Triple
T32286419
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oskar Werner as Jules |
E824841
|
entity |
| Predicate | originalTitleOfFilm |
P6930
|
FINISHED |
| Object | Jules et Jim |
—
|
NE NERFINISHED |
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: Jules et Jim | Statement: [Oskar Werner as Jules, originalTitleOfFilm, Jules et Jim]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originalTitleOfFilm Context triple: [Oskar Werner as Jules, originalTitleOfFilm, Jules et Jim]
-
A.
originalTitleName
Indicates that one entity is the original or primary title name associated with another entity.
-
B.
originalTitleOfWork
chosen
Indicates that one work is the original title under which another work was first created, published, or released.
-
C.
originalTitleUsedIn
Indicates that an entity’s original title is used in or associated with a particular work, edition, or context.
-
D.
originalLanguageTitle
Indicates the title of a work as it appears in its original language of creation or publication.
-
E.
originallyTitleOf
Indicates that one title is the original title from which another work, edition, or localized title is derived.
- 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_69f349101b788190b4f14884dc7d1ed2 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a0189f3f0248190a1b018164d18e6f5 |
completed | May 11, 2026, 7:49 a.m. |
| PD | Predicate disambiguation | batch_6a0187ee0920819097047bb55e1f9506 |
completed | May 11, 2026, 7:40 a.m. |
Created at: May 1, 2026, 12:43 a.m.