Triple
T20819804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Legion (2010 film) |
E512542
|
entity |
| Predicate | stars |
P1956
|
FINISHED |
| Object | Kate Walsh |
—
|
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: Kate Walsh | Statement: [Legion (2010 film), stars, Kate Walsh]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kate Walsh Context triple: [Legion (2010 film), stars, Kate Walsh]
-
A.
Kate Walsh
chosen
Kate Walsh is an American actress best known for her role as Dr. Addison Montgomery on the television series Grey's Anatomy and its spin-off Private Practice.
-
B.
Jorja Fox
Jorja Fox is an American actress best known for her long-running role as Sara Sidle on the television series CSI: Crime Scene Investigation.
-
C.
Kyra Sedgwick
Kyra Sedgwick is an American actress and producer best known for her award-winning lead role as Deputy Chief Brenda Leigh Johnson on the crime drama series "The Closer."
-
D.
Melissa Cobb
Melissa Cobb is an American film producer best known for her work on major animated features, including the Kung Fu Panda franchise.
-
E.
Elizabeth Berkley
Elizabeth Berkley is an American actress best known for her roles in the TV series "Saved by the Bell" and the film "Showgirls."
- 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_69e0b4ce39108190a6e8e5df4f1c8dc5 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c2f6a65481909a0df78616e185e4 |
completed | April 21, 2026, 12:21 a.m. |
Created at: April 16, 2026, 12:41 p.m.