Triple

T26550360
Position Surface form Disambiguated ID Type / Status
Subject Comune di Padova E671661 entity
Predicate roadNetwork P385 FINISHED
Object A13 motorway
The A13 motorway is a major Italian highway in northeastern Italy that connects the cities of Bologna and Padua, serving as an important route for regional and long-distance traffic.
 E2204437 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: A13 motorway | Statement: [Comune di Padova, roadNetwork, A13 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: A13 motorway
Triple: [Comune di Padova, roadNetwork, A13 motorway]
Generated description
The A13 motorway is a major Italian highway in northeastern Italy that connects the cities of Bologna and Padua, serving as an important route for regional and long-distance traffic.

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_69eeb32163f08190af5f81282738e27a completed April 27, 2026, 12:51 a.m.
NER Named-entity recognition batch_69f614391e4c81908b14843830640ad1 completed May 2, 2026, 3:11 p.m.
NED1 Entity disambiguation (via context triple) batch_6a7a6c1e2eb08190a9c35e3165ad8906 completed Aug. 11, 2026, 12:26 a.m.
NEDg Description generation batch_6a7a6c8b55ac8190b0ccd22d9e82874d completed Aug. 11, 2026, 12:27 a.m.
NED2 Entity disambiguation (via description) batch_6a7a6cd89a70819080d7292940bb3e43 completed Aug. 11, 2026, 12:29 a.m.
Created at: April 27, 2026, 1:47 a.m.