Triple
T18354652
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Desire (1936 film) |
E439756
|
entity |
| Predicate | hasMarleneDietrichRoleType |
P130780
|
FINISHED |
| Object | sophisticated jewel thief |
—
|
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: sophisticated jewel thief | Statement: [Desire (1936 film), hasMarleneDietrichRoleType, sophisticated jewel thief]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMarleneDietrichRoleType Context triple: [Desire (1936 film), hasMarleneDietrichRoleType, sophisticated jewel thief]
-
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.
hasElizabethTaylorRole
Indicates that an entity has a role that was originally played by, associated with, or famously portrayed by Elizabeth Taylor.
-
D.
MarilynMonroeRoleType
Indicates the type or category of role associated with Marilyn Monroe in a given context.
-
E.
hasShirleyTempleRoleType
Indicates that an entity has a specific type or category of role related to Shirley Temple.
- F. None of above. chosen
Provenance (4 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_69d8b918221c8190a9f7b563d64ac677 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e516d458148190849ed28fa90eb92b |
completed | April 19, 2026, 5:54 p.m. |
| PD | Predicate disambiguation | batch_69e44fed3fdc81908f4ed6a81db42416 |
completed | April 19, 2026, 3:45 a.m. |
| PDg | Predicate description generation | batch_69e451a1bda48190a9cd1db436d4be62 |
completed | April 19, 2026, 3:53 a.m. |
Created at: April 10, 2026, 10:37 a.m.