Triple

T2695743
Position Surface form Disambiguated ID Type / Status
Subject Espoo E58506 entity
Predicate hasShoppingCentre P4285 FINISHED
Object Sello
Sello is a major shopping and entertainment center located in Espoo, Finland, featuring a wide range of shops, restaurants, and cultural services.
 E290453 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: Sello | Statement: [Espoo, hasShoppingCentre, Sello]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Sello
Context triple: [Espoo, hasShoppingCentre, Sello]
  • A. Selloi
    The Selloi were the ancient priests associated with the oracle of Zeus at Dodona in Epirus, known from Homeric tradition.
  • B. Consuela
    Consuela is a recurring character on the animated TV series "Family Guy," known as a stubborn, heavily accented Latina maid who often says "No, no, no."
  • C. Pesa
    Pesa is a Polish manufacturer of rail vehicles, particularly known for producing modern trams and trains used in various European cities.
  • D. Zegelsem
    Zegelsem is a village in the Flemish Ardennes region of East Flanders, Belgium, known for its rural character and cobblestone cycling roads.
  • E. Sabetzki
    Sabetzki is a German surname most notably associated with Günther Sabetzki, a prominent ice hockey executive and former president of the International Ice Hockey Federation.
  • 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: Sello
Triple: [Espoo, hasShoppingCentre, Sello]
Generated description
Sello is a major shopping and entertainment center located in Espoo, Finland, featuring a wide range of shops, restaurants, and cultural services.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Sello
Target entity description: Sello is a major shopping and entertainment center located in Espoo, Finland, featuring a wide range of shops, restaurants, and cultural services.
  • A. Selloi
    The Selloi were the ancient priests associated with the oracle of Zeus at Dodona in Epirus, known from Homeric tradition.
  • B. Consuela
    Consuela is a recurring character on the animated TV series "Family Guy," known as a stubborn, heavily accented Latina maid who often says "No, no, no."
  • C. Pesa
    Pesa is a Polish manufacturer of rail vehicles, particularly known for producing modern trams and trains used in various European cities.
  • D. Zegelsem
    Zegelsem is a village in the Flemish Ardennes region of East Flanders, Belgium, known for its rural character and cobblestone cycling roads.
  • E. Sabetzki
    Sabetzki is a German surname most notably associated with Günther Sabetzki, a prominent ice hockey executive and former president of the International Ice Hockey Federation.
  • 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_69ab4ac269e481909cb317d79e68b75b completed March 6, 2026, 9:44 p.m.
NER Named-entity recognition batch_69abda2f7bf88190a1e3103dd014d871 completed March 7, 2026, 7:56 a.m.
NED1 Entity disambiguation (via context triple) batch_69afaf6aa78c8190b57be36042008361 completed March 10, 2026, 5:43 a.m.
NEDg Description generation batch_69afb01b48508190a9b668a7273ad422 completed March 10, 2026, 5:46 a.m.
NED2 Entity disambiguation (via description) batch_69afb09133788190862d4b24d77facc0 completed March 10, 2026, 5:48 a.m.
Created at: March 6, 2026, 9:55 p.m.