Triple
T38614107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Académie centrafricaine |
E934565
|
entity |
| Predicate | standardizesGrammarOf |
P69032
|
FINISHED |
| Object | Sango |
—
|
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: Sango | Statement: [Académie centrafricaine, standardizesGrammarOf, Sango]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: standardizesGrammarOf Context triple: [Académie centrafricaine, standardizesGrammarOf, Sango]
-
A.
hasStandardizedGrammar
chosen
Indicates that a language or notation follows an officially defined and consistently applied set of grammatical rules.
-
B.
standardizesOrthographyOf
Indicates that one entity establishes or applies a consistent writing system or spelling conventions to another entity’s language or text.
-
C.
hasGrammar
Indicates that an entity possesses, follows, or is associated with a particular system of grammatical rules or structure.
-
D.
standardizedFor
Indicates that something has been adjusted or converted to conform to a common standard, format, or reference so it can be consistently compared or used.
-
E.
scriptUsedForStandardization
Indicates that a particular writing system or script is employed as the standard reference form for normalizing or harmonizing text or data.
- 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_69f76eccd6d081909ccce171011739a1 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fdbc5ef46c8190bbcfb9798f4615b7 |
completed | May 8, 2026, 10:35 a.m. |
| PD | Predicate disambiguation | batch_69fdbb270338819082ce3f73903e884f |
completed | May 8, 2026, 10:29 a.m. |
Created at: May 3, 2026, 4:32 p.m.