Triple

T4821057
Position Surface form Disambiguated ID Type / Status
Subject Gillian Wearing E107710 entity
Predicate familyName P18 FINISHED
Object Wearing
Wearing is a surname most prominently associated with British conceptual artist and photographer Gillian Wearing.
 E471302 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: Wearing | Statement: [Gillian Wearing, familyName, Wearing]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Wearing
Context triple: [Gillian Wearing, familyName, Wearing]
  • A. Outfit
    Outfit is a common nickname for the Chicago Outfit, a powerful Italian-American organized crime syndicate historically based in Chicago.
  • B. Tailo
    Tailo is a widely used Latin-based romanization system for writing Taiwanese Hokkien, employed in education, literature, and language preservation.
  • C. Boots
    Boots is a major British pharmacy-led health and beauty retailer and pharmacy chain with stores across the United Kingdom and other countries.
  • D. Boots
    Boots is an American singer, songwriter, and record producer best known for his influential work on Beyoncé’s self-titled 2013 album.
  • E. Adorn
    "Adorn" is a Grammy-winning R&B single by American singer Miguel, known for its smooth vocals and sensual, minimalist production.
  • 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: Wearing
Triple: [Gillian Wearing, familyName, Wearing]
Generated description
Wearing is a surname most prominently associated with British conceptual artist and photographer Gillian Wearing.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Wearing
Target entity description: Wearing is a surname most prominently associated with British conceptual artist and photographer Gillian Wearing.
  • A. Outfit
    Outfit is a common nickname for the Chicago Outfit, a powerful Italian-American organized crime syndicate historically based in Chicago.
  • B. Tailo
    Tailo is a widely used Latin-based romanization system for writing Taiwanese Hokkien, employed in education, literature, and language preservation.
  • C. Boots
    Boots is a major British pharmacy-led health and beauty retailer and pharmacy chain with stores across the United Kingdom and other countries.
  • D. Boots
    Boots is an American singer, songwriter, and record producer best known for his influential work on Beyoncé’s self-titled 2013 album.
  • E. Adorn
    "Adorn" is a Grammy-winning R&B single by American singer Miguel, known for its smooth vocals and sensual, minimalist production.
  • 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_69bd43f9efa081908314cb3e94fa1695 completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6c99b46c8190b6fbcf9f98b9e993 completed March 20, 2026, 3:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69be4dc02118819093f4dfad16c6085f completed March 21, 2026, 7:50 a.m.
NEDg Description generation batch_69be4e38bccc81909102f922fd395568 completed March 21, 2026, 7:52 a.m.
NED2 Entity disambiguation (via description) batch_69be4ea8fa708190909e26268b49b678 completed March 21, 2026, 7:54 a.m.
Created at: March 20, 2026, 1:24 p.m.