Triple
T33975973
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wanzi people |
E871140
|
entity |
| Predicate | sharesLinguisticAffiliationWith |
P76213
|
FINISHED |
| Object | Nzebi people |
—
|
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: Nzebi people | Statement: [Wanzi people, sharesLinguisticAffiliationWith, Nzebi people]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sharesLinguisticAffiliationWith Context triple: [Wanzi people, sharesLinguisticAffiliationWith, Nzebi people]
-
A.
hasLinguisticAffiliation
Indicates a relationship where an entity is associated with or belongs to a particular language or linguistic group.
-
B.
sharesLinguisticFamilyWith
chosen
Indicates that two languages belong to the same linguistic family or branch within a language family.
-
C.
sharesLanguageWith
Indicates that two entities use at least one common language for communication.
-
D.
linguisticallyRelatedTo
Indicates that two entities are connected through a linguistic relationship, such as sharing a common language, origin, structure, or other language-based association.
-
E.
sharesGeographicTiesWith
Indicates that two entities are connected through a common or related geographic area, such as shared location, region, or territorial association.
- 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_69f3499da0188190ab1a4ff06fb06a2a |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69ffde9263248190996f970b6cf6e49d |
completed | May 10, 2026, 1:25 a.m. |
| PD | Predicate disambiguation | batch_69ffdd760f1c8190abc6c0c1cd97ba5f |
completed | May 10, 2026, 1:20 a.m. |
Created at: May 1, 2026, 1:50 a.m.