Triple
T27864372
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | ISO 639-5: bnt |
E704315
|
entity |
| Predicate | hasMemberLanguages |
P7390
|
FINISHED |
| Object | Swahili language |
—
|
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 language | Statement: [ISO 639-5: bnt, hasMemberLanguages, Swahili language]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMemberLanguages Context triple: [ISO 639-5: bnt, hasMemberLanguages, Swahili language]
-
A.
hasMemberLanguage
chosen
Indicates that one entity is a language that is a constituent or member of a larger language group, family, or collection represented by the other entity.
-
B.
hasLanguages
Indicates that an entity is associated with one or more languages it uses, supports, or is expressed in.
-
C.
languageOfMembers
Indicates that the specified language is used or spoken by the members of a given group or organization.
-
D.
hasOfficerLanguage
Indicates that an officer is able or authorized to communicate in a specified language.
-
E.
hasLanguageGroup
Indicates that an entity belongs to, is associated with, or is categorized under a particular language 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_69ef840f12408190b539d00d79658abf |
completed | April 27, 2026, 3:43 p.m. |
| NER | Named-entity recognition | batch_69f7c777e924819081a6634f549fe552 |
completed | May 3, 2026, 10:08 p.m. |
| PD | Predicate disambiguation | batch_69f7c475c58c8190a883554231e88c88 |
completed | May 3, 2026, 9:56 p.m. |
Created at: April 27, 2026, 6:20 p.m.