Triple
T27618198
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | O mio babbino caro |
E700496
|
entity |
| Predicate | operaNumberInTrittico |
P194343
|
FINISHED |
| Object | third |
—
|
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: third | Statement: [O mio babbino caro, operaNumberInTrittico, third]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: operaNumberInTrittico Context triple: [O mio babbino caro, operaNumberInTrittico, third]
-
A.
operaNumberInVerdiOeuvre
Indicates the ordinal position of an opera within the complete body of operatic works composed by Verdi.
-
B.
operaNumberInMozartsOutput
Indicates the ordinal position or catalog number assigned to an opera within the complete body of Mozart’s operatic works.
-
C.
operaNumberInComposerOutput
Indicates the ordinal position or catalog number assigned to an opera within the complete body of works by a specific composer.
-
D.
thirdOpera
chosen
Indicates that one entity is the third opera created, performed, or associated within a sequence or body of work related to the other entity.
-
E.
numberOfOperas
Indicates the total count of operas associated with a given entity (such as a person, organization, or catalog entry).
- 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_69ef6a4f1d9c8190b0705acda054368d |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69fdee770af48190aca2670db50f8b49 |
completed | May 8, 2026, 2:08 p.m. |
| PD | Predicate disambiguation | batch_69fdecec98a08190a357d816dc2a6dbe |
completed | May 8, 2026, 2:02 p.m. |
Created at: April 27, 2026, 2:13 p.m.