Triple

T27080762
Position Surface form Disambiguated ID Type / Status
Subject Kiev railway junction E685589 entity
Predicate hasFreightStation P19495 FINISHED
Object Darnytsia railway station
Darnytsia railway station is a major freight and passenger rail hub on the eastern bank of Kyiv, serving as one of the city’s key transport and logistics centers.
 E1755297 NE FINISHED

How this triple was built (3 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: Darnytsia railway station | Statement: [Kiev railway junction, hasFreightStation, Darnytsia railway station]
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: Darnytsia railway station
Triple: [Kiev railway junction, hasFreightStation, Darnytsia railway station]
Generated description
Darnytsia railway station is a major freight and passenger rail hub on the eastern bank of Kyiv, serving as one of the city’s key transport and logistics centers.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: hasFreightStation
Context triple: [Kiev railway junction, hasFreightStation, Darnytsia railway station]
  • A. hasFreightFacility
    Indicates that an entity is equipped with or connected to a facility used for handling, loading, unloading, or storing freight.
  • B. hasNearbyShippingLane
    Indicates that there is a shipping lane located close to the referenced entity or area.
  • C. hasRailFacility chosen
    Indicates that an entity possesses or is served by a rail-related facility, such as a railway station, terminal, or yard.
  • D. hasInterchangeStationWith
    Indicates that two transportation lines, routes, or systems share a station where passengers can transfer between them.
  • E. hasCargoTerminal
    Indicates that a location or facility includes or is equipped with a cargo terminal for handling freight.
  • F. None of above.

Provenance (6 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_69ef14843b1481909d828b3d5a44550a completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f65aa07c048190a5df30d53d8f0cf5 completed May 2, 2026, 8:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a123ae02598819095a9471e89a420c3 completed May 23, 2026, 11:40 p.m.
NEDg Description generation batch_6a123bead64881909ae531a7adbaafe1 completed May 23, 2026, 11:44 p.m.
NED2 Entity disambiguation (via description) batch_6a123c4f67388190a885b5ce89f9baa6 completed May 23, 2026, 11:46 p.m.
PD Predicate disambiguation batch_69f659cc571c819097e51e531961d812 completed May 2, 2026, 8:08 p.m.
Created at: April 27, 2026, 8:34 a.m.