Triple
T10559473
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Afar Region |
E249175
|
entity |
| Predicate | hasZoneCount |
P57453
|
FINISHED |
| Object | 5 administrative zones |
—
|
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: 5 administrative zones | Statement: [Afar Region, hasZoneCount, 5 administrative zones]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasZoneCount Context triple: [Afar Region, hasZoneCount, 5 administrative zones]
-
A.
numberOfZones
chosen
Indicates the quantity of distinct zones associated with or contained by a given entity.
-
B.
hasZone
Indicates that one entity possesses, contains, or is associated with a specific zone or designated area.
-
C.
hasNumberOfNationalTimeZones
Indicates the quantity of distinct official time zones that a nation or country uses within its territory.
-
D.
hasDRSZones
Indicates that one entity possesses, defines, or is associated with specific DRS (Disaster Recovery Site or similarly defined) zones.
-
E.
numberOfThemedZones
Indicates the total count of distinct themed zones associated with or contained within an entity.
- 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_69d381c8bd708190acf3d275c908251e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d5271e65688190bcf7931373d87f94 |
completed | April 7, 2026, 3:47 p.m. |
| PD | Predicate disambiguation | batch_69d51901ff6c819095e7b528170a69dc |
completed | April 7, 2026, 2:47 p.m. |
Created at: April 6, 2026, 12:35 p.m.