Triple

T27042864
Position Surface form Disambiguated ID Type / Status
Subject Gare de Riom – Châtel-Guyon E684541 entity
Predicate railwayLine P848 FINISHED
Object Paris–Clermont-Ferrand railway
The Paris–Clermont-Ferrand railway is a major French mainline route connecting the capital Paris with the central city of Clermont-Ferrand, serving numerous regional towns along the way.
 E1752854 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: Paris–Clermont-Ferrand railway | Statement: [Gare de Riom – Châtel-Guyon, railwayLine, Paris–Clermont-Ferrand railway]
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: Paris–Clermont-Ferrand railway
Triple: [Gare de Riom – Châtel-Guyon, railwayLine, Paris–Clermont-Ferrand railway]
Generated description
The Paris–Clermont-Ferrand railway is a major French mainline route connecting the capital Paris with the central city of Clermont-Ferrand, serving numerous regional towns along the way.

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_69ef148193c48190bb1a0cfae6a407c4 completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f6226ea9488190a30b83ccccbc756f completed May 2, 2026, 4:12 p.m.
NED1 Entity disambiguation (via context triple) batch_6a123ac45a04819083d717a011e4ea29 completed May 23, 2026, 11:39 p.m.
NEDg Description generation batch_6a123ba9aea081909f20ff78ab91747e completed May 23, 2026, 11:43 p.m.
NED2 Entity disambiguation (via description) batch_6a123c56268c81909d0e71dad0aeab01 completed May 23, 2026, 11:46 p.m.
Created at: April 27, 2026, 8:07 a.m.