Triple
T14478459
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Judge |
E359036
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Vera Farmiga |
E201564
|
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: Vera Farmiga | Statement: [The Judge, starring, Vera Farmiga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vera Farmiga Context triple: [The Judge, starring, Vera Farmiga]
-
A.
Vera Farmiga
chosen
Vera Farmiga is an American actress known for her acclaimed performances in films such as "The Conjuring" series and "Up in the Air," as well as the TV series "Bates Motel."
-
B.
Taissa Farmiga
Taissa Farmiga is an American actress best known for her recurring roles in the horror anthology series "American Horror Story" and films such as "The Nun."
-
C.
Lena Olin
Lena Olin is a Swedish actress known for her acclaimed film and television roles, including performances in "The Unbearable Lightness of Being," "Enemies, A Love Story," and "Alias."
-
D.
Maria Bello
Maria Bello is an American actress known for her versatile roles in film and television, including performances in projects like "A History of Violence," "ER," and "NCIS."
-
E.
Maura West
Maura West is an American actress best known for her long-running, Emmy-winning work in daytime soap operas.
- 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_69d827966698819082e140837737501d |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de9248edb48190a74eb032aeaac027 |
completed | April 14, 2026, 7:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd64a257488190818c65c1cc84c4b5 |
completed | May 8, 2026, 4:20 a.m. |
Created at: April 10, 2026, 1:20 a.m.