Triple
T153791
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kyiv National Economic University |
E3487
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object | Kyiv |
E17733
|
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: Kyiv | Statement: [Kyiv National Economic University, city, Kyiv]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kyiv Context triple: [Kyiv National Economic University, city, Kyiv]
-
A.
Kyiv
chosen
Kyiv is the capital and largest city of Ukraine, serving as its political, cultural, and economic center.
-
B.
Kryvyi Rih
Kryvyi Rih is a major industrial city in central Ukraine known for its extensive iron ore mining and steel production.
-
C.
Lutsk
Lutsk is a historic city in northwestern Ukraine, known as the administrative center of Volyn Oblast and one of the region’s oldest cultural and economic hubs.
-
D.
Simferopol
Simferopol is the administrative and cultural center of Crimea, known as a key regional hub for transportation, education, and industry.
-
E.
Eupatoria
Eupatoria is a historic resort and port city on the western coast of Crimea, known for its beaches, therapeutic muds, and diverse cultural heritage.
- 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_69a252868de4819080e21c9938bfe8b6 |
completed | Feb. 28, 2026, 2:27 a.m. |
| NER | Named-entity recognition | batch_69a2582d25448190931c2d785e678a8a |
completed | Feb. 28, 2026, 2:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a2db53aaf081909577b743e3660e5c |
completed | Feb. 28, 2026, 12:10 p.m. |
Created at: Feb. 28, 2026, 2:31 a.m.