Triple
T37028811
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cantata "Meine Freundin, du bist schön" |
E916430
|
entity |
| Predicate | hasTextIn |
P197650
|
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: [Cantata "Meine Freundin, du bist schön", hasTextIn, German]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTextIn Context triple: [Cantata "Meine Freundin, du bist schön", hasTextIn, German]
-
A.
hasText
Indicates that an entity is associated with or contains a specific piece of textual content.
-
B.
hasTextBy
Indicates that one entity (such as a document, work, or record) contains or is associated with text authored or written by another entity.
-
C.
containsText
Indicates that one entity includes the specified text string within its content.
-
D.
hasTextFrom
Indicates that one entity contains, is derived from, or directly uses the textual content originating from another entity.
-
E.
containsTextsFor
Indicates that one entity holds or includes text content intended for use by another entity.
- 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_69f76e92c7648190bcfa277f64c71a21 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fe9fb9735c8190a360b556c9d00b3f |
completed | May 9, 2026, 2:45 a.m. |
| PD | Predicate disambiguation | batch_69fe9eaa88008190a9b2a469dc685002 |
completed | May 9, 2026, 2:40 a.m. |
| PDg | Predicate description generation | batch_69fe9fb88db08190a8f4af350633330e |
completed | May 9, 2026, 2:45 a.m. |
Created at: May 3, 2026, 4:14 p.m.