Triple

T3468117
Position Surface form Disambiguated ID Type / Status
Subject Lavazza E73185 entity
Predicate hasSubsidiary P254 FINISHED
Object Merrild
Merrild is a coffee brand and company known primarily in Northern Europe, offering a range of ground and whole-bean coffees.
 E361827 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: Merrild | Statement: [Lavazza, hasSubsidiary, Merrild]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Merrild
Context triple: [Lavazza, hasSubsidiary, Merrild]
  • A. Pattensen
    Pattensen is a small town in Lower Saxony, Germany, situated just south of Hanover in a predominantly rural and agricultural region.
  • B. Helleren
    Helleren is a small historic settlement in Norway known for its traditional houses built under a large rock overhang near the Jøssingfjord.
  • C. Svaneke
    Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
  • D. Bruun
    Bruun is the individual taxpayer who served as the respondent in the U.S. Supreme Court tax case Helvering v. Bruun.
  • E. Sommerda
    Sommerda is a town in the German state of Thuringia, known for its industrial history and central location near the Unstrut River.
  • 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: Merrild
Triple: [Lavazza, hasSubsidiary, Merrild]
Generated description
Merrild is a coffee brand and company known primarily in Northern Europe, offering a range of ground and whole-bean coffees.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Merrild
Target entity description: Merrild is a coffee brand and company known primarily in Northern Europe, offering a range of ground and whole-bean coffees.
  • A. Pattensen
    Pattensen is a small town in Lower Saxony, Germany, situated just south of Hanover in a predominantly rural and agricultural region.
  • B. Helleren
    Helleren is a small historic settlement in Norway known for its traditional houses built under a large rock overhang near the Jøssingfjord.
  • C. Svaneke
    Svaneke is a picturesque coastal town on the Danish island of Bornholm, known for its well-preserved half-timbered houses, harbor, and traditional smokehouses.
  • D. Bruun
    Bruun is the individual taxpayer who served as the respondent in the U.S. Supreme Court tax case Helvering v. Bruun.
  • E. Sommerda
    Sommerda is a town in the German state of Thuringia, known for its industrial history and central location near the Unstrut River.
  • 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_69ad85b224d481908ff8be51338d24ff completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adbb11ec5881908347bf92883a25ee completed March 8, 2026, 6:08 p.m.
NED1 Entity disambiguation (via context triple) batch_69b3680763608190acdd146dc7c0b239 completed March 13, 2026, 1:27 a.m.
NEDg Description generation batch_69b36c4d77448190abe198ec9d48597d completed March 13, 2026, 1:45 a.m.
NED2 Entity disambiguation (via description) batch_69b36cca06d48190bc72ad2e9bd9bdb5 completed March 13, 2026, 1:47 a.m.
Created at: March 8, 2026, 3:17 p.m.