Triple
T16007650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Olivia Dunham |
E388258
|
entity |
| Predicate | portrayedBy |
P1507
|
FINISHED |
| Object | Anna Torv |
E393692
|
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: Anna Torv | Statement: [Olivia Dunham, portrayedBy, Anna Torv]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anna Torv Context triple: [Olivia Dunham, portrayedBy, Anna Torv]
-
A.
Anna Torv
chosen
Anna Torv is an Australian actress best known for her lead role as FBI agent Olivia Dunham in the science fiction television series "Fringe."
-
B.
Kiernan Shipka
Kiernan Shipka is an American actress best known for her leading roles in the series Mad Men and Chilling Adventures of Sabrina.
-
C.
Emily Alyn Lind
Emily Alyn Lind is an American actress known for her roles in film and television, including playing the young Amanda Clarke on the TV series "Revenge."
-
D.
Lauren Ambrose
Lauren Ambrose is an American actress best known for her critically acclaimed role as Claire Fisher on the HBO drama series "Six Feet Under."
-
E.
Elisha Cuthbert
Elisha Cuthbert is a Canadian actress known for her roles in film and television, including prominent parts in series like "24" and various comedy and thriller movies.
- 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_69d86dabcb7c8190b6a39d6831d2fa1b |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e15800e3608190bd3e1123ccc6c326 |
completed | April 16, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffdbca65a4819090109589dba7f7a9 |
completed | May 10, 2026, 1:13 a.m. |
Created at: April 10, 2026, 4:55 a.m.