Triple
T13343686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fugue in G major, BWV 860 |
E317889
|
entity |
| Predicate | hasMainSubjectKey |
P109105
|
FINISHED |
| Object | G major |
—
|
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: G major | Statement: [Fugue in G major, BWV 860, hasMainSubjectKey, G major]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMainSubjectKey Context triple: [Fugue in G major, BWV 860, hasMainSubjectKey, G major]
-
A.
hasPrimarySubject
Indicates that an entity is the main or principal subject associated with another entity or resource.
-
B.
hasTypicalSubject
Indicates that something is commonly or characteristically used as the subject (agent or topic) of a given relation or action.
-
C.
hasSecondarySubject
Indicates that an entity is associated with an additional, non-primary subject in a given context or relationship.
-
D.
hasNotableSubject
Indicates that an entity is associated with a subject that is particularly significant, prominent, or noteworthy in relation to it.
-
E.
subjectKey
Indicates that the subject serves as a unique key or identifier used to reference or distinguish an entity in a relationship or dataset.
- 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_69d806b5a3c08190b42c267fb092f98a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99e8839b48190b164414b418e756c |
completed | April 11, 2026, 1:06 a.m. |
| PD | Predicate disambiguation | batch_69d98f6e53d88190bd6aa42f69b10ffb |
completed | April 11, 2026, 12:01 a.m. |
| PDg | Predicate description generation | batch_69d99073e4708190843bda3a1ae78f43 |
completed | April 11, 2026, 12:06 a.m. |
Created at: April 9, 2026, 9:31 p.m.