Triple

T33569760
Position Surface form Disambiguated ID Type / Status
Subject Santa Marta de Tormes E859861 entity
Predicate roadAccessTo P22549 FINISHED
Object A-50 motorway
The A-50 motorway is a Spanish highway in western Spain that connects the cities of Salamanca and Ávila, forming part of the regional road network linking Castile and León with central Spain.
 E2297543 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-50 motorway | Statement: [Santa Marta de Tormes, roadAccessTo, A-50 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-50 motorway
Triple: [Santa Marta de Tormes, roadAccessTo, A-50 motorway]
Generated description
The A-50 motorway is a Spanish highway in western Spain that connects the cities of Salamanca and Ávila, forming part of the regional road network linking Castile and León with central 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_69f3497c1d288190a844ea699914e038 completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6f742f9788190be2d5890a46bfe8b completed May 3, 2026, 7:20 a.m.
NED1 Entity disambiguation (via context triple) batch_6a839da6f5f08190a96642cbc6f0961a completed Aug. 17, 2026, 11:47 p.m.
NEDg Description generation batch_6a839df5660c81909ba788dcb621d509 completed Aug. 17, 2026, 11:49 p.m.
NED2 Entity disambiguation (via description) batch_6a839e62b8608190a4e20c2c08a10f9b completed Aug. 17, 2026, 11:50 p.m.
Created at: May 1, 2026, 1:40 a.m.