Triple
T4447124
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Air Union |
E96315
|
entity |
| Predicate | operatedRoute |
P18593
|
FINISHED |
| Object |
Paris–Prague
Paris–Prague was an international air route connecting the French and Czech capitals, served by the early 20th-century French airline Air Union.
|
E440971
|
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: Paris–Prague | Statement: [Air Union, operatedRoute, Paris–Prague]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paris–Prague Context triple: [Air Union, operatedRoute, Paris–Prague]
-
A.
Moscow–Prague
Moscow–Prague is an international air route connecting the capitals of Russia and the Czech Republic.
-
B.
Praga
Praga is a historic district on the eastern bank of the Vistula River in Warsaw, Poland, known for its older architecture, cultural life, and role in the city's wartime history.
-
C.
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.
-
D.
Prazhskaya
Prazhskaya is a Moscow Metro station named after Prague, featuring Soviet-era architecture with Czech design influences.
-
E.
Paris–Brussels
Paris–Brussels is a major international high-speed rail corridor linking the capitals of France and Belgium.
- 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: Paris–Prague Triple: [Air Union, operatedRoute, Paris–Prague]
Generated description
Paris–Prague was an international air route connecting the French and Czech capitals, served by the early 20th-century French airline Air Union.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Paris–Prague Target entity description: Paris–Prague was an international air route connecting the French and Czech capitals, served by the early 20th-century French airline Air Union.
-
A.
Moscow–Prague
Moscow–Prague is an international air route connecting the capitals of Russia and the Czech Republic.
-
B.
Praga
Praga is a historic district on the eastern bank of the Vistula River in Warsaw, Poland, known for its older architecture, cultural life, and role in the city's wartime history.
-
C.
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.
-
D.
Prazhskaya
Prazhskaya is a Moscow Metro station named after Prague, featuring Soviet-era architecture with Czech design influences.
-
E.
Paris–Brussels
Paris–Brussels is a major international high-speed rail corridor linking the capitals of France and Belgium.
- 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_69b345415ba481908df738e7174448ba |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b355d31e10819086590b9f828d50b0 |
completed | March 13, 2026, 12:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b61386df48819080e44a23b9d67d23 |
completed | March 15, 2026, 2:03 a.m. |
| NEDg | Description generation | batch_69b617c13d4481909d22d201ce405d3a |
completed | March 15, 2026, 2:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b6187687f8819084e2d611e9e31f79 |
completed | March 15, 2026, 2:24 a.m. |
Created at: March 12, 2026, 11:32 p.m.