Triple
T3035473
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roman Catholic Diocese of Nice |
E82997
|
entity |
| Predicate | languageOfPastoralCare |
P33549
|
FINISHED |
| Object | French |
—
|
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: French | Statement: [Roman Catholic Diocese of Nice, languageOfPastoralCare, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfPastoralCare Context triple: [Roman Catholic Diocese of Nice, languageOfPastoralCare, French]
-
A.
languageOfCommunications
chosen
Indicates that a specified language is used as the medium for communications associated with an entity or interaction.
-
B.
primaryLanguageConcerned
Indicates that the relationship or action specifically involves or pertains to the main or principal language in question.
-
C.
languageOfCoverage
Indicates the language in which the coverage, such as reporting or documentation about something, is expressed.
-
D.
languageProvision
Indicates that one entity supplies, supports, or makes available a particular language (or set of languages) for use by another entity.
-
E.
languageOfExpression
Indicates that a particular language is used as the medium or form in which an expression (such as a text, utterance, or work) is realized.
- 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_69ad8b2298908190a7cb4e9bdbf064d0 |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9b2bb60c8190b1721f832f2581a1 |
completed | March 8, 2026, 3:52 p.m. |
| PD | Predicate disambiguation | batch_69ad961e2a408190afb1759132701305 |
completed | March 8, 2026, 3:30 p.m. |
Created at: March 8, 2026, 3:01 p.m.