Triple
T2759042
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tupolev Tu-104 |
E61174
|
entity |
| Predicate | introducedOnRoute |
P513
|
FINISHED |
| Object |
Moscow–Prague
Moscow–Prague is an international air route connecting the capitals of Russia and the Czech Republic.
|
E296054
|
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: Moscow–Prague | Statement: [Tupolev Tu-104, introducedOnRoute, Moscow–Prague]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Moscow–Prague Context triple: [Tupolev Tu-104, introducedOnRoute, Moscow–Prague]
-
A.
Prazhskaya
Prazhskaya is a Moscow Metro station named after Prague, featuring Soviet-era architecture with Czech design influences.
-
B.
Prague
Prague is the historic capital city of the Czech Republic, renowned for its well-preserved medieval architecture, iconic Charles Bridge and Prague Castle, and vibrant cultural life.
-
C.
Kolín
Kolín is a historic industrial town and important transport hub on the Elbe River in the Central Bohemian Region of the Czech Republic.
-
D.
Liberec
Liberec is a city in the northern Czech Republic known for its textile industry heritage, mountainous surroundings, and the landmark Ještěd Tower.
-
E.
Nymburk
Nymburk is a historic town in the Czech Republic known for its medieval fortifications and location on the Elbe River.
- 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: Moscow–Prague Triple: [Tupolev Tu-104, introducedOnRoute, Moscow–Prague]
Generated description
Moscow–Prague is an international air route connecting the capitals of Russia and the Czech Republic.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Moscow–Prague Target entity description: Moscow–Prague is an international air route connecting the capitals of Russia and the Czech Republic.
-
A.
Prazhskaya
Prazhskaya is a Moscow Metro station named after Prague, featuring Soviet-era architecture with Czech design influences.
-
B.
Prague
Prague is the historic capital city of the Czech Republic, renowned for its well-preserved medieval architecture, iconic Charles Bridge and Prague Castle, and vibrant cultural life.
-
C.
Kolín
Kolín is a historic industrial town and important transport hub on the Elbe River in the Central Bohemian Region of the Czech Republic.
-
D.
Liberec
Liberec is a city in the northern Czech Republic known for its textile industry heritage, mountainous surroundings, and the landmark Ještěd Tower.
-
E.
Nymburk
Nymburk is a historic town in the Czech Republic known for its medieval fortifications and location on the Elbe River.
- 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_69ab4b7a85bc819094a349b84beb1f2c |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdd2121548190b96f174e6f61f9b5 |
completed | March 7, 2026, 8:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afbbe2d9cc81909b041635ae793bc3 |
completed | March 10, 2026, 6:36 a.m. |
| NEDg | Description generation | batch_69afbcc1dd988190826ab05e55adf1ee |
completed | March 10, 2026, 6:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afbd452e1c8190a3ee9eaf642e80a0 |
completed | March 10, 2026, 6:42 a.m. |
Created at: March 6, 2026, 9:57 p.m.