Triple

T30181444
Position Surface form Disambiguated ID Type / Status
Subject Lichtensteig E767210 entity
Predicate railwayLine P848 FINISHED
Object Wil–Ebnat-Kappel line
The Wil–Ebnat-Kappel line is a Swiss standard-gauge railway route in the canton of St. Gallen that connects the town of Wil with Ebnat-Kappel, serving several intermediate communities.
E1902956 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: Wil–Ebnat-Kappel line | Statement: [Lichtensteig, railwayLine, Wil–Ebnat-Kappel 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: Wil–Ebnat-Kappel line
Triple: [Lichtensteig, railwayLine, Wil–Ebnat-Kappel line]
Generated description
The Wil–Ebnat-Kappel line is a Swiss standard-gauge railway route in the canton of St. Gallen that connects the town of Wil with Ebnat-Kappel, serving several intermediate communities.

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_69f2247cc3d88190811dec3face94bf5 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67f419e088190ba19a6ab9465d951 completed May 2, 2026, 10:48 p.m.
NED1 Entity disambiguation (via context triple) batch_6a275868e0108190be8f589481fb1e59 completed June 9, 2026, 12:03 a.m.
NEDg Description generation batch_6a275a7e7e78819088b7aef8057de369 completed June 9, 2026, 12:12 a.m.
NED2 Entity disambiguation (via description) batch_6a275b67290c8190bb71f367c87d8e09 completed June 9, 2026, 12:16 a.m.
Created at: April 29, 2026, 7:26 p.m.