Triple

T2761631
Position Surface form Disambiguated ID Type / Status
Subject Grand Central Birmingham E61232 entity
Predicate architect P184 FINISHED
Object Haskoll
Haskoll is a British architectural practice known for designing major retail and mixed-use developments, including prominent shopping centres in the UK.
 E296537 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: Haskoll | Statement: [Grand Central Birmingham, architect, Haskoll]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Haskoll
Context triple: [Grand Central Birmingham, architect, Haskoll]
  • A. Holthees
    Holthees is a small village in the Dutch province of North Brabant, known for its rural character and historic church.
  • B. Haldenstein
    Haldenstein is a small Swiss village in the canton of Graubünden, known in architecture circles as the longtime base of renowned architect Peter Zumthor.
  • C. Hassel
    Hassel is a Norwegian surname most notably borne by Nobel Prize–winning chemist Odd Hassel.
  • D. Ryhall
    Ryhall is a village and civil parish in the South Kesteven district of Lincolnshire, England, known for its historic church and rural character.
  • E. Hase
    The Hase is a river in northwestern Germany that flows through Lower Saxony and North Rhine-Westphalia, passing towns such as Quakenbrück before joining the Ems.
  • 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: Haskoll
Triple: [Grand Central Birmingham, architect, Haskoll]
Generated description
Haskoll is a British architectural practice known for designing major retail and mixed-use developments, including prominent shopping centres in the UK.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Haskoll
Target entity description: Haskoll is a British architectural practice known for designing major retail and mixed-use developments, including prominent shopping centres in the UK.
  • A. Holthees
    Holthees is a small village in the Dutch province of North Brabant, known for its rural character and historic church.
  • B. Haldenstein
    Haldenstein is a small Swiss village in the canton of Graubünden, known in architecture circles as the longtime base of renowned architect Peter Zumthor.
  • C. Hassel
    Hassel is a Norwegian surname most notably borne by Nobel Prize–winning chemist Odd Hassel.
  • D. Ryhall
    Ryhall is a village and civil parish in the South Kesteven district of Lincolnshire, England, known for its historic church and rural character.
  • E. Hase
    The Hase is a river in northwestern Germany that flows through Lower Saxony and North Rhine-Westphalia, passing towns such as Quakenbrück before joining the Ems.
  • 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_69ab4b7bab6c8190a5c2efef19a8ef34 completed March 6, 2026, 9:47 p.m.
NER Named-entity recognition batch_69abdd5072548190946f037c38aabb02 completed March 7, 2026, 8:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69afc04365448190b37e5ed16c16d650 completed March 10, 2026, 6:54 a.m.
NEDg Description generation batch_69afc0b6368081908e2520ac6680a409 completed March 10, 2026, 6:56 a.m.
NED2 Entity disambiguation (via description) batch_69afc145e61881908c0eeae455b02a78 completed March 10, 2026, 6:59 a.m.
Created at: March 6, 2026, 9:57 p.m.