Triple
T6702560
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nggela people |
E152914
|
entity |
| Predicate | colonialLanguageInfluence |
P54956
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Nggela people, colonialLanguageInfluence, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: colonialLanguageInfluence Context triple: [Nggela people, colonialLanguageInfluence, English]
-
A.
languageInfluence
Indicates that one language has an effect on the development, usage, or characteristics of another language.
-
B.
historicalLanguageInfluenceOn
Indicates that one language has had a shaping or contributory effect on the development, vocabulary, structure, or usage of another language over time.
-
C.
officialLanguageDuringColonialPeriod
chosen
Indicates that a language served as the official language of administration or governance in a territory during its colonial period.
-
D.
influencedLanguage
Indicates that one language has had an effect on the development, structure, or usage of another language.
-
E.
shareLanguageInfluence
Indicates that two entities affect or shape each other’s language use, development, or characteristics through mutual or shared influence.
- 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_69c68807adbc8190b8632df42b39eda0 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d16897e48190b43eda2206b14d6a |
completed | March 27, 2026, 6:50 p.m. |
| PD | Predicate disambiguation | batch_69c6d089c7488190a00853fb12f53b2a |
completed | March 27, 2026, 6:46 p.m. |
Created at: March 27, 2026, 2:06 p.m.