Triple
T11608961
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Thembu |
E275332
|
entity |
| Predicate | ethnicLanguageUsage |
P61052
|
FINISHED |
| Object | isiXhosa |
—
|
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: isiXhosa | Statement: [Thembu, ethnicLanguageUsage, isiXhosa]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ethnicLanguageUsage Context triple: [Thembu, ethnicLanguageUsage, isiXhosa]
-
A.
ethnicLanguageStatus
Indicates the status or role of a language in relation to a particular ethnic group (e.g., primary, secondary, heritage, or minority language).
-
B.
nationalLanguageSpoken
Indicates that a particular language is officially recognized and commonly used as a national language within a given country or region.
-
C.
languageDiversity
Indicates the degree to which multiple distinct languages are present and used within a given context or population.
-
D.
majorityLanguageOf
Indicates that a given language is the primary or most widely spoken language within a specified group, region, or entity.
-
E.
demographicsSignificantLanguage
chosen
Indicates that a particular language is significantly represented or prevalent within the demographic profile of a population or group.
- 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_69d6aaf84b548190ac072e4fb89ae18f |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a04231d08190b8a07cd977d11ffa |
completed | April 10, 2026, 7:01 a.m. |
| PD | Predicate disambiguation | batch_69d85dd20d188190863d1190d4c16048 |
completed | April 10, 2026, 2:17 a.m. |
Created at: April 8, 2026, 9:38 p.m.