Triple
T10391135
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MARV |
E244893
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Layer Cake |
E53909
|
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: Layer Cake | Statement: [MARV, notableWork, Layer Cake]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Layer Cake Context triple: [MARV, notableWork, Layer Cake]
-
A.
Layer Cake
chosen
Layer Cake is a 2004 British crime thriller film directed by Matthew Vaughn, known for its stylish depiction of London’s criminal underworld and for helping launch Daniel Craig to wider fame.
-
B.
Beecake
Beecake is a Scottish alternative rock band fronted by actor and musician Billy Boyd.
-
C.
Snow Cake
Snow Cake is a 2006 Canadian drama film starring Sigourney Weaver and Carrie-Anne Moss that explores the relationship between a reserved man and an autistic woman he meets after a tragic accident.
-
D.
Cake
"Cake" is a 2014 drama film in which Jennifer Aniston stars as a woman struggling with chronic pain and grief, featuring Mamie Gummer in a supporting role.
-
E.
Layer Cake (novel)
Layer Cake (novel) is a 2000 British crime thriller by J.J. Connolly that follows an unnamed London cocaine dealer navigating the dangerous criminal underworld as he plans to retire from the drug trade.
- 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_69d381b5116081908d85227bab6d3c0c |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4e9b5b43081908641a5abfb08dc2b |
completed | April 7, 2026, 11:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d7fbae9a9c81908178fca68eb142b6 |
completed | April 9, 2026, 7:19 p.m. |
Created at: April 6, 2026, 12:06 p.m.