Triple
T16089472
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line A (Prague Metro) |
E390322
|
entity |
| Predicate | hasStation |
P35
|
FINISHED |
| Object |
Malostranská
Malostranská is a Prague Metro station on Line A serving the historic Malá Strana (Lesser Town) district near Prague Castle.
|
E1209699
|
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: Malostranská | Statement: [Line A (Prague Metro), hasStation, Malostranská]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Malostranská Context triple: [Line A (Prague Metro), hasStation, Malostranská]
-
A.
Hradčanská
Hradčanská is a Prague Metro station on Line A located near Prague Castle in the Hradčany district.
-
B.
Vávrová
Vávrová is a Czech surname most notably borne by Dana Vávrová, a well-known Czech-German actress and film director.
-
C.
Moravice
Moravice is a river in the northern part of the historical Moravia region of the Czech Republic.
-
D.
Strakonice
Strakonice is a historic town in the Czech Republic known for its medieval castle and traditional bagpipe festival.
-
E.
Osek
Osek is a town in the Czech Republic historically associated with the family origins of writer Franz Kafka’s father, Hermann Kafka.
- 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: Malostranská Triple: [Line A (Prague Metro), hasStation, Malostranská]
Generated description
Malostranská is a Prague Metro station on Line A serving the historic Malá Strana (Lesser Town) district near Prague Castle.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Malostranská Target entity description: Malostranská is a Prague Metro station on Line A serving the historic Malá Strana (Lesser Town) district near Prague Castle.
-
A.
Hradčanská
Hradčanská is a Prague Metro station on Line A located near Prague Castle in the Hradčany district.
-
B.
Vávrová
Vávrová is a Czech surname most notably borne by Dana Vávrová, a well-known Czech-German actress and film director.
-
C.
Moravice
Moravice is a river in the northern part of the historical Moravia region of the Czech Republic.
-
D.
Strakonice
Strakonice is a historic town in the Czech Republic known for its medieval castle and traditional bagpipe festival.
-
E.
Osek
Osek is a town in the Czech Republic historically associated with the family origins of writer Franz Kafka’s father, Hermann Kafka.
- 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_69d87f198bc48190a8b7e53ca15b7ead |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e1845161908190adca2af94710b2cc |
completed | April 17, 2026, 12:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a003546d3e081908f1244b7f4fb1067 |
completed | May 10, 2026, 7:35 a.m. |
| NEDg | Description generation | batch_6a0035cfc31c8190a8ab73bbc1aacaca |
completed | May 10, 2026, 7:37 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00369714a88190a5e4733b67fdacfb |
completed | May 10, 2026, 7:41 a.m. |
Created at: April 10, 2026, 4:59 a.m.