Triple
T10126039
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Magic Mountain (1982 film) |
E226216
|
entity |
| Predicate | literarySourceAuthorNationality |
P6689
|
FINISHED |
| Object | German |
—
|
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: German | Statement: [The Magic Mountain (1982 film), literarySourceAuthorNationality, German]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: literarySourceAuthorNationality Context triple: [The Magic Mountain (1982 film), literarySourceAuthorNationality, German]
-
A.
literaryAuthor
Indicates that one entity is the author or writer of a literary work represented by the other entity.
-
B.
literarySource
Indicates that one entity serves as the written or literary origin, reference, or basis for another entity.
-
C.
literaryOriginCountry
Indicates the country from which a literary work or literary tradition originally comes.
-
D.
authorNationality
chosen
Indicates the relationship between an author and the country or nationality with which that author is identified.
-
E.
famousWriterAssociated
Indicates a relationship where a well-known or renowned writer is connected or linked to a particular entity (such as a work, place, event, or organization).
- 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_69ca843057b48190a86730167f5d6b98 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cdd2ed85b4819097dfe89e044e1a90 |
completed | April 2, 2026, 2:22 a.m. |
| PD | Predicate disambiguation | batch_69cd4ba1d360819087698d04a53cc87e |
completed | April 1, 2026, 4:45 p.m. |
Created at: March 30, 2026, 9:05 p.m.