Triple

T31146011
Position Surface form Disambiguated ID Type / Status
Subject E105 European route E793924 entity
Predicate hasJunctionWith P1018 FINISHED
Object E50 European route
The E50 European route is a major transcontinental road in the international E-road network, running roughly west–east across Europe and connecting several key countries and cities.
 E1961450 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: E50 European route | Statement: [E105 European route, hasJunctionWith, E50 European route]
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: E50 European route
Triple: [E105 European route, hasJunctionWith, E50 European route]
Generated description
The E50 European route is a major transcontinental road in the international E-road network, running roughly west–east across Europe and connecting several key countries and cities.

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_69f224d41bb48190a5621cd1485e3a30 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f69799e82c8190823843f4986522ff completed May 3, 2026, 12:32 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2ad219b4d081908b1b55a13e4992b4 completed June 11, 2026, 3:19 p.m.
NEDg Description generation batch_6a2ae99f42348190baaef1836419f0f0 completed June 11, 2026, 5 p.m.
NED2 Entity disambiguation (via description) batch_6a2aea47ec748190ab027bd10c76d47b completed June 11, 2026, 5:03 p.m.
Created at: April 29, 2026, 9:06 p.m.