Triple

T128803
Position Surface form Disambiguated ID Type / Status
Subject Lima E2605 entity
Predicate hasDistrict P459 FINISHED
Object La Molina
La Molina is an affluent residential and educational district located in the eastern part of Lima, Peru.
E16954 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: La Molina | Statement: [Lima, hasDistrict, La Molina]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: La Molina
Context triple: [Lima, hasDistrict, La Molina]
  • A. Malecón
    Malecón is a famous seaside promenade and seawall in Havana, Cuba, known for its ocean views, social life, and historic architecture.
  • B. Madrid
    Madrid is the capital and largest city of Spain, renowned for its rich cultural heritage, historic architecture, and vibrant arts and nightlife scenes.
  • C. Valencia
    Valencia is a major Spanish coastal city known for its historic architecture, vibrant culture, and significant role as a key Mediterranean trade and tourism hub.
  • D. Community of Madrid
    The Community of Madrid is an autonomous region in central Spain that includes the nation’s capital, Madrid, and serves as a major political, cultural, and economic hub.
  • E. Barcelona
    Barcelona is a major Spanish Mediterranean city renowned for its distinctive Catalan culture, Gaudí architecture, and vibrant arts and nightlife scenes.
  • 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: La Molina
Triple: [Lima, hasDistrict, La Molina]
Generated description
La Molina is an affluent residential and educational district located in the eastern part of Lima, Peru.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: La Molina
Target entity description: La Molina is an affluent residential and educational district located in the eastern part of Lima, Peru.
  • A. Malecón
    Malecón is a famous seaside promenade and seawall in Havana, Cuba, known for its ocean views, social life, and historic architecture.
  • B. Madrid
    Madrid is the capital and largest city of Spain, renowned for its rich cultural heritage, historic architecture, and vibrant arts and nightlife scenes.
  • C. Valencia
    Valencia is a major Spanish coastal city known for its historic architecture, vibrant culture, and significant role as a key Mediterranean trade and tourism hub.
  • D. Community of Madrid
    The Community of Madrid is an autonomous region in central Spain that includes the nation’s capital, Madrid, and serves as a major political, cultural, and economic hub.
  • E. Barcelona
    Barcelona is a major Spanish Mediterranean city renowned for its distinctive Catalan culture, Gaudí architecture, and vibrant arts and nightlife scenes.
  • 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_69a2520c0f3481908b0ed054a2fca8d0 completed Feb. 28, 2026, 2:25 a.m.
NER Named-entity recognition batch_69a2576518e0819096b35d8af7a4d1bd completed Feb. 28, 2026, 2:48 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2bf67902c8190b4e88c70d25439e8 completed Feb. 28, 2026, 10:11 a.m.
NEDg Description generation batch_69a2bfd78fa48190b7af7a5113c1bbf6 completed Feb. 28, 2026, 10:13 a.m.
NED2 Entity disambiguation (via description) batch_69a2c0ac71bc8190a80b2090d96c1e37 completed Feb. 28, 2026, 10:17 a.m.
Created at: Feb. 28, 2026, 2:30 a.m.