Triple
T16089526
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Line C (Prague Metro) |
E390323
|
entity |
| Predicate | connectsStation |
P845
|
FINISHED |
| Object |
Opatov station
Opatov station is a Prague Metro station on Line C serving the Opatov district in the southern part of the city.
|
E1201564
|
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: Opatov station | Statement: [Line C (Prague Metro), connectsStation, Opatov station]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Opatov station Context triple: [Line C (Prague Metro), connectsStation, Opatov station]
-
A.
Frunzenskaya station
Frunzenskaya station is a Moscow Metro station known for its deep-level construction and classic Soviet-era architectural design.
-
B.
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.
-
C.
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.
-
D.
Traktorozavodskaya station
Traktorozavodskaya station is a stop on Volgograd’s Metrotram system serving the industrial Traktorozavodsky district of the city.
-
E.
Yelshanka station
Yelshanka station is a stop on the Volgograd Metrotram light rail system in Volgograd, Russia.
- 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: Opatov station Triple: [Line C (Prague Metro), connectsStation, Opatov station]
Generated description
Opatov station is a Prague Metro station on Line C serving the Opatov district in the southern part of the city.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Opatov station Target entity description: Opatov station is a Prague Metro station on Line C serving the Opatov district in the southern part of the city.
-
A.
Frunzenskaya station
Frunzenskaya station is a Moscow Metro station known for its deep-level construction and classic Soviet-era architectural design.
-
B.
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.
-
C.
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.
-
D.
Traktorozavodskaya station
Traktorozavodskaya station is a stop on Volgograd’s Metrotram system serving the industrial Traktorozavodsky district of the city.
-
E.
Yelshanka station
Yelshanka station is a stop on the Volgograd Metrotram light rail system in Volgograd, Russia.
- 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_6a000ec4d9808190a3d1bfc8f3d73168 |
completed | May 10, 2026, 4:51 a.m. |
| NEDg | Description generation | batch_6a000f6dddc88190b23fe53690fbff2e |
completed | May 10, 2026, 4:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a000fbfc4d88190b50967788e6af340 |
completed | May 10, 2026, 4:55 a.m. |
Created at: April 10, 2026, 4:59 a.m.