Triple
T10584678
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ethiopia–South Sudan border |
E249823
|
entity |
| Predicate | nearbyLanguageFamily |
P38997
|
FINISHED |
| Object | Koman languages |
—
|
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: Koman languages | Statement: [Ethiopia–South Sudan border, nearbyLanguageFamily, Koman languages]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nearbyLanguageFamily Context triple: [Ethiopia–South Sudan border, nearbyLanguageFamily, Koman languages]
-
A.
neighboringLanguageFamilies
chosen
Indicates that two language families are geographically adjacent or border each other in their primary regions of use.
-
B.
shareLanguageFamilyContext
Indicates that two entities are associated with languages that belong to the same language family or broader linguistic grouping within a given context.
-
C.
languageFamilyAssociated
Indicates that there is an association or connection between a language and a particular language family.
-
D.
sharesLinguisticFamilyWith
Indicates that two languages belong to the same linguistic family or branch within a language family.
-
E.
inLanguageFamily
Indicates that two languages belong to the same linguistic family or classification.
- 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_69d381c9d3d48190a29ee491e1696a0e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d52768da9c8190add1db88bf2e16ea |
completed | April 7, 2026, 3:48 p.m. |
| PD | Predicate disambiguation | batch_69d51907b2b881908ab9a8594688ee06 |
completed | April 7, 2026, 2:47 p.m. |
Created at: April 6, 2026, 12:39 p.m.