Triple
T9015742
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Good Thief |
E215588
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object | Ralph Fiennes |
E57559
|
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: Ralph Fiennes | Statement: [The Good Thief, hasCastMember, Ralph Fiennes]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ralph Fiennes Context triple: [The Good Thief, hasCastMember, Ralph Fiennes]
-
A.
Ralph Fiennes
chosen
Ralph Fiennes is an acclaimed English actor and filmmaker known for his intense, nuanced performances in films such as Schindler's List, the Harry Potter series, and The Grand Budapest Hotel.
-
B.
Magnus Fiennes
Magnus Fiennes is a British composer, record producer, and songwriter known for his work in film, television, and pop music.
-
C.
William Fiennes
William Fiennes is an English writer and memoirist best known for his acclaimed books "The Snow Geese" and "The Music Room."
-
D.
Mark Fiennes
Mark Fiennes was an English photographer and illustrator, best known as the father of actors Ralph and Joseph Fiennes.
-
E.
Joseph Fiennes
Joseph Fiennes is an English actor known for his roles in films such as "Shakespeare in Love" and various historical and dramatic productions in both cinema and television.
- 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_69ca83a38aa88190bf1bb80c4548b5e2 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc69fc0e4c819080b60456375f94cd |
completed | April 1, 2026, 12:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfdba8bd8c81909860d561d9d16611 |
completed | April 3, 2026, 3:24 p.m. |
Created at: March 30, 2026, 7:06 p.m.