Triple
T19579544
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ken Scott |
E489949
|
entity |
| Predicate | wrote |
P2831
|
FINISHED |
| Object | The Grand Seduction |
—
|
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: The Grand Seduction | Statement: [Ken Scott, wrote, The Grand Seduction]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: The Grand Seduction Context triple: [Ken Scott, wrote, The Grand Seduction]
-
A.
The Grand Seduction
chosen
The Grand Seduction is a 2013 Canadian comedy film about a small fishing village’s elaborate scheme to charm a doctor into staying in their community.
-
B.
A Soft Seduction
"A Soft Seduction" is a song by the American alternative rock band Feelings.
-
C.
The Temptress
The Temptress is a 1926 silent romantic drama film starring Greta Garbo and directed by Fred Niblo, known for its tale of destructive passion and moral downfall.
-
D.
16th Seduction
"16th Seduction" is a crime thriller novel by James Patterson and Maxine Paetro, following the Women's Murder Club as they investigate a deadly bombing and confront personal betrayals.
-
E.
Siduction
Siduction is a Linux distribution derived from Debian, known for its rolling-release model and focus on providing an up-to-date, user-friendly desktop environment.
- 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_69d8e8dd9374819098e36349b3211663 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6402763b8819099e535979f094f9d |
completed | April 20, 2026, 3:03 p.m. |
Created at: April 10, 2026, 1:42 p.m.