Triple
T12240052
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Contrapunctus XI |
E291704
|
entity |
| Predicate | hasSubjectCount |
P103965
|
FINISHED |
| Object | multiple fugue subjects |
—
|
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: multiple fugue subjects | Statement: [Contrapunctus XI, hasSubjectCount, multiple fugue subjects]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSubjectCount Context triple: [Contrapunctus XI, hasSubjectCount, multiple fugue subjects]
-
A.
hasTypicalSubject
Indicates that something is commonly or characteristically used as the subject (agent or topic) of a given relation or action.
-
B.
hasGradeCount
Indicates a relationship where an entity is associated with the number of grades it has or has received.
-
C.
hasPrimarySubject
Indicates that an entity is the main or principal subject associated with another entity or resource.
-
D.
hasCategoryCount
Indicates the number of distinct categories associated with a given entity.
-
E.
hasWorkSubject
Indicates that a work (such as a document, artwork, or project) is about or concerns a particular subject or topic.
- 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_69d6ab67950c8190be08450a06228c4b |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d924a3973c8190a882046963b320fb |
completed | April 10, 2026, 4:26 p.m. |
| PD | Predicate disambiguation | batch_69d91c41bcbc81909782f4e3c571b218 |
completed | April 10, 2026, 3:50 p.m. |
| PDg | Predicate description generation | batch_69d92468052c819090546f36d009a64f |
completed | April 10, 2026, 4:25 p.m. |
Created at: April 8, 2026, 9:51 p.m.