Triple
T17697592
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tosca |
E441210
|
entity |
| Predicate | operaLanguage |
P128616
|
FINISHED |
| Object | Italian |
—
|
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: Italian | Statement: [Tosca, operaLanguage, Italian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: operaLanguage Context triple: [Tosca, operaLanguage, Italian]
-
A.
operaGenre
Indicates that an opera belongs to or is categorized under a particular musical or dramatic genre.
-
B.
operaAct
Indicates that an entity performs in or takes part in an act (segment) of an opera performance.
-
C.
operaEnÁmbito
Indicates that an entity operates, functions, or carries out its activities within a specified domain, field, or scope.
-
D.
languageOfOperator
Indicates that a particular language is used by, or associated with, a given operator in performing its functions or services.
-
E.
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).
- 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_69d8b9ea20b48190ace88bb46b01e6a9 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e47157fd688190ba990eaf46ceab01 |
completed | April 19, 2026, 6:08 a.m. |
| PD | Predicate disambiguation | batch_69e3cde3673c8190a889e14ba1f07dc1 |
completed | April 18, 2026, 6:30 p.m. |
| PDg | Predicate description generation | batch_69e3cfaac2b881909e1140339eb1a0dd |
completed | April 18, 2026, 6:38 p.m. |
Created at: April 10, 2026, 10:04 a.m.