Triple
T16928149
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Konstantin Lopushansky |
E410630
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Lopushansky
Lopushansky is a Russian surname most notably associated with film director Konstantin Lopushansky, known for his philosophical and post-apocalyptic cinema.
|
E1241351
|
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: Lopushansky | Statement: [Konstantin Lopushansky, familyName, Lopushansky]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lopushansky Context triple: [Konstantin Lopushansky, familyName, Lopushansky]
-
A.
Lopatin
Lopatin is a Russian surname borne by various notable individuals in fields such as the military, arts, and academia.
-
B.
Lyova
Lyova is a Russian diminutive form of the male given name Lev.
-
C.
Lopar
Lopar is a coastal village and popular tourist resort located on the northern part of the Croatian island of Rab, known for its sandy beaches such as Paradise Beach.
-
D.
Golymin
Golymin is a village in east-central Poland best known as the site of a significant engagement during the Napoleonic Wars.
-
E.
Yunaska
Yunaska is the maiden surname of Lara Trump, who is married to Eric Trump, son of former U.S. President Donald Trump.
- 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: Lopushansky Triple: [Konstantin Lopushansky, familyName, Lopushansky]
Generated description
Lopushansky is a Russian surname most notably associated with film director Konstantin Lopushansky, known for his philosophical and post-apocalyptic cinema.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lopushansky Target entity description: Lopushansky is a Russian surname most notably associated with film director Konstantin Lopushansky, known for his philosophical and post-apocalyptic cinema.
-
A.
Lopatin
Lopatin is a Russian surname borne by various notable individuals in fields such as the military, arts, and academia.
-
B.
Lyova
Lyova is a Russian diminutive form of the male given name Lev.
-
C.
Lopar
Lopar is a coastal village and popular tourist resort located on the northern part of the Croatian island of Rab, known for its sandy beaches such as Paradise Beach.
-
D.
Golymin
Golymin is a village in east-central Poland best known as the site of a significant engagement during the Napoleonic Wars.
-
E.
Yunaska
Yunaska is the maiden surname of Lara Trump, who is married to Eric Trump, son of former U.S. President Donald Trump.
- 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_69d886c7b1e481908c3766dfa8c13458 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3cdf3fc3c8190a884f7ecd5c47adb |
completed | April 18, 2026, 6:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a00cfdb6b608190af910e225d942d37 |
completed | May 10, 2026, 6:35 p.m. |
| NEDg | Description generation | batch_6a00d0ce499c81909bd4ec1c77ae0202 |
completed | May 10, 2026, 6:39 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00d14b83d88190b3dbc124d5b33029 |
completed | May 10, 2026, 6:41 p.m. |
Created at: April 10, 2026, 5:30 a.m.