Triple

T14116419
Position Surface form Disambiguated ID Type / Status
Subject Downy E339786 entity
Predicate competitor P1375 FINISHED
Object Lenor
Lenor is a popular fabric softener and laundry care brand owned by Procter & Gamble and sold in many international markets.
 E1079812 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: Lenor | Statement: [Downy, competitor, Lenor]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Lenor
Context triple: [Downy, competitor, Lenor]
  • A. Jacintha
    Jacintha is a feminine given name, typically considered a variant of Hyacinth and associated with the flower of the same name.
  • B. Rosana
    Rosana is a municipality in the state of São Paulo, Brazil, known for hosting a campus of São Paulo State University (UNESP).
  • C. Rosana
    Rosana is a Brazilian professional footballer known for her successful international career and contributions to top women’s clubs, including Avaldsnes IL.
  • D. Lelia
    Lelia is the given name of A'Lelia Walker, an influential African-American businesswoman and patron of the arts during the Harlem Renaissance.
  • E. Luciana
    Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
  • 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: Lenor
Triple: [Downy, competitor, Lenor]
Generated description
Lenor is a popular fabric softener and laundry care brand owned by Procter & Gamble and sold in many international markets.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Lenor
Target entity description: Lenor is a popular fabric softener and laundry care brand owned by Procter & Gamble and sold in many international markets.
  • A. Jacintha
    Jacintha is a feminine given name, typically considered a variant of Hyacinth and associated with the flower of the same name.
  • B. Rosana
    Rosana is a municipality in the state of São Paulo, Brazil, known for hosting a campus of São Paulo State University (UNESP).
  • C. Rosana
    Rosana is a Brazilian professional footballer known for her successful international career and contributions to top women’s clubs, including Avaldsnes IL.
  • D. Lelia
    Lelia is the given name of A'Lelia Walker, an influential African-American businesswoman and patron of the arts during the Harlem Renaissance.
  • E. Luciana
    Luciana is a feminine given name of Latin origin, commonly used in Spanish- and Portuguese-speaking countries.
  • 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_69d81c6a95b481909e39111e0c1f31ee completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de6010a03c81909f5f160f8d1fa8fa completed April 14, 2026, 3:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcd0baa328819099511dfa7b9666d3 completed May 7, 2026, 5:49 p.m.
NEDg Description generation batch_69fcd3a8b8e08190b230ab8a2215145e completed May 7, 2026, 6:02 p.m.
NED2 Entity disambiguation (via description) batch_69fcd48acddc819087d626c4764bf148 completed May 7, 2026, 6:06 p.m.
Created at: April 9, 2026, 10:22 p.m.