Triple
T2202476
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Astana |
E50521
|
entity |
| Predicate | renamedFrom |
P65
|
FINISHED |
| Object | Nur-Sultan |
E50521
|
NE 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: Nur-Sultan | Statement: [Astana, renamedFrom, Nur-Sultan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nur-Sultan Context triple: [Astana, renamedFrom, Nur-Sultan]
-
A.
Almaty
Almaty is the largest city and main commercial and cultural center of Kazakhstan, located in the country’s mountainous southeast.
-
B.
Karaganda
Karaganda is a large industrial city in central Kazakhstan known for its coal mining industry and Soviet-era history.
-
C.
Astana
chosen
Astana is the planned, modernist capital city of Kazakhstan, known for its futuristic architecture and rapid development since the late 20th century.
-
D.
Shymkent
Shymkent is one of the largest and most populous cities in southern Kazakhstan, serving as a key industrial, commercial, and cultural center of the region.
-
E.
Alma-Atinskaya
Alma-Atinskaya is a southern terminus station of the Moscow Metro, serving as one endpoint of the Zamoskvoretskaya Line.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69a88b044ab48190add007487680f009 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abbfa33f0881908403604eafb73ecf |
completed | March 7, 2026, 6:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae95f7e5448190a31dcb5ba3c547fb |
completed | March 9, 2026, 9:42 a.m. |
Created at: March 4, 2026, 7:46 p.m.