Triple
T11736540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kherson |
E279040
|
entity |
| Predicate | hasRailConnectionTo |
P848
|
FINISHED |
| Object | Dnipro |
E38828
|
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: Dnipro | Statement: [Kherson, hasRailConnectionTo, Dnipro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dnipro Context triple: [Kherson, hasRailConnectionTo, Dnipro]
-
A.
Dnipro
chosen
Dnipro is one of Ukraine’s largest industrial and cultural centers, located on the Dnieper River in the central-eastern part of the country.
-
B.
Oleksandriia
Oleksandriia is a city in central Ukraine known as an industrial and transport hub within the Kirovohrad region.
-
C.
Kryvyi Rih
Kryvyi Rih is a major industrial city in central Ukraine known for its extensive iron ore mining and steel production.
-
D.
Kremenchuk
Kremenchuk is an industrial city in central Ukraine on the Dnieper River, historically significant as a major transport and strategic hub.
-
E.
Kharkiv
Kharkiv is Ukraine’s second-largest city and a major industrial, cultural, and educational center in the northeast of the country.
- 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_69d6aaffec6881908bead509e8621742 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a4edced48190b7a59dd45921828e |
completed | April 10, 2026, 7:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f1308339ac8190b579a8c1bee2a2c2 |
completed | April 28, 2026, 10:11 p.m. |
Created at: April 8, 2026, 9:41 p.m.