Triple
T4652758
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kežmarok |
E102333
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object |
Velký Meder
Velký Meder is a spa and tourist town in southwestern Slovakia known for its thermal baths and recreational facilities.
|
E457982
|
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: Velký Meder | Statement: [Kežmarok, hasTwinTown, Velký Meder]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Velký Meder Context triple: [Kežmarok, hasTwinTown, Velký Meder]
-
A.
Medveditsa
Medveditsa is a river in southwestern Russia that flows through the Volgograd and Saratov regions before joining the Don River.
-
B.
Velkua
Velkua is a former island municipality in southwestern Finland known for its coastal archipelago landscape in the Baltic Sea.
-
C.
Veľký Krtíš
Veľký Krtíš is a small town in southern Slovakia known as an administrative and economic center of the surrounding wine-growing and agricultural region.
-
D.
Oreshek
Oreshek is the historic Russian fortress on Lake Ladoga that later gave rise to the town of Shlisselburg.
-
E.
Berounka
Berounka is a major river in western Bohemia in the Czech Republic, known for flowing through the Plzeň Region and eventually joining the Vltava near Prague.
- 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: Velký Meder Triple: [Kežmarok, hasTwinTown, Velký Meder]
Generated description
Velký Meder is a spa and tourist town in southwestern Slovakia known for its thermal baths and recreational facilities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Velký Meder Target entity description: Velký Meder is a spa and tourist town in southwestern Slovakia known for its thermal baths and recreational facilities.
-
A.
Medveditsa
Medveditsa is a river in southwestern Russia that flows through the Volgograd and Saratov regions before joining the Don River.
-
B.
Velkua
Velkua is a former island municipality in southwestern Finland known for its coastal archipelago landscape in the Baltic Sea.
-
C.
Veľký Krtíš
Veľký Krtíš is a small town in southern Slovakia known as an administrative and economic center of the surrounding wine-growing and agricultural region.
-
D.
Oreshek
Oreshek is the historic Russian fortress on Lake Ladoga that later gave rise to the town of Shlisselburg.
-
E.
Berounka
Berounka is a major river in western Bohemia in the Czech Republic, known for flowing through the Plzeň Region and eventually joining the Vltava near Prague.
- 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_69bd43d71a308190afea7280841b0de8 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd6314883481908f085a7af497b0d8 |
completed | March 20, 2026, 3:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdfaeb1ee081909ef641953bdf8df3 |
completed | March 21, 2026, 1:56 a.m. |
| NEDg | Description generation | batch_69bdfbc12acc8190b8116a6003abb3e3 |
completed | March 21, 2026, 2 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69bdfc44536c8190a71e52b0690a7570 |
completed | March 21, 2026, 2:02 a.m. |
Created at: March 20, 2026, 1:14 p.m.