Triple

T24512255
Position Surface form Disambiguated ID Type / Status
Subject Leszno railway station E606255 entity
Predicate railwayLine P848 FINISHED
Object Leszno–Głogów railway line
The Leszno–Głogów railway line is a regional rail route in western Poland connecting the cities of Leszno and Głogów and serving local passenger and freight traffic.
E1646244 NE FINISHED

How this triple was built (2 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: Leszno–Głogów railway line | Statement: [Leszno railway station, railwayLine, Leszno–Głogów railway line]
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: Leszno–Głogów railway line
Triple: [Leszno railway station, railwayLine, Leszno–Głogów railway line]
Generated description
The Leszno–Głogów railway line is a regional rail route in western Poland connecting the cities of Leszno and Głogów and serving local passenger and freight traffic.

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_69e2c4c725148190a4e41577c5cb409c completed April 17, 2026, 11:39 p.m.
NER Named-entity recognition batch_69f2a84d3da08190bfb7805cea1525d1 completed April 30, 2026, 12:54 a.m.
NED1 Entity disambiguation (via context triple) batch_6a100fe18884819088abb81ea72298d0 completed May 22, 2026, 8:12 a.m.
NEDg Description generation batch_6a10136871588190b4e4b4618ab7a400 completed May 22, 2026, 8:27 a.m.
NED2 Entity disambiguation (via description) batch_6a10141161b08190b471a7882a4d8aa0 completed May 22, 2026, 8:30 a.m.
Created at: April 18, 2026, 2:24 a.m.