Triple
T856941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amur Oblast |
E18512
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Belogorsk
Belogorsk is a city in Russia’s Far East that serves as an important regional center within Amur Oblast.
|
E110214
|
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: Belogorsk | Statement: [Amur Oblast, hasCity, Belogorsk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Belogorsk Context triple: [Amur Oblast, hasCity, Belogorsk]
-
A.
Shchyolkovo
Shchyolkovo is a town in western Russia that serves as a residential and industrial suburb of Moscow within Moscow Oblast.
-
B.
Novo-Ogaryovo
Novo-Ogaryovo is a suburban governmental estate outside Moscow that serves as one of the primary official residences of Russian President Vladimir Putin.
-
C.
Astapovo
Astapovo is a small Russian railway station village historically known as the place where the writer Leo Tolstoy died in 1910.
-
D.
Krasnaya Sloboda
Krasnaya Sloboda is a historic all-Jewish settlement in northern Azerbaijan, known as one of the world's only exclusively Mountain Jewish towns.
-
E.
Novoslobodskaya
Novoslobodskaya is a Moscow Metro station famed for its distinctive stained-glass panels and ornate, cathedral-like interior design.
- 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: Belogorsk Triple: [Amur Oblast, hasCity, Belogorsk]
Generated description
Belogorsk is a city in Russia’s Far East that serves as an important regional center within Amur Oblast.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Belogorsk Target entity description: Belogorsk is a city in Russia’s Far East that serves as an important regional center within Amur Oblast.
-
A.
Shchyolkovo
Shchyolkovo is a town in western Russia that serves as a residential and industrial suburb of Moscow within Moscow Oblast.
-
B.
Novo-Ogaryovo
Novo-Ogaryovo is a suburban governmental estate outside Moscow that serves as one of the primary official residences of Russian President Vladimir Putin.
-
C.
Astapovo
Astapovo is a small Russian railway station village historically known as the place where the writer Leo Tolstoy died in 1910.
-
D.
Krasnaya Sloboda
Krasnaya Sloboda is a historic all-Jewish settlement in northern Azerbaijan, known as one of the world's only exclusively Mountain Jewish towns.
-
E.
Novoslobodskaya
Novoslobodskaya is a Moscow Metro station famed for its distinctive stained-glass panels and ornate, cathedral-like interior design.
- 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_69a4938bdd3c8190a954a3c11844d9cf |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4ac4d47508190b48d944aa2d881bf |
completed | March 1, 2026, 9:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7edfe79bc8190bcfb89f4bbc1feb4 |
completed | March 4, 2026, 8:31 a.m. |
| NEDg | Description generation | batch_69a80a93f1f081909ca6a76346fd607e |
completed | March 4, 2026, 10:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a80aecb32c81908456b38f0d4603ef |
completed | March 4, 2026, 10:35 a.m. |
Created at: March 1, 2026, 7:39 p.m.