Triple
T38599400
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mozart–Da Ponte trilogy |
E934167
|
entity |
| Predicate | settingOfMostOperas |
P195485
|
FINISHED |
| Object | 18th-century Europe |
—
|
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: 18th-century Europe | Statement: [Mozart–Da Ponte trilogy, settingOfMostOperas, 18th-century Europe]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: settingOfMostOperas Context triple: [Mozart–Da Ponte trilogy, settingOfMostOperas, 18th-century Europe]
-
A.
numberOfOperas
Indicates the total count of operas associated with a given entity (such as a person, organization, or catalog entry).
-
B.
estimatedNumberOfOperas
Indicates the approximate count of operas associated with an entity, rather than an exact, verified number.
-
C.
associatedOpera
Indicates that there is a relationship linking an entity to an opera with which it is connected or related (e.g., as subject, inspiration, or context).
-
D.
operaAct
Indicates that an entity performs in or takes part in an act (segment) of an opera performance.
-
E.
operaNumberInMozartsOutput
Indicates the ordinal position or catalog number assigned to an opera within the complete body of Mozart’s operatic works.
- 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_69f76ecc17688190b389b693a5927501 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fdd2be648c8190b60b3d1caeb44364 |
completed | May 8, 2026, 12:10 p.m. |
| PD | Predicate disambiguation | batch_69fdd14a5c708190a6f95ec61f4fc28f |
completed | May 8, 2026, 12:04 p.m. |
| PDg | Predicate description generation | batch_69fdd2bda90881909aa229194d014ba7 |
completed | May 8, 2026, 12:10 p.m. |
Created at: May 3, 2026, 4:32 p.m.