Triple

T5515327
Position Surface form Disambiguated ID Type / Status
Subject Cencosud E144667 entity
Predicate ownsBrand P1500 FINISHED
Object Paris
Paris is a major Chilean department store and retail chain offering a wide range of apparel, home goods, and consumer products.
 E528511 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: Paris | Statement: [Cencosud, ownsBrand, Paris]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Paris
Context triple: [Cencosud, ownsBrand, Paris]
  • A. Paris
    Paris is the capital and largest city of France, renowned for its historic architecture, art, fashion, and cultural influence worldwide.
  • B. Paris
    Paris is a prince of Troy in Greek mythology, best known for judging the beauty contest of the goddesses and for abducting Helen, which sparked the Trojan War.
  • C. Parigi
    Parigi is a coastal town that serves as the administrative center of Parigi Moutong Regency in Central Sulawesi, Indonesia.
  • D. Lyon
    Lyon is a major city in east-central France known for its historical and architectural landmarks, gastronomy, and role as a key economic and cultural center.
  • E. Paris Bar
    The Paris Bar is the professional association and regulatory body for lawyers practicing in Paris, France.
  • 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: Paris
Triple: [Cencosud, ownsBrand, Paris]
Generated description
Paris is a major Chilean department store and retail chain offering a wide range of apparel, home goods, and consumer products.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Paris
Target entity description: Paris is a major Chilean department store and retail chain offering a wide range of apparel, home goods, and consumer products.
  • A. Paris
    Paris is the capital and largest city of France, renowned for its historic architecture, art, fashion, and cultural influence worldwide.
  • B. Paris
    Paris is a prince of Troy in Greek mythology, best known for judging the beauty contest of the goddesses and for abducting Helen, which sparked the Trojan War.
  • C. Parigi
    Parigi is a coastal town that serves as the administrative center of Parigi Moutong Regency in Central Sulawesi, Indonesia.
  • D. Lyon
    Lyon is a major city in east-central France known for its historical and architectural landmarks, gastronomy, and role as a key economic and cultural center.
  • E. Paris Bar
    The Paris Bar is the professional association and regulatory body for lawyers practicing in Paris, France.
  • 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_69c008f77ff88190b0cd50ca207295d1 completed March 22, 2026, 3:21 p.m.
NER Named-entity recognition batch_69c01f5d1d188190b3a222a9ecf2a0e6 completed March 22, 2026, 4:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0277e577481909f1559082e482995 completed March 22, 2026, 5:31 p.m.
NEDg Description generation batch_69c033dc91e08190888fb6e94027fbdb completed March 22, 2026, 6:24 p.m.
NED2 Entity disambiguation (via description) batch_69c03460b21481908b78aa4bdc989d2c completed March 22, 2026, 6:26 p.m.
Created at: March 22, 2026, 3:33 p.m.