Triple
T11347260
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mia Kirshner |
E268749
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Mia Kirshner |
E268749
|
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: Mia Kirshner | Statement: [Mia Kirshner, name, Mia Kirshner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mia Kirshner Context triple: [Mia Kirshner, name, Mia Kirshner]
-
A.
Mia Kirshner
chosen
Mia Kirshner is a Canadian actress known for her dark, nuanced performances in film and television, including her notable role in the crime drama "The Black Dahlia."
-
B.
Samara Weaving
Samara Weaving is an Australian actress known for her roles in film and television, particularly in horror-comedy and thriller projects such as "Ready or Not" and "The Babysitter."
-
C.
Madelaine Petsch
Madelaine Petsch is an American actress best known for playing Cheryl Blossom on the television series "Riverdale."
-
D.
Deborah Anne Mazar
Deborah Anne Mazar is an American actress known for her sharp-tongued, tough-girl roles in film and television, including notable appearances in "Goodfellas," "Entourage," and "Younger."
-
E.
Jessica Lucas
Jessica Lucas is a Canadian actress known for her roles in film and television, including prominent appearances in projects like the monster movie "Cloverfield."
- 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_69d6aacbe18081909e5fadb50082dd96 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7ea214bb88190bb66f7fd3ef73081 |
completed | April 9, 2026, 6:04 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e55649a9188190911608fef5894bd8 |
completed | April 19, 2026, 10:25 p.m. |
Created at: April 8, 2026, 9:33 p.m.