Triple
T18522005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zbigniew Rybczyński |
E452608
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Rybczyński
Rybczyński is a Polish surname most notably associated with Oscar-winning experimental filmmaker and visual effects pioneer Zbigniew Rybczyński.
|
E1329626
|
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: Rybczyński | Statement: [Zbigniew Rybczyński, familyName, Rybczyński]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rybczyński Context triple: [Zbigniew Rybczyński, familyName, Rybczyński]
-
A.
Myrdal
Myrdal is a remote mountain railway station in Norway that serves as a key junction between the Bergen Line and the scenic Flåm Line.
-
B.
Slutsky
Slutsky is a Slavic surname borne by various notable individuals in fields such as politics, economics, and sports.
-
C.
Feenstra
Feenstra is a Dutch-origin surname borne by various notable individuals, including American politician Randy Feenstra.
-
D.
Okun
Okun is a surname most notably associated with figures such as economist Arthur Okun, known for Okun's law relating unemployment and economic output.
-
E.
Dornbusch
Dornbusch is a prominent hill and lighthouse site on the Baltic Sea island of Hiddensee in Germany, known for its scenic views and coastal landscape.
- 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: Rybczyński Triple: [Zbigniew Rybczyński, familyName, Rybczyński]
Generated description
Rybczyński is a Polish surname most notably associated with Oscar-winning experimental filmmaker and visual effects pioneer Zbigniew Rybczyński.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Rybczyński Target entity description: Rybczyński is a Polish surname most notably associated with Oscar-winning experimental filmmaker and visual effects pioneer Zbigniew Rybczyński.
-
A.
Myrdal
Myrdal is a remote mountain railway station in Norway that serves as a key junction between the Bergen Line and the scenic Flåm Line.
-
B.
Slutsky
Slutsky is a Slavic surname borne by various notable individuals in fields such as politics, economics, and sports.
-
C.
Feenstra
Feenstra is a Dutch-origin surname borne by various notable individuals, including American politician Randy Feenstra.
-
D.
Okun
Okun is a surname most notably associated with figures such as economist Arthur Okun, known for Okun's law relating unemployment and economic output.
-
E.
Dornbusch
Dornbusch is a prominent hill and lighthouse site on the Baltic Sea island of Hiddensee in Germany, known for its scenic views and coastal landscape.
- 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_69d8d386df84819092355ebb260d848e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5338e6e188190a41a4ee12c1ad330 |
completed | April 19, 2026, 7:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0491719490819090c39b4ca8973077 |
completed | May 13, 2026, 2:57 p.m. |
| NEDg | Description generation | batch_6a0492b3bb048190af9b28f3a1d89d7b |
completed | May 13, 2026, 3:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a049393db308190be7428a534c83ff9 |
completed | May 13, 2026, 3:07 p.m. |
Created at: April 10, 2026, 11:37 a.m.