Triple
T4654541
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sophie Devereaux |
E102376
|
entity |
| Predicate | worksWith |
P398
|
FINISHED |
| Object | Nate Ford |
E457324
|
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: Nate Ford | Statement: [Sophie Devereaux, worksWith, Nate Ford]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nate Ford Context triple: [Sophie Devereaux, worksWith, Nate Ford]
-
A.
Nate Ford
chosen
Nate Ford is the brilliant but morally conflicted former insurance investigator who leads the crew of con artists in the television series "Leverage."
-
B.
Nate Morgan
Nate Morgan is a musician best known as a member of the American funk band Rufus.
-
C.
Nate Cooper
Nate Cooper is a character in the film "The Devil Wears Prada," known as the boyfriend of protagonist Andy Sachs who represents her pre-fashion-world life and values.
-
D.
Nate Heller
Nate Heller is a film composer and songwriter known for his emotionally resonant scores for movies such as "A Beautiful Day in the Neighborhood" and "Can You Ever Forgive Me?".
-
E.
Nate Farley
Nate Farley is an American rock guitarist best known for his work in the indie and alternative scenes, including his tenure with The Breeders.
- 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_69bd43d71a308190afea7280841b0de8 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd6317ba70819089145766d3462e57 |
completed | March 20, 2026, 3:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be0378825881908fe3214f60be579e |
completed | March 21, 2026, 2:33 a.m. |
Created at: March 20, 2026, 1:14 p.m.