Triple

T12857230
Position Surface form Disambiguated ID Type / Status
Subject Migros E307487 entity
Predicate hasSubsidiary P254 FINISHED
Object Migrol
Migrol is a Swiss energy and fuel company best known for operating a nationwide network of petrol stations and heating oil services as part of the Migros Group.
 E1006143 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: Migrol | Statement: [Migros, hasSubsidiary, Migrol]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Migrol
Context triple: [Migros, hasSubsidiary, Migrol]
  • A. Milorg
    Milorg was the main Norwegian military resistance organization during World War II, coordinating sabotage, intelligence, and preparations for liberation under German occupation.
  • B. Musina
    Musina is a northern South African town in Limpopo Province, known as a key border and transport hub near Zimbabwe and for its history of copper and iron ore mining.
  • C. Mosina
    Mosina is an alternative name for Vurës, a language spoken on the island of Vanua Lava in Vanuatu.
  • D. Micol
    Micol is a given name that is an Italian variant of the name Michael, sharing its origins with the Hungarian form Miklós.
  • E. Fremulon
    Fremulon is a television production company founded by Michael Schur, best known for producing acclaimed comedy series such as Brooklyn Nine-Nine.
  • 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: Migrol
Triple: [Migros, hasSubsidiary, Migrol]
Generated description
Migrol is a Swiss energy and fuel company best known for operating a nationwide network of petrol stations and heating oil services as part of the Migros Group.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Migrol
Target entity description: Migrol is a Swiss energy and fuel company best known for operating a nationwide network of petrol stations and heating oil services as part of the Migros Group.
  • A. Milorg
    Milorg was the main Norwegian military resistance organization during World War II, coordinating sabotage, intelligence, and preparations for liberation under German occupation.
  • B. Musina
    Musina is a northern South African town in Limpopo Province, known as a key border and transport hub near Zimbabwe and for its history of copper and iron ore mining.
  • C. Mosina
    Mosina is an alternative name for Vurës, a language spoken on the island of Vanua Lava in Vanuatu.
  • D. Micol
    Micol is a given name that is an Italian variant of the name Michael, sharing its origins with the Hungarian form Miklós.
  • E. Fremulon
    Fremulon is a television production company founded by Michael Schur, best known for producing acclaimed comedy series such as Brooklyn Nine-Nine.
  • 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_69d7bdf5e7cc8190be357278bc5ba3bb completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d970231ce48190a4eabc4b8c24a3ff completed April 10, 2026, 9:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f69ba9a53c81908e9ed120f6cb94af completed May 3, 2026, 12:49 a.m.
NEDg Description generation batch_69f69d4a2ab481908460797a8ee6bc8d completed May 3, 2026, 12:56 a.m.
NED2 Entity disambiguation (via description) batch_69f69dc8b86c81908557fa8538e942de completed May 3, 2026, 12:58 a.m.
Created at: April 9, 2026, 5:37 p.m.