Triple

T33611676
Position Surface form Disambiguated ID Type / Status
Subject Melizzano E861002 entity
Predicate mayor P185 FINISHED
Object Rossano Insogna
Rossano Insogna is an Italian local politician who serves as the mayor of the municipality of Melizzano in the Campania region.
E2060383 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: Rossano Insogna | Statement: [Melizzano, mayor, Rossano Insogna]
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: Rossano Insogna
Triple: [Melizzano, mayor, Rossano Insogna]
Generated description
Rossano Insogna is an Italian local politician who serves as the mayor of the municipality of Melizzano in the Campania region.

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_69f3498037c88190a4500f002b5540e0 completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f6f7e34e8c819089dc407a13cc60f0 completed May 3, 2026, 7:23 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3611a18f0881909564c67a59e63fbe completed June 20, 2026, 4:05 a.m.
NEDg Description generation batch_6a36211ddc608190a780193bd49f6442 completed June 20, 2026, 5:11 a.m.
NED2 Entity disambiguation (via description) batch_6a3621a2708481909ce510ca390eddb7 completed June 20, 2026, 5:14 a.m.
Created at: May 1, 2026, 1:41 a.m.