Triple
T20300186
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ministry of Higher Education of Afghanistan |
E505456
|
entity |
| Predicate | usesLanguage |
P238
|
FINISHED |
| Object | Dari |
—
|
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: Dari | Statement: [Ministry of Higher Education of Afghanistan, usesLanguage, Dari]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dari Context triple: [Ministry of Higher Education of Afghanistan, usesLanguage, Dari]
-
A.
Dari
chosen
Dari is a variety of the Persian language primarily spoken in Afghanistan and used in media, education, and government there.
-
B.
Dijlah
Dijlah is the Arabic name for the Tigris River, one of the major rivers of Western Asia flowing through Turkey, Syria, and Iraq.
-
C.
Dairut
Dairut is a city in Upper Egypt known as an important urban and agricultural center within the Asyut region along the Nile.
-
D.
Daudin
Daudin is a French surname most notably associated with François Marie Daudin, an 18th-century zoologist and herpetologist.
-
E.
Dawar
Dawar is a town in the Gurez Valley of Jammu and Kashmir, India, known for its remote Himalayan setting near the Line of Control.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4b8ab648190906e18538c250148 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6770b9484819090ffcb339f2a435a |
completed | April 20, 2026, 6:57 p.m. |
Created at: April 16, 2026, 11:16 a.m.