Triple
T12757115
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fugue in D major, BWV 850 |
E304887
|
entity |
| Predicate | hasCounterSubject |
P106740
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Fugue in D major, BWV 850, hasCounterSubject, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCounterSubject Context triple: [Fugue in D major, BWV 850, hasCounterSubject, yes]
-
A.
hasCounterService
Indicates that a place provides service to customers over a counter, such as ordering, paying, or receiving items at a service counter.
-
B.
hasSubjectCount
Indicates that an entity is associated with a specific number of subjects.
-
C.
hasCounterpart
Indicates that one entity corresponds to, matches, or serves as an equivalent or parallel version of another entity.
-
D.
usesCounterLength
Indicates that one entity determines or measures something based on the length of a counter value.
-
E.
hasCountingPeriod
Indicates that there is a defined time span or interval over which occurrences, quantities, or measurements related to an entity are counted or aggregated.
- 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_69d7bdf1fcd081909ffb0e0d6fa3a07d |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96d8b57b88190b29b8fdca415c81c |
completed | April 10, 2026, 9:37 p.m. |
| PD | Predicate disambiguation | batch_69d96406e97c8190b79081039847115c |
completed | April 10, 2026, 8:56 p.m. |
| PDg | Predicate description generation | batch_69d96d87078c819083ea724238992204 |
completed | April 10, 2026, 9:37 p.m. |
Created at: April 9, 2026, 5:27 p.m.