Triple

T26258675
Position Surface form Disambiguated ID Type / Status
Subject Basel region E656784 entity
Predicate hasMuseumCenter P155201 FINISHED
Object Basel museum landscape
Basel museum landscape is the dense and diverse network of museums and cultural institutions in and around Basel, renowned for its rich collections spanning art, history, and science.
 E1713944 NE FINISHED

How this triple was built (3 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: Basel museum landscape | Statement: [Basel region, hasMuseumCenter, Basel museum landscape]
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: Basel museum landscape
Triple: [Basel region, hasMuseumCenter, Basel museum landscape]
Generated description
Basel museum landscape is the dense and diverse network of museums and cultural institutions in and around Basel, renowned for its rich collections spanning art, history, and science.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: hasMuseumCenter
Context triple: [Basel region, hasMuseumCenter, Basel museum landscape]
  • A. hasMuseumAt
    Indicates that a museum is located at or exists in a specified place or location.
  • B. hasMuseumComponent
    Indicates that something includes, contains, or is composed of a museum or museum-related part as one of its components.
  • C. hasMuseumFunction
    Indicates that an entity serves the role or performs the function of a museum.
  • D. hasMuseumCluster chosen
    Indicates that one entity contains, hosts, or is associated with a group or network of museums as a clustered unit.
  • E. hasNumberOfMuseums
    Indicates the quantity of museums associated with a given entity.
  • F. None of above.

Provenance (6 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_69ee5b4d25ac819086acb51184602576 completed April 26, 2026, 6:37 p.m.
NER Named-entity recognition batch_69f68f670b608190a0b6ab60d722b4e0 completed May 2, 2026, 11:57 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1185ad7bc08190ad17fe8180a1fe58 completed May 23, 2026, 10:47 a.m.
NEDg Description generation batch_6a11865b89b88190bb7786de150068e9 completed May 23, 2026, 10:50 a.m.
NED2 Entity disambiguation (via description) batch_6a1186d5ad5c81908645150955c109dc completed May 23, 2026, 10:52 a.m.
PD Predicate disambiguation batch_69f68b78f29481908cc8f390496dee97 completed May 2, 2026, 11:40 p.m.
Created at: April 26, 2026, 9:09 p.m.