Triple
T9191082
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | East African languages |
E220588
|
entity |
| Predicate | hasCreoleInfluenceOn |
P23173
|
FINISHED |
| Object | Swahili-based creoles |
—
|
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: Swahili-based creoles | Statement: [East African languages, hasCreoleInfluenceOn, Swahili-based creoles]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCreoleInfluenceOn Context triple: [East African languages, hasCreoleInfluenceOn, Swahili-based creoles]
-
A.
hasCreoleLanguage
Indicates that an entity possesses, uses, or is associated with a creole language.
-
B.
languageFamilyOfMajorCreole
Indicates that a given language family is the primary source or base language group from which a particular major creole language is derived.
-
C.
hasLatinInfluence
Indicates that one entity exerts or reflects cultural, linguistic, or stylistic influence derived from Latin on another entity.
-
D.
hasEthnicInfluence
Indicates that one entity has a cultural, traditional, or ethnic impact on, or contributes to shaping the ethnic character of, another entity.
-
E.
languageInfluence
chosen
Indicates that one language has an effect on the development, usage, or characteristics of another language.
- 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_69ca83e7ba70819088b74866d9da2c30 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccd5bf25c081909e651b67ef8ecc33 |
completed | April 1, 2026, 8:22 a.m. |
| PD | Predicate disambiguation | batch_69cc66090e5881908889dc1213815626 |
completed | April 1, 2026, 12:25 a.m. |
Created at: March 30, 2026, 7:24 p.m.