Triple
T11258075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luvenda |
E266490
|
entity |
| Predicate | usesNounClassesFrom |
P5217
|
FINISHED |
| Object | Bantu noun class system |
—
|
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: Bantu noun class system | Statement: [Luvenda, usesNounClassesFrom, Bantu noun class system]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesNounClassesFrom Context triple: [Luvenda, usesNounClassesFrom, Bantu noun class system]
-
A.
hasNounClassSystem
chosen
Indicates that an entity possesses a grammatical system in which nouns are categorized into distinct classes that affect their agreement with other elements in the language.
-
B.
usesClassifiers
Indicates that one entity employs or relies on a system of classifiers (such as category labels or measure words) when referring to or organizing another entity.
-
C.
usesPostpositions
Indicates that one entity employs postpositions, placing relational or grammatical markers after the words they modify rather than before them.
-
D.
hasNounEnding
Indicates that something possesses or exhibits a particular noun-forming ending or suffix.
-
E.
usesClass
Indicates that one entity makes use of, depends on, or is implemented using a particular class in its structure or behavior.
- 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_69d6aac7953c8190b82caf9d7640fdf9 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e935b85c819085e1abf2dd4099c5 |
completed | April 9, 2026, 6 p.m. |
| PD | Predicate disambiguation | batch_69d78793c00481908a3f764b610b77a4 |
completed | April 9, 2026, 11:03 a.m. |
Created at: April 8, 2026, 9:31 p.m.