Triple
T36806333
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Fantasia in the Russian Manner on English Themes |
E909462
|
entity |
| Predicate | indicatesSubjectMatter |
P450
|
FINISHED |
| Object | English themes |
—
|
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: English themes | Statement: [A Fantasia in the Russian Manner on English Themes, indicatesSubjectMatter, English themes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: indicatesSubjectMatter Context triple: [A Fantasia in the Russian Manner on English Themes, indicatesSubjectMatter, English themes]
-
A.
subjectMatter
chosen
Indicates the topic, theme, or content area that something (such as a work, document, or discussion) is about.
-
B.
subjectMatterScope
Indicates the thematic or topical domain that an action, statement, or resource pertains to or falls within.
-
C.
countryOfSubjectMatter
Indicates the country that the subject matter (e.g., a work, topic, or issue) primarily concerns or is associated with.
-
D.
subjectType
Indicates the classification or category that defines what kind of entity the subject is.
-
E.
subjectOfLaw
Indicates that a law, legal document, or legal provision is about, concerns, or applies to the referenced subject.
- 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_69f76e7cbbf48190891227b14d041139 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb3425666081908916fcbf3b5dd907 |
completed | May 6, 2026, 12:29 p.m. |
| PD | Predicate disambiguation | batch_69fb2f5f3164819099429c2cc3d24e01 |
completed | May 6, 2026, 12:09 p.m. |
Created at: May 3, 2026, 4:13 p.m.