Triple
T2654296
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Northeast Africa |
E53971
|
entity |
| Predicate | hasEthnoLinguisticRegion |
P1968
|
FINISHED |
| Object | Afroasiatic languages area |
—
|
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: Afroasiatic languages area | Statement: [Northeast Africa, hasEthnoLinguisticRegion, Afroasiatic languages area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEthnoLinguisticRegion Context triple: [Northeast Africa, hasEthnoLinguisticRegion, Afroasiatic languages area]
-
A.
hasEthnologueMacroArea
Indicates that something belongs to, or is classified within, a specific macro-geographical area as defined by Ethnologue.
-
B.
ethnogenesisRegion
Indicates the geographic region where the formation or emergence of an ethnic group took place.
-
C.
culturalRegion
chosen
Indicates that an entity is located in, associated with, or belongs to a specific cultural region or cultural area.
-
D.
regionOfMajorLanguage
Indicates the geographic region where a particular language is predominantly spoken or holds major usage.
-
E.
hasEthnologueEntry
Indicates that there exists an entry for the subject in the Ethnologue language reference resource.
- 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_69ab495e192081909c77b622e8e7e15a |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abda0ba2208190ad87763ecbef8c3c |
completed | March 7, 2026, 7:55 a.m. |
| PD | Predicate disambiguation | batch_69abd815d06481909535c02b0aba8553 |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:53 p.m.