Triple
T9064033
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | László Bárdossy |
E217198
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Bárdossy
Bárdossy is a Hungarian surname most notably associated with László Bárdossy, who served as Hungary’s prime minister during World War II.
|
E774005
|
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: Bárdossy | Statement: [László Bárdossy, familyName, Bárdossy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bárdossy Context triple: [László Bárdossy, familyName, Bárdossy]
-
A.
Somlyó
Somlyó is a historical locality in the Kingdom of Hungary, best known as the birthplace of Stephen Báthory, who became King of Poland and Grand Duke of Lithuania in the 16th century.
-
B.
Lehel
Lehel is a historic and upscale central district of Munich, Germany, known for its elegant architecture and proximity to the Old Town and the Isar River.
-
C.
Ercsi
Ercsi is a small town in central Hungary situated along the Danube River in Fejér County.
-
D.
Sajó
Sajó is a river in Central Europe that flows through Slovakia and northeastern Hungary before joining the Tisza River.
-
E.
Bácska
Bácska is a historical region in the Pannonian Plain, today divided between northern Serbia and southern Hungary, known for its multicultural population and agricultural importance.
- 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: Bárdossy Triple: [László Bárdossy, familyName, Bárdossy]
Generated description
Bárdossy is a Hungarian surname most notably associated with László Bárdossy, who served as Hungary’s prime minister during World War II.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bárdossy Target entity description: Bárdossy is a Hungarian surname most notably associated with László Bárdossy, who served as Hungary’s prime minister during World War II.
-
A.
Somlyó
Somlyó is a historical locality in the Kingdom of Hungary, best known as the birthplace of Stephen Báthory, who became King of Poland and Grand Duke of Lithuania in the 16th century.
-
B.
Lehel
Lehel is a historic and upscale central district of Munich, Germany, known for its elegant architecture and proximity to the Old Town and the Isar River.
-
C.
Ercsi
Ercsi is a small town in central Hungary situated along the Danube River in Fejér County.
-
D.
Sajó
Sajó is a river in Central Europe that flows through Slovakia and northeastern Hungary before joining the Tisza River.
-
E.
Bácska
Bácska is a historical region in the Pannonian Plain, today divided between northern Serbia and southern Hungary, known for its multicultural population and agricultural importance.
- 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_69ca83d5a7f48190b16c1e59bd43ede0 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc94bb26588190b7d6f2d70819e86f |
completed | April 1, 2026, 3:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfebf8ecb48190b1802b5b41bc7aec |
completed | April 3, 2026, 4:34 p.m. |
| NEDg | Description generation | batch_69cfecbd94a08190841b9bd528fb51a5 |
completed | April 3, 2026, 4:37 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cfed3da2808190b0dbcae662b07957 |
completed | April 3, 2026, 4:39 p.m. |
Created at: March 30, 2026, 7:11 p.m.