Triple

T18411741
Position Surface form Disambiguated ID Type / Status
Subject Rho-Fiera exhibition area E441773 entity
Predicate near P350 FINISHED
Object Pero
Pero is a municipality in the Metropolitan City of Milan in northern Italy, known for its proximity to major trade fair and exhibition centers.
 E1322739 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: Pero | Statement: [Rho-Fiera exhibition area, near, Pero]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Pero
Context triple: [Rho-Fiera exhibition area, near, Pero]
  • A. Pero
    Pero is a common South Slavic diminutive form of the male given name Petar (Peter).
  • B. Pero
    Pero is a West Chadic language spoken in parts of Nigeria.
  • C. Peca
    Peca is the surname of Michael Peca, a former professional ice hockey player and two-time Selke Trophy–winning center in the NHL.
  • D. Piojó
    Piojó is a small municipality and town in northern Colombia, located in the Caribbean region within the department of Atlántico.
  • E. Perche
    Perche is a historic rural region in northwestern France known for its rolling countryside, forests, and traditional manors.
  • 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: Pero
Triple: [Rho-Fiera exhibition area, near, Pero]
Generated description
Pero is a municipality in the Metropolitan City of Milan in northern Italy, known for its proximity to major trade fair and exhibition centers.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Pero
Target entity description: Pero is a municipality in the Metropolitan City of Milan in northern Italy, known for its proximity to major trade fair and exhibition centers.
  • A. Pero
    Pero is a West Chadic language spoken in parts of Nigeria.
  • B. Pero
    Pero is a common South Slavic diminutive form of the male given name Petar (Peter).
  • C. Peca
    Peca is the surname of Michael Peca, a former professional ice hockey player and two-time Selke Trophy–winning center in the NHL.
  • D. Piojó
    Piojó is a small municipality and town in northern Colombia, located in the Caribbean region within the department of Atlántico.
  • E. Perche
    Perche is a historic rural region in northwestern France known for its rolling countryside, forests, and traditional manors.
  • 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_69d8b9eb8a508190a942fd75ebd8b1dc completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e51a24e130819082b92f98d8d5fa3c completed April 19, 2026, 6:08 p.m.
NED1 Entity disambiguation (via context triple) batch_6a03e24be008819083d4ab019e8c66a6 completed May 13, 2026, 2:30 a.m.
NEDg Description generation batch_6a03e7a576b881908267669e3ed394cf completed May 13, 2026, 2:53 a.m.
NED2 Entity disambiguation (via description) batch_6a03e8046c188190b860addd21ee8346 completed May 13, 2026, 2:55 a.m.
Created at: April 10, 2026, 10:47 a.m.