Triple
T4465615
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kyiv Metro |
E98369
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Livoberezhna
Livoberezhna is a metro station on the Kyiv Metro system, serving the left-bank area of Ukraine’s capital city.
|
E442314
|
NE FINISHED |
How this triple was built (4 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: Livoberezhna | Statement: [Kyiv Metro, hasStation, Livoberezhna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Livoberezhna Context triple: [Kyiv Metro, hasStation, Livoberezhna]
-
A.
Liozna
Liozna is a small settlement in present-day Belarus historically known as the birthplace of the artist Marc Chagall.
-
B.
Dobryninskaya
Dobryninskaya is a Moscow Metro station on the circular Koltsevaya Line, known for its Stalinist-era architecture and central location.
-
C.
Borovitskaya
Borovitskaya is a Moscow Metro station located in the city center, providing key interchange access between several central lines.
-
D.
Voykovskaya
Voykovskaya is a Moscow Metro station serving the Zamoskvoretskaya Line in the northern part of the city.
-
E.
Medveditsa
Medveditsa is a river in southwestern Russia that flows through the Volgograd and Saratov regions before joining the Don River.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Livoberezhna Triple: [Kyiv Metro, hasStation, Livoberezhna]
Generated description
Livoberezhna is a metro station on the Kyiv Metro system, serving the left-bank area of Ukraine’s capital city.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Livoberezhna Target entity description: Livoberezhna is a metro station on the Kyiv Metro system, serving the left-bank area of Ukraine’s capital city.
-
A.
Liozna
Liozna is a small settlement in present-day Belarus historically known as the birthplace of the artist Marc Chagall.
-
B.
Dobryninskaya
Dobryninskaya is a Moscow Metro station on the circular Koltsevaya Line, known for its Stalinist-era architecture and central location.
-
C.
Borovitskaya
Borovitskaya is a Moscow Metro station located in the city center, providing key interchange access between several central lines.
-
D.
Voykovskaya
Voykovskaya is a Moscow Metro station serving the Zamoskvoretskaya Line in the northern part of the city.
-
E.
Medveditsa
Medveditsa is a river in southwestern Russia that flows through the Volgograd and Saratov regions before joining the Don River.
- F. None of above. chosen
Provenance (5 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_69b3454a7c608190944f5455c8031d73 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b356991a588190be2f95fd957d7f99 |
completed | March 13, 2026, 12:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b62860151c8190b43465537b154f00 |
completed | March 15, 2026, 3:32 a.m. |
| NEDg | Description generation | batch_69b62c3bf15c8190826f93c4c43733e6 |
completed | March 15, 2026, 3:49 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b62cbaef8c8190b5d89a607c76eaf7 |
completed | March 15, 2026, 3:51 a.m. |
Created at: March 12, 2026, 11:34 p.m.