Triple
T12898938
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canonical Hours |
E308565
|
entity |
| Predicate | languageCurrently |
P18209
|
FINISHED |
| Object | vernacular languages |
—
|
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: vernacular languages | Statement: [Canonical Hours, languageCurrently, vernacular languages]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageCurrently Context triple: [Canonical Hours, languageCurrently, vernacular languages]
-
A.
languageUse
chosen
Indicates the language or languages an entity uses for communication, expression, or interaction.
-
B.
languageLabel
Indicates the human-readable name or label of a language associated with an entity or resource.
-
C.
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.
-
D.
languageIndependence
Indicates that a concept, method, or representation does not depend on any specific programming or natural language and can be applied uniformly across different languages.
-
E.
languageDiscussedIn
Indicates that a particular language is the topic of discussion within a specified context, source, or discourse.
- 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_69d7bdf7c1f0819098102569a8d8cbf5 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9717f3fc48190b61c8f6f36cd0725 |
completed | April 10, 2026, 9:54 p.m. |
| PD | Predicate disambiguation | batch_69d96fa776648190b9b5c30722ea50b6 |
completed | April 10, 2026, 9:46 p.m. |
Created at: April 9, 2026, 5:40 p.m.