for her comedic roles in films like "Problem Child" and for her work on television.
-
C.
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.
-
D.
Claire Lademacher
Claire Lademacher is a German-born bioethics researcher who became a member of the Luxembourg royal family through her marriage to Prince Félix.
-
E.
Lisa Eilbacher
Lisa Eilbacher is an American actress best known for her roles in 1980s films and television series, including prominent appearances in action and drama movies.
- 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_69d8d387b5548190aa030dad2cb4947e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e533ffc38881909f8ee132314f58a3 |
completed | April 19, 2026, 7:58 p.m. |
Created at: April 10, 2026, 11:37 a.m.