Triple
T8601387
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dnipro University of Technology |
E203682
|
entity |
| Predicate | locatedIn |
P40
|
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: [Dnipro University of Technology, locatedIn, Dnipro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dnipro Context triple: [Dnipro University of Technology, locatedIn, 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.
Kryvyi Rih
Kryvyi Rih is a major industrial city in central Ukraine known for its extensive iron ore mining and steel production.
-
C.
Kremenchuk
Kremenchuk is an industrial city in central Ukraine on the Dnieper River, historically significant as a major transport and strategic hub.
-
D.
Kharkiv
Kharkiv is Ukraine’s second-largest city and a major industrial, cultural, and educational center in the northeast of the country.
-
E.
Dniprodzerzhynsk
Dniprodzerzhynsk (now officially called Kamianske) is an industrial city in central Ukraine known for its heavy industry and metallurgical enterprises along the Dnieper River.
- 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_69ca832b56948190ba751cec255308f1 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cc46d8ff408190acc7cd8dc99b2689 |
completed | March 31, 2026, 10:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d181fc06c48190b6a7444d975b1e09 |
completed | April 4, 2026, 9:26 p.m. |
Created at: March 30, 2026, 6:24 p.m.