Triple
T8813760
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Die schöne Müllerin |
E209727
|
entity |
| Predicate | otherCharacter |
P37304
|
FINISHED |
| Object | miller’s beloved |
—
|
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: miller’s beloved | Statement: [Die schöne Müllerin, otherCharacter, miller’s beloved]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: otherCharacter Context triple: [Die schöne Müllerin, otherCharacter, miller’s beloved]
-
A.
relatedCharacter
chosen
Indicates that one character has a specified relationship or association with another character.
-
B.
characterIn
Indicates that an entity appears as a character within a specified work, story, or narrative.
-
C.
supportingCharacter
Indicates that one entity plays a secondary or assisting role in the story or context relative to another primary entity.
-
D.
character1
Indicates that the subject is identified as the first or primary character in a narrative or context.
-
E.
characters
Indicates that one entity is a character (or set of characters) associated with, appearing in, or belonging to another entity (such as a work, story, or medium).
- 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_69ca8363f3308190a47e3f1ebd51f613 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5ff02e9c819080a8e45ba9ca044e |
completed | March 31, 2026, 11:59 p.m. |
| PD | Predicate disambiguation | batch_69cc5c1f28ec8190a34311cb412920c2 |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:45 p.m.