Triple

T27359144
Position Surface form Disambiguated ID Type / Status
Subject A-6 motorway E685770 entity
Predicate partOf P40 FINISHED
Object European route E90
European route E90 is a major trans-European east–west highway corridor that stretches from Portugal across southern Europe to Turkey, linking multiple countries and key Mediterranean cities.
 E1779210 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: European route E90 | Statement: [A-6 motorway, partOf, European route E90]
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: European route E90
Triple: [A-6 motorway, partOf, European route E90]
Generated description
European route E90 is a major trans-European east–west highway corridor that stretches from Portugal across southern Europe to Turkey, linking multiple countries and key Mediterranean 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_69ef14887c288190931b8431fdbf53c4 completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f62c21405c81909c486d3b9d9be274 completed May 2, 2026, 4:53 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12d0b94b388190b279d45aa37c1120 completed May 24, 2026, 10:19 a.m.
NEDg Description generation batch_6a12d10cedc88190bd016635b51fd8c8 completed May 24, 2026, 10:21 a.m.
NED2 Entity disambiguation (via description) batch_6a12d19279a881908236e7043970cd42 completed May 24, 2026, 10:23 a.m.
Created at: April 27, 2026, 11:52 a.m.