Triple
T30401921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sheng |
E773371
|
entity |
| Predicate | mainLexifierLanguage |
P11298
|
FINISHED |
| Object | Swahili |
—
|
NE NERFINISHED |
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: Swahili | Statement: [Sheng, mainLexifierLanguage, Swahili]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mainLexifierLanguage Context triple: [Sheng, mainLexifierLanguage, Swahili]
-
A.
primaryLexifierLanguage
Indicates the main source language from which the core vocabulary and structure of another language, typically a contact or creole language, are primarily derived.
-
B.
lexifierLanguage
chosen
Indicates that one language serves as the primary source or base language from which the core vocabulary and structure of another language, typically a pidgin or creole, are derived.
-
C.
derivationLanguage
Indicates the language from which something (such as a word, term, or expression) is derived.
-
D.
ISO639Language
Indicates that an entity is associated with a language identified or classified according to the ISO 639 language code standard.
-
E.
languageCategory
Indicates the classification relationship where a language is assigned to a particular linguistic or functional category.
- 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_69f2248facd48190b183c3f3ca6daef7 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6abaa1f648190b77073771df3bf3b |
completed | May 3, 2026, 1:58 a.m. |
| PD | Predicate disambiguation | batch_69f6aa1e84b88190b025f6ca40f17a8a |
completed | May 3, 2026, 1:51 a.m. |
Created at: April 29, 2026, 8:03 p.m.