Triple
T37358565
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leporello |
E927518
|
entity |
| Predicate | languageOfAria |
P50222
|
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: [Leporello, languageOfAria, Italian]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfAria Context triple: [Leporello, languageOfAria, Italian]
-
A.
languageSpecifies
Indicates that one entity defines or constrains the syntax, semantics, or usage rules that govern how another language or linguistic system is expressed or interpreted.
-
B.
languageLabel
chosen
Indicates the human-readable name or label of a language associated with an entity or resource.
-
C.
languageOfCues
Indicates the language in which the cues (such as prompts, hints, or signals) are expressed.
-
D.
languageArea
Indicates the geographic or cultural region in which a particular language is used or predominantly spoken.
-
E.
languageCategory
Indicates the classification relationship where a language is assigned to a particular linguistic or functional category.
- 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_69f76eb701788190b40824bc4594d985 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a018f9c7cf88190adc5daf3806f2077 |
completed | May 11, 2026, 8:13 a.m. |
| PD | Predicate disambiguation | batch_6a018ca4e0e481908091f0c1f8a4c31b |
completed | May 11, 2026, 8 a.m. |
Created at: May 3, 2026, 4:16 p.m.