Triple
T16089506
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line C (Prague Metro) |
E390323
|
entity |
| Predicate | hasTerminus |
P388
|
FINISHED |
| Object |
Letňany station
Letňany station is a Prague Metro station in the Letňany district that serves as the northern terminus of Line C.
|
E1192859
|
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: Letňany station | Statement: [Line C (Prague Metro), hasTerminus, Letňany station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Letňany station Context triple: [Line C (Prague Metro), hasTerminus, Letňany station]
-
A.
Chelas station
Chelas station is a Lisbon Metro underground station serving the Chelas neighborhood on the system’s Red Line.
-
B.
Nikolassee station
Nikolassee station is a Berlin S-Bahn railway station in the Nikolassee district, serving as a local transit hub on the city's southwestern rail network.
-
C.
Paulina station
Paulina station is an elevated Chicago 'L' rapid transit stop in the Lakeview neighborhood, served by the Brown Line.
-
D.
Můstek station
Můstek station is a major Prague Metro interchange located at the lower end of Wenceslas Square, connecting lines A and B in the city center.
-
E.
Bolna Station
Bolna Station is a remote railway stop on Norway’s Nordland Line, serving the mountainous Saltfjellet region just north of the Arctic Circle.
- 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: Letňany station Triple: [Line C (Prague Metro), hasTerminus, Letňany station]
Generated description
Letňany station is a Prague Metro station in the Letňany district that serves as the northern terminus of Line C.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Letňany station Target entity description: Letňany station is a Prague Metro station in the Letňany district that serves as the northern terminus of Line C.
-
A.
Chelas station
Chelas station is a Lisbon Metro underground station serving the Chelas neighborhood on the system’s Red Line.
-
B.
Nikolassee station
Nikolassee station is a Berlin S-Bahn railway station in the Nikolassee district, serving as a local transit hub on the city's southwestern rail network.
-
C.
Paulina station
Paulina station is an elevated Chicago 'L' rapid transit stop in the Lakeview neighborhood, served by the Brown Line.
-
D.
Můstek station
Můstek station is a major Prague Metro interchange located at the lower end of Wenceslas Square, connecting lines A and B in the city center.
-
E.
Bolna Station
Bolna Station is a remote railway stop on Norway’s Nordland Line, serving the mountainous Saltfjellet region just north of the Arctic Circle.
- 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_69ffe490d494819081f812811f032702 |
completed | May 10, 2026, 1:51 a.m. |
| NEDg | Description generation | batch_69ffe63f757c81908c7dc3c5ae3075c6 |
completed | May 10, 2026, 1:58 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffe6b3f25481908dd4b6108b5d95c0 |
completed | May 10, 2026, 2 a.m. |
Created at: April 10, 2026, 4:59 a.m.