Triple
T34219110
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ethiopia–Djibouti border |
E877875
|
entity |
| Predicate | ethnicRegionAffected |
P25846
|
FINISHED |
| Object | Afar Region |
—
|
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: Afar Region | Statement: [Ethiopia–Djibouti border, ethnicRegionAffected, Afar Region]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ethnicRegionAffected Context triple: [Ethiopia–Djibouti border, ethnicRegionAffected, Afar Region]
-
A.
ethnicRegionOfFocus
Indicates that a particular ethnic group or ethnicity is the primary regional focus or subject of attention in a given context.
-
B.
populationRegion
Indicates that a specified population is located within or associated with a particular geographic region.
-
C.
countryOrRegionOfPrevalence
Indicates the country or geographic region where something (such as a condition, practice, or phenomenon) is most commonly found or occurs most frequently.
-
D.
impactRegion
chosen
Indicates the geographic or spatial area that is affected or influenced by a particular event, action, or phenomenon.
-
E.
demographicRegion
Indicates that an entity is associated with, belongs to, or is characterized by a particular geographic or administrative region for demographic purposes.
- 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_69f349b0b4bc819088c1552424089ee9 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fcef654d588190b29ecc76678d1aa0 |
completed | May 7, 2026, 8 p.m. |
| PD | Predicate disambiguation | batch_69fcecdb97f48190b382b7d13be92dc0 |
completed | May 7, 2026, 7:49 p.m. |
Created at: May 1, 2026, 1:55 a.m.