Triple

T24293384
Position Surface form Disambiguated ID Type / Status
Subject Riddes E605884 entity
Predicate servedBy P82 FINISHED
Object Martigny–Sion railway line
The Martigny–Sion railway line is a regional rail route in the Swiss canton of Valais that connects key towns in the Rhône Valley and forms part of the area’s local passenger transport network.
 E1645562 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: Martigny–Sion railway line | Statement: [Riddes, servedBy, Martigny–Sion 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: Martigny–Sion railway line
Triple: [Riddes, servedBy, Martigny–Sion railway line]
Generated description
The Martigny–Sion railway line is a regional rail route in the Swiss canton of Valais that connects key towns in the Rhône Valley and forms part of the area’s local passenger transport network.

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_69e29549335881909cbf27adcaba1cf0 completed April 17, 2026, 8:17 p.m.
NER Named-entity recognition batch_69f2915870c8819089c14de19ba2a5c5 completed April 29, 2026, 11:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a10045919a08190a74b0f8e10f9f48a completed May 22, 2026, 7:23 a.m.
NEDg Description generation batch_6a10095d986881909082cc5a32b6d56e completed May 22, 2026, 7:44 a.m.
NED2 Entity disambiguation (via description) batch_6a1009d311b08190acf35a3ed552b9c1 completed May 22, 2026, 7:46 a.m.
Created at: April 18, 2026, 12:09 a.m.