Triple
T11092670
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Église Saint-Nicolas-du-Chardonnet |
E262293
|
entity |
| Predicate | massLanguage |
P28304
|
FINISHED |
| Object | Latin |
—
|
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: Latin | Statement: [Église Saint-Nicolas-du-Chardonnet, massLanguage, Latin]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: massLanguage Context triple: [Église Saint-Nicolas-du-Chardonnet, massLanguage, Latin]
-
A.
languageDiversity
Indicates the degree to which multiple distinct languages are present and used within a given context or population.
-
B.
macrolanguageOf
Indicates that one language functions as a macrolanguage encompassing or grouping together one or more related individual languages.
-
C.
mediaLanguage
chosen
Indicates the language in which a media item (such as a film, broadcast, or publication) is originally produced or presented.
-
D.
macrolanguage
Indicates that a language is classified as a macrolanguage encompassing multiple closely related individual languages or varieties.
-
E.
majorityLanguageOf
Indicates that a given language is the primary or most widely spoken language within a specified group, region, or entity.
- 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_69d6aa9a40d88190a373e2c7e48285db |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d799ec6564819097624195d0cd9093 |
completed | April 9, 2026, 12:22 p.m. |
| PD | Predicate disambiguation | batch_69d744185a5881909ba4cf151d1798ec |
completed | April 9, 2026, 6:15 a.m. |
Created at: April 8, 2026, 9:27 p.m.