Triple
T6215403
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tshiilafuri |
E138973
|
entity |
| Predicate | hasLanguageCodeScope |
P68948
|
FINISHED |
| Object | macrolanguage (Tshivenda) |
—
|
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: macrolanguage (Tshivenda) | Statement: [Tshiilafuri, hasLanguageCodeScope, macrolanguage (Tshivenda)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLanguageCodeScope Context triple: [Tshiilafuri, hasLanguageCodeScope, macrolanguage (Tshivenda)]
-
A.
hasLinguisticCode
Indicates that an entity is associated with a specific linguistic identifier or code (such as a language or script code) that characterizes its linguistic properties.
-
B.
hasLinguasphereCode
Indicates that an entity is associated with a specific Linguasphere code that identifies its language or linguistic variety within the Linguasphere classification system.
-
C.
hasLanguageStatus
Indicates that an entity has a particular status or condition regarding its language use, recognition, or classification.
-
D.
hasLanguageContext
Indicates that an entity is associated with or interpreted within a specific language or linguistic context.
-
E.
hasLanguageType
Indicates that an entity is associated with a particular type or category of language (e.g., spoken, written, programming, sign).
- 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_69c008ada364819096c9e92c74d639b5 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c062a0e0488190b71b42386bacf982 |
completed | March 22, 2026, 9:44 p.m. |
| PD | Predicate disambiguation | batch_69c055fdea3c81908f5d910f0d36234a |
completed | March 22, 2026, 8:50 p.m. |
| PDg | Predicate description generation | batch_69c056c965ac8190b938502fa8c74e1b |
completed | March 22, 2026, 8:53 p.m. |
Created at: March 22, 2026, 4:21 p.m.