Triple
T16617199
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lou Tresor dóu Felibrige |
E403724
|
entity |
| Predicate | hasMetalanguage |
P123560
|
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: [Lou Tresor dóu Felibrige, hasMetalanguage, French]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMetalanguage Context triple: [Lou Tresor dóu Felibrige, hasMetalanguage, French]
-
A.
hasMacroLanguage
Indicates that one language functions as a macrolanguage encompassing or grouping together multiple closely related individual languages or varieties.
-
B.
hasLanguageType
Indicates that an entity is associated with a particular type or category of language (e.g., spoken, written, programming, sign).
-
C.
hasSchemaLanguage
Indicates that an entity uses, conforms to, or is defined in a particular schema language.
-
D.
hasLanguageOn
Indicates that an entity uses or is associated with a particular language in a specific context, medium, or location.
-
E.
hasMinorLanguage
Indicates that an entity possesses or is associated with a secondary or less prominent language in addition to its primary language.
- 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_69d883897eb481909eaaa088ba9918d9 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3754ac9dc8190965197024594742b |
completed | April 18, 2026, 12:12 p.m. |
| PD | Predicate disambiguation | batch_69e296aabc508190b3836a91b49113ad |
completed | April 17, 2026, 8:23 p.m. |
| PDg | Predicate description generation | batch_69e2d7fb02f481908885a226c2191231 |
completed | April 18, 2026, 1:01 a.m. |
Created at: April 10, 2026, 5:17 a.m.