Triple

T27146509
Position Surface form Disambiguated ID Type / Status
Subject Tergnier E681961 entity
Predicate servedByRailwayLine P848 FINISHED
Object Creil–Jeumont railway
The Creil–Jeumont railway is a major French rail line in northern France that connects the Paris region to the Belgian border, serving as an important route for both passenger and freight traffic.
 E1775715 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: Creil–Jeumont railway | Statement: [Tergnier, servedByRailwayLine, Creil–Jeumont 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: Creil–Jeumont railway
Triple: [Tergnier, servedByRailwayLine, Creil–Jeumont railway]
Generated description
The Creil–Jeumont railway is a major French rail line in northern France that connects the Paris region to the Belgian border, serving as an important route for both 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_69eefacca3888190b67238d380e8f28b completed April 27, 2026, 5:57 a.m.
NER Named-entity recognition batch_69f624c59988819084bf9be39c3fa44b completed May 2, 2026, 4:22 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12bbbd8d5c8190998f668166d98630 completed May 24, 2026, 8:50 a.m.
NEDg Description generation batch_6a12bd205e9c81908e89639719aa4ac2 completed May 24, 2026, 8:56 a.m.
NED2 Entity disambiguation (via description) batch_6a12bdc819e4819090b6ecae640773ab completed May 24, 2026, 8:58 a.m.
Created at: April 27, 2026, 9:11 a.m.