Triple
T37145762
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mai-Mai Simba |
E920234
|
entity |
| Predicate | languageOfAreasOfOperation |
P203385
|
FINISHED |
| Object | Swahili |
—
|
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 | Statement: [Mai-Mai Simba, languageOfAreasOfOperation, Swahili]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfAreasOfOperation Context triple: [Mai-Mai Simba, languageOfAreasOfOperation, Swahili]
-
A.
hasPrimaryLanguageOfOperations
Indicates that an entity conducts its main activities or operations primarily using a specified language.
-
B.
languageOfOperation
Indicates the language in which an entity (such as a system, service, or process) primarily operates or functions.
-
C.
tertiaryLanguageOfOperation
Indicates that an entity uses a specified language as its third most prominent or prioritized language of operation.
-
D.
languageArea
Indicates the geographic or cultural region in which a particular language is used or predominantly spoken.
-
E.
hasLanguageOfMission
Indicates that an entity (such as a mission or project) is associated with a specific language used for its communication, documentation, or operation.
- 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_69f76e9f87c08190b4c8f7fafbd8345a |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_6a017114e7148190b7479284c316443d |
completed | May 11, 2026, 6:03 a.m. |
| PD | Predicate disambiguation | batch_6a016eebd60481909212e2a49ef95fbe |
completed | May 11, 2026, 5:53 a.m. |
| PDg | Predicate description generation | batch_6a0171143bd081909b68cca366b4dfb0 |
completed | May 11, 2026, 6:03 a.m. |
Created at: May 3, 2026, 4:15 p.m.