Triple

T19075228
Position Surface form Disambiguated ID Type / Status
Subject UCCI E466885 entity
Predicate hasMember P10 FINISHED
Object Lima
Lima is the capital and largest city of Peru, known for its rich colonial history, coastal location, and status as the country’s political and cultural center.
 E2605 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: Lima | Statement: [UCCI, hasMember, Lima]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lima
Context triple: [UCCI, hasMember, Lima]
  • A. Lima
    Lima is a station on Buenos Aires’ historic Underground Line A, serving passengers in the city’s central area.
  • B. Lima
    Lima is the capital and largest city of Peru, known as a major political, economic, and cultural center on South America's Pacific coast.
  • C. Lima
    Lima is a subregion of Portugal’s Vinho Verde wine area, known for producing fresh, aromatic white wines from local grape varieties.
  • D. Sucre
    Sucre is a coastal state in northeastern Venezuela known for its Caribbean shoreline, fishing communities, and colonial-era towns.
  • E. Sucre
    Sucre is a neighborhood or locality within the Chapinero district of Bogotá, Colombia, known primarily as a residential and commercial urban area.
  • 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: Lima
Triple: [UCCI, hasMember, Lima]
Generated description
Lima is the capital and largest city of Peru, known for its rich colonial history, coastal location, and status as the country’s political and cultural center.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lima
Target entity description: Lima is the capital and largest city of Peru, known for its rich colonial history, coastal location, and status as the country’s political and cultural center.
  • A. Lima chosen
    Lima is the capital and largest city of Peru, known as a major political, economic, and cultural center on South America's Pacific coast.
  • B. Lima
    Lima is a station on Buenos Aires’ historic Underground Line A, serving passengers in the city’s central area.
  • C. Lima
    Lima is a subregion of Portugal’s Vinho Verde wine area, known for producing fresh, aromatic white wines from local grape varieties.
  • D. Sucre
    Sucre is a coastal state in northeastern Venezuela known for its Caribbean shoreline, fishing communities, and colonial-era towns.
  • E. Sucre
    Sucre is a neighborhood or locality within the Chapinero district of Bogotá, Colombia, known primarily as a residential and commercial urban area.
  • F. None of above.

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_69d8dd04f4488190b1121cc53ef2bfd6 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5e2e3c7b08190bf6448ead11ba916 completed April 20, 2026, 8:25 a.m.
NED1 Entity disambiguation (via context triple) batch_6a05d36054588190918fbf7272a28a1a completed May 14, 2026, 1:51 p.m.
NEDg Description generation batch_6a05d440fcdc81908c26a8aed70ce7b7 completed May 14, 2026, 1:55 p.m.
NED2 Entity disambiguation (via description) batch_6a05d4f48fdc8190ad1607287e8fe304 completed May 14, 2026, 1:58 p.m.
Created at: April 10, 2026, 12:04 p.m.