Triple

T8334033
Position Surface form Disambiguated ID Type / Status
Subject Kimberly-Clark Corporation E195142 entity
Predicate brand P1500 FINISHED
Object Scott
Scott is a well-known Kimberly-Clark brand that offers paper-based hygiene and cleaning products such as toilet tissue, paper towels, and napkins.
 E724807 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: Scott | Statement: [Kimberly-Clark Corporation, brand, Scott]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Scott
Context triple: [Kimberly-Clark Corporation, brand, Scott]
  • A. Scott
    Scott is the middle name of Francis Scott Key, the American lawyer and poet who wrote the lyrics to the United States national anthem, "The Star-Spangled Banner."
  • B. Scott
    Scott is a central fictional character in Don DeLillo’s novel "Mao II," around whom key themes of identity, terrorism, and the role of the writer in contemporary society revolve.
  • C. Scott
    Scott is a common English-language surname borne by numerous notable individuals across fields such as literature, politics, science, and entertainment.
  • D. Kay
    Kay is a common diminutive or nickname for the given name Catherine.
  • E. Blaine
    Blaine is the laid-back, surfing-obsessed teenage protagonist of the 1993 comedy film "Airborne," known for his inline skating skills and culture clash after moving from California to Cincinnati.
  • 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: Scott
Triple: [Kimberly-Clark Corporation, brand, Scott]
Generated description
Scott is a well-known Kimberly-Clark brand that offers paper-based hygiene and cleaning products such as toilet tissue, paper towels, and napkins.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Scott
Target entity description: Scott is a well-known Kimberly-Clark brand that offers paper-based hygiene and cleaning products such as toilet tissue, paper towels, and napkins.
  • A. Scott
    Scott is the middle name of Francis Scott Key, the American lawyer and poet who wrote the lyrics to the United States national anthem, "The Star-Spangled Banner."
  • B. Scott
    Scott is a central fictional character in Don DeLillo’s novel "Mao II," around whom key themes of identity, terrorism, and the role of the writer in contemporary society revolve.
  • C. Scott
    Scott is a common English-language surname borne by numerous notable individuals across fields such as literature, politics, science, and entertainment.
  • D. Kay
    Kay is a common diminutive or nickname for the given name Catherine.
  • E. Blaine
    Blaine is a small coastal city in northwestern Washington State, located near the Canadian border.
  • 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_69ca82e87f2c8190bdb71ee29dfc642d completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb7fbe61f481909cde8ab2c42f89fc completed March 31, 2026, 8:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69cd955100448190862fd52d660585fd completed April 1, 2026, 9:59 p.m.
NEDg Description generation batch_69cda342c10881908ebafc7853815424 completed April 1, 2026, 10:59 p.m.
NED2 Entity disambiguation (via description) batch_69cdab736f208190a90bd4344b21a22c completed April 1, 2026, 11:34 p.m.
Created at: March 30, 2026, 5:57 p.m.