Triple
T9154560
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Good Fight |
E219675
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Rose Leslie |
E267913
|
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: Rose Leslie | Statement: [The Good Fight, starring, Rose Leslie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rose Leslie Context triple: [The Good Fight, starring, Rose Leslie]
-
A.
Rose Leslie
chosen
Rose Leslie is a Scottish actress best known for her roles in the TV series "Game of Thrones" and "Downton Abbey," as well as various film and television projects.
-
B.
Katheryn Winnick
Katheryn Winnick is a Canadian actress best known for her role as the fierce shield-maiden Lagertha in the television series "Vikings" and for appearances in various film and TV productions.
-
C.
Tessa Menzies
Tessa Menzies is a child of California politician and governor Gavin Newsom.
-
D.
Jane Wenham
Jane Wenham was a British actress known for her work in mid-20th-century film, television, and theatre.
-
E.
Gwyneth Jones
Gwyneth Jones is a British science fiction and fantasy author known for her intellectually challenging, feminist-themed works and multiple award-winning novels.
- 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_69ca83e25418819093c6503deeaf30de |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca96f87ac8190b2fc6d2b2834c1b6 |
completed | April 1, 2026, 5:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0484e580c8190944ad76f6ef0be9d |
completed | April 3, 2026, 11:07 p.m. |
Created at: March 30, 2026, 7:20 p.m.