Triple
T1597863
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central Bohemian Region |
E34324
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Nymburk
Nymburk is a historic town in the Czech Republic known for its medieval fortifications and location on the Elbe River.
|
E235268
|
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: Nymburk | Statement: [Central Bohemian Region, contains, Nymburk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nymburk Context triple: [Central Bohemian Region, contains, Nymburk]
-
A.
Kolín
Kolín is a historic industrial town and important transport hub on the Elbe River in the Central Bohemian Region of the Czech Republic.
-
B.
Karviná
Karviná is an industrial city in the Moravian-Silesian Region of the Czech Republic, historically part of Cieszyn Silesia and known for its coal mining heritage.
-
C.
Plzeň
Plzeň is a major city in western Bohemia in the Czech Republic, known for its brewing tradition and industrial heritage.
-
D.
Ústí nad Labem
Ústí nad Labem is an industrial city in the north of the Czech Republic, known as a major transport hub and river port in the Bohemian region.
-
E.
Liberec
Liberec is a city in the northern Czech Republic known for its textile industry heritage, mountainous surroundings, and the landmark Ještěd Tower.
- 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: Nymburk Triple: [Central Bohemian Region, contains, Nymburk]
Generated description
Nymburk is a historic town in the Czech Republic known for its medieval fortifications and location on the Elbe River.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nymburk Target entity description: Nymburk is a historic town in the Czech Republic known for its medieval fortifications and location on the Elbe River.
-
A.
Kolín
Kolín is a historic industrial town and important transport hub on the Elbe River in the Central Bohemian Region of the Czech Republic.
-
B.
Karviná
Karviná is an industrial city in the Moravian-Silesian Region of the Czech Republic, historically part of Cieszyn Silesia and known for its coal mining heritage.
-
C.
Plzeň
Plzeň is a major city in western Bohemia in the Czech Republic, known for its brewing tradition and industrial heritage.
-
D.
Ústí nad Labem
Ústí nad Labem is an industrial city in the north of the Czech Republic, known as a major transport hub and river port in the Bohemian region.
-
E.
Liberec
Liberec is a city in the northern Czech Republic known for its textile industry heritage, mountainous surroundings, and the landmark Ještěd Tower.
- 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_69a885fdcb9c819081ce6f0b8cd477dd |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a9092f5f148190b987bc943e89e29c |
completed | March 5, 2026, 4:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae303cdce081909b66ec04de43cf53 |
completed | March 9, 2026, 2:28 a.m. |
| NEDg | Description generation | batch_69ae31549dec8190b3c9e8a73d561334 |
completed | March 9, 2026, 2:32 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae32673a948190bd2bc136edc33344 |
completed | March 9, 2026, 2:37 a.m. |
Created at: March 4, 2026, 7:27 p.m.