Triple
T2708913
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nizhny Novgorod Oblast |
E59809
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object |
Dzerzhinsk
Dzerzhinsk is a major industrial city in western Russia known for its large chemical manufacturing sector and associated environmental issues.
|
E290966
|
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: Dzerzhinsk | Statement: [Nizhny Novgorod Oblast, hasMajorCity, Dzerzhinsk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dzerzhinsk Context triple: [Nizhny Novgorod Oblast, hasMajorCity, Dzerzhinsk]
-
A.
Zyuzino
Zyuzino is a Moscow Metro station on the Big Circle Line serving the Zyuzino District in southern Moscow.
-
B.
Dzerzhinovo
Dzerzhinovo is a village in present-day Belarus best known as the birthplace of Soviet statesman and secret police founder Felix Dzerzhinsky.
-
C.
Novoslobodskaya
Novoslobodskaya is a Moscow Metro station famed for its distinctive stained-glass panels and ornate, cathedral-like interior design.
-
D.
Shuisky
Shuisky is a scheming boyar and political intriguer in Alexander Pushkin’s historical drama and Modest Mussorgsky’s opera "Boris Godunov."
-
E.
Krasnopresnenskaya
Krasnopresnenskaya is a Moscow Metro station on the city’s circular Koltsevaya Line, known for its deep-level construction and Soviet-era architectural 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: Dzerzhinsk Triple: [Nizhny Novgorod Oblast, hasMajorCity, Dzerzhinsk]
Generated description
Dzerzhinsk is a major industrial city in western Russia known for its large chemical manufacturing sector and associated environmental issues.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dzerzhinsk Target entity description: Dzerzhinsk is a major industrial city in western Russia known for its large chemical manufacturing sector and associated environmental issues.
-
A.
Zyuzino
Zyuzino is a Moscow Metro station on the Big Circle Line serving the Zyuzino District in southern Moscow.
-
B.
Dzerzhinovo
Dzerzhinovo is a village in present-day Belarus best known as the birthplace of Soviet statesman and secret police founder Felix Dzerzhinsky.
-
C.
Novoslobodskaya
Novoslobodskaya is a Moscow Metro station famed for its distinctive stained-glass panels and ornate, cathedral-like interior design.
-
D.
Shuisky
Shuisky is a scheming boyar and political intriguer in Alexander Pushkin’s historical drama and Modest Mussorgsky’s opera "Boris Godunov."
-
E.
Krasnopresnenskaya
Krasnopresnenskaya is a Moscow Metro station on the city’s circular Koltsevaya Line, known for its deep-level construction and Soviet-era architectural 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_69ab4ac92a088190bc74bca14038e3de |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abda7542548190bbf6c947145f7f63 |
completed | March 7, 2026, 7:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afaf7f99508190acfd00baec64b7e9 |
completed | March 10, 2026, 5:43 a.m. |
| NEDg | Description generation | batch_69afb02d8ff08190af2224c03b762c68 |
completed | March 10, 2026, 5:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afb0ae71888190ab0675b7897f1589 |
completed | March 10, 2026, 5:48 a.m. |
Created at: March 6, 2026, 9:55 p.m.