Triple

T27359137
Position Surface form Disambiguated ID Type / Status
Subject A-6 motorway E685770 entity
Predicate hasJunctionWith P1018 FINISHED
Object A-52 motorway
The A-52 motorway is a Spanish highway that connects the regions of Galicia and Castile and León, forming part of an important east–west transport corridor in the northwest of Spain.
 E2294096 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: A-52 motorway | Statement: [A-6 motorway, hasJunctionWith, A-52 motorway]
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: A-52 motorway
Triple: [A-6 motorway, hasJunctionWith, A-52 motorway]
Generated description
The A-52 motorway is a Spanish highway that connects the regions of Galicia and Castile and León, forming part of an important east–west transport corridor in the northwest of Spain.

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_6a7b7a890aa48190868c3f8e99b9243c completed Aug. 11, 2026, 7:39 p.m.
NEDg Description generation batch_6a7b7ae94da4819089f0fc66dabed666 completed Aug. 11, 2026, 7:41 p.m.
NED2 Entity disambiguation (via description) batch_6a7b7bbb17848190b9d674968c543361 completed Aug. 11, 2026, 7:44 p.m.
Created at: April 27, 2026, 11:52 a.m.