Triple

T35487596
Position Surface form Disambiguated ID Type / Status
Subject Sestola E1025638 entity
Predicate hasSubdivision P747 FINISHED
Object Roncobotto
Roncobotto is a small locality or hamlet that forms part of the municipality of Sestola in the Emilia-Romagna region of northern Italy.
E2142607 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: Roncobotto | Statement: [Sestola, hasSubdivision, Roncobotto]
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: Roncobotto
Triple: [Sestola, hasSubdivision, Roncobotto]
Generated description
Roncobotto is a small locality or hamlet that forms part of the municipality of Sestola in the Emilia-Romagna region of northern Italy.

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_69f76dfbcdd881908c7b0b6bc502252b completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f796f1743c819088938812cbad7385 completed May 3, 2026, 6:41 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3840451f288190b99afb235df8b5cd completed June 21, 2026, 7:49 p.m.
NEDg Description generation batch_6a38412b825c8190bb041dcf4f238c3e completed June 21, 2026, 7:53 p.m.
NED2 Entity disambiguation (via description) batch_6a38425594c481908679cd38b14e31c8 completed June 21, 2026, 7:58 p.m.
Created at: May 3, 2026, 4:04 p.m.