Triple
T12833778
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flesh and the Devil |
E306852
|
entity |
| Predicate | hasGretaGarboBreakthroughRole |
P42600
|
FINISHED |
| Object | true |
—
|
LITERAL 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: true | Statement: [Flesh and the Devil, hasGretaGarboBreakthroughRole, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGretaGarboBreakthroughRole Context triple: [Flesh and the Devil, hasGretaGarboBreakthroughRole, true]
-
A.
hasGingerRogersRole
Indicates that an entity is assigned or associated with a role specifically identified as the "Ginger Rogers" role in a given context or production.
-
B.
hasJoanFontaineRole
Indicates that an entity has a role played by Joan Fontaine in a film, television, or theatrical production.
-
C.
debutAsLeadActressYear
Indicates the year in which an entity first made her debut as a lead actress.
-
D.
leadActorBreakthrough
chosen
Indicates that the actor had a breakthrough or career-defining leading role in the referenced work or context.
-
E.
filmDebutInHollywoodFor
Indicates that one entity made its first appearance or debut in Hollywood through the other entity (such as a specific film or role).
- F. None of above.
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_69d7bdf52b94819096d6f0ba4ab50a98 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9714208f881908f7f8a921362909a |
completed | April 10, 2026, 9:53 p.m. |
| PD | Predicate disambiguation | batch_69d96fa08cd481909a946046ba63809f |
completed | April 10, 2026, 9:46 p.m. |
Created at: April 9, 2026, 5:34 p.m.