Triple

T27359140
Position Surface form Disambiguated ID Type / Status
Subject A-6 motorway E685770 entity
Predicate hasJunctionWith P1018 FINISHED
Object A-52 autovía
The A-52 autovía is a major Spanish dual carriageway in the northwest of the country that connects Galicia with inland regions and links to the A-6 motorway.
 E1772001 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 autovía | Statement: [A-6 motorway, hasJunctionWith, A-52 autovía]
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 autovía
Triple: [A-6 motorway, hasJunctionWith, A-52 autovía]
Generated description
The A-52 autovía is a major Spanish dual carriageway in the northwest of the country that connects Galicia with inland regions and links to the A-6 motorway.

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_6a12b2331fd48190bb5eaada961c082a completed May 24, 2026, 8:09 a.m.
NEDg Description generation batch_6a12b2e7e7a08190a7066b6fb6c07568 completed May 24, 2026, 8:12 a.m.
NED2 Entity disambiguation (via description) batch_6a12b3573a6c819093c3df4feaa23f0a completed May 24, 2026, 8:14 a.m.
Created at: April 27, 2026, 11:52 a.m.