Triple

T37341932
Position Surface form Disambiguated ID Type / Status
Subject Madrid–Huesca high-speed rail line E927060 entity
Predicate usesRollingStock P5426 FINISHED
Object Renfe Class 112
The Renfe Class 112 is a series of Spanish high-speed electric multiple unit trains operated by Renfe for long-distance AVE services.
 E2226514 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: Renfe Class 112 | Statement: [Madrid–Huesca high-speed rail line, usesRollingStock, Renfe Class 112]
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: Renfe Class 112
Triple: [Madrid–Huesca high-speed rail line, usesRollingStock, Renfe Class 112]
Generated description
The Renfe Class 112 is a series of Spanish high-speed electric multiple unit trains operated by Renfe for long-distance AVE services.

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_69f76eb4e8a881908bd40da28f36fc7e completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fb5b9692448190a970a73881a44e27 completed May 6, 2026, 3:17 p.m.
NED1 Entity disambiguation (via context triple) batch_6a40823c560081908d137a2108e1703e completed June 28, 2026, 2:09 a.m.
NEDg Description generation batch_6a4083266990819092378557ff164ce0 completed June 28, 2026, 2:12 a.m.
NED2 Entity disambiguation (via description) batch_6a4083cb3a90819082ec8d56067094a0 completed June 28, 2026, 2:15 a.m.
Created at: May 3, 2026, 4:16 p.m.