Triple
T12838703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sumy Oblast |
E306986
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object |
Lebedyn
Lebedyn is a city in northeastern Ukraine known for its historical architecture and role as a local cultural and administrative center.
|
E1004144
|
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: Lebedyn | Statement: [Sumy Oblast, hasMajorCity, Lebedyn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lebedyn Context triple: [Sumy Oblast, hasMajorCity, Lebedyn]
-
A.
Lebedus
Lebedus was an ancient Greek city of Ionia on the western coast of Asia Minor, known as a minor but strategically located coastal settlement involved in regional trade and politics.
-
B.
Barysaw
Barysaw is a city in Belarus known as an important industrial and transportation center northeast of Minsk.
-
C.
Zvenyhorodka
Zvenyhorodka is a town in central Ukraine that serves as a local administrative and cultural center within Cherkasy Oblast.
-
D.
Bogrod
Bogrod is a goblin banker who works at Gringotts Wizarding Bank in the Harry Potter series.
-
E.
Bronnitsy
Bronnitsy is a historic town in Russia known for its jewelry-making traditions and its location southeast of Moscow along the Moskva 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: Lebedyn Triple: [Sumy Oblast, hasMajorCity, Lebedyn]
Generated description
Lebedyn is a city in northeastern Ukraine known for its historical architecture and role as a local cultural and administrative center.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lebedyn Target entity description: Lebedyn is a city in northeastern Ukraine known for its historical architecture and role as a local cultural and administrative center.
-
A.
Lebedus
Lebedus was an ancient Greek city of Ionia on the western coast of Asia Minor, known as a minor but strategically located coastal settlement involved in regional trade and politics.
-
B.
Barysaw
Barysaw is a city in Belarus known as an important industrial and transportation center northeast of Minsk.
-
C.
Zvenyhorodka
Zvenyhorodka is a town in central Ukraine that serves as a local administrative and cultural center within Cherkasy Oblast.
-
D.
Bogrod
Bogrod is a goblin banker who works at Gringotts Wizarding Bank in the Harry Potter series.
-
E.
Bronnitsy
Bronnitsy is a historic town in Russia known for its jewelry-making traditions and its location southeast of Moscow along the Moskva 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_69d7bdf52b94819096d6f0ba4ab50a98 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96ff11b4481909fb2f92c46186853 |
completed | April 10, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f68edd30e881909062e8f91f614990 |
completed | May 2, 2026, 11:55 p.m. |
| NEDg | Description generation | batch_69f68f8d2ca08190a385635fb6130a9f |
completed | May 2, 2026, 11:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f69033a66481908bf4ae23fced5983 |
completed | May 3, 2026, midnight |
Created at: April 9, 2026, 5:35 p.m.