Triple

T7949323
Position Surface form Disambiguated ID Type / Status
Subject Milan Malpensa Airport E184573 entity
Predicate nearLocality P350 FINISHED
Object Ferno
Ferno is a small municipality in the Lombardy region of northern Italy, situated in the province of Varese.
 E704591 NE FINISHED

How this triple was built (4 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: Ferno | Statement: [Milan Malpensa Airport, nearLocality, Ferno]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Ferno
Context triple: [Milan Malpensa Airport, nearLocality, Ferno]
  • A. Ferro
    Ferro is an alternative name for El Hierro, the smallest and westernmost of Spain’s Canary Islands in the Atlantic Ocean.
  • B. Griante
    Griante is a small lakeside village on Lake Como in Lombardy, Italy, known for its scenic views and historic villas.
  • C. Jenefar
    Jenefar is an alternative spelling of the given name Jennifer, typically used as a feminine first name.
  • D. Fiser
    Fiser is a surname variant of Fischer, commonly associated with Central or Eastern European origins.
  • E. Fuhse
    Fuhse is a river in Lower Saxony, Germany, that flows through several towns before joining the Aller River.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Ferno
Triple: [Milan Malpensa Airport, nearLocality, Ferno]
Generated description
Ferno is a small municipality in the Lombardy region of northern Italy, situated in the province of Varese.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Ferno
Target entity description: Ferno is a small municipality in the Lombardy region of northern Italy, situated in the province of Varese.
  • A. Ferro
    Ferro is an alternative name for El Hierro, the smallest and westernmost of Spain’s Canary Islands in the Atlantic Ocean.
  • B. Griante
    Griante is a small lakeside village on Lake Como in Lombardy, Italy, known for its scenic views and historic villas.
  • C. Jenefar
    Jenefar is an alternative spelling of the given name Jennifer, typically used as a feminine first name.
  • D. Fiser
    Fiser is a surname variant of Fischer, commonly associated with Central or Eastern European origins.
  • E. Fuhse
    Fuhse is a river in Lower Saxony, Germany, that flows through several towns before joining the Aller River.
  • F. None of above. chosen

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_69ca8292cba881908a64427b938dac47 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb3b2d09a4819097aa49e29a5426ec completed March 31, 2026, 3:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69cbe03c7d308190aec1172415be995c completed March 31, 2026, 2:54 p.m.
NEDg Description generation batch_69cbe4383d0c819085e7c95e7b0be16e completed March 31, 2026, 3:11 p.m.
NED2 Entity disambiguation (via description) batch_69cc34a83cec81908aba7afbaea53449 completed March 31, 2026, 8:55 p.m.
Created at: March 30, 2026, 5:10 p.m.