Triple

T11170987
Position Surface form Disambiguated ID Type / Status
Subject Melissa E264271 entity
Predicate hasCognate P2525 FINISHED
Object Melitta
Melitta is a feminine given name of Greek origin, closely related to Melissa and historically associated with meanings like “bee” and “honey.”
 E908881 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: Melitta | Statement: [Melissa, hasCognate, Melitta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Melitta
Context triple: [Melissa, hasCognate, Melitta]
  • A. Faema
    Faema was a prominent professional Italian cycling team of the 1950s and 1960s, best known for sponsoring and supporting legendary riders such as Eddy Merckx.
  • B. Tassimo
    Tassimo is a single-serve hot beverage system brand known for its coffee and other drink pods, originally developed and marketed by Kraft Foods.
  • C. Nescafé
    Nescafé is a globally popular brand of instant coffee and related coffee products owned by Nestlé.
  • D. Lavazza
    Lavazza is a major Italian coffee company renowned worldwide for its espresso blends and coffee products.
  • E. Keurig
    Keurig is a popular American brand best known for its single-serve pod-based coffee makers widely used in homes and offices.
  • 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: Melitta
Triple: [Melissa, hasCognate, Melitta]
Generated description
Melitta is a feminine given name of Greek origin, closely related to Melissa and historically associated with meanings like “bee” and “honey.”
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Melitta
Target entity description: Melitta is a feminine given name of Greek origin, closely related to Melissa and historically associated with meanings like “bee” and “honey.”
  • A. Faema
    Faema was a prominent professional Italian cycling team of the 1950s and 1960s, best known for sponsoring and supporting legendary riders such as Eddy Merckx.
  • B. Tassimo
    Tassimo is a single-serve hot beverage system brand known for its coffee and other drink pods, originally developed and marketed by Kraft Foods.
  • C. Nescafé
    Nescafé is a globally popular brand of instant coffee and related coffee products owned by Nestlé.
  • D. Lavazza
    Lavazza is a major Italian coffee company renowned worldwide for its espresso blends and coffee products.
  • E. Keurig
    Keurig is a popular American brand best known for its single-serve pod-based coffee makers widely used in homes and offices.
  • 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_69d6aa9dafac8190bd90d2c74f661aa7 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e89660208190b1d9e91529f5d246 completed April 9, 2026, 5:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69e463b155a08190b361b38a39d25b1f completed April 19, 2026, 5:10 a.m.
NEDg Description generation batch_69e46c37efec81908aa709587c37569d completed April 19, 2026, 5:46 a.m.
NED2 Entity disambiguation (via description) batch_69e47292cdd08190b05c4c8b09f4f918 completed April 19, 2026, 6:13 a.m.
Created at: April 8, 2026, 9:29 p.m.