Triple

T3318820
Position Surface form Disambiguated ID Type / Status
Subject South Gloucestershire E69742 entity
Predicate contains P35 FINISHED
Object Hallen
Hallen is a small village in South Gloucestershire, England, situated near Bristol and known for its rural character and proximity to major transport routes.
 E345947 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: Hallen | Statement: [South Gloucestershire, contains, Hallen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hallen
Context triple: [South Gloucestershire, contains, Hallen]
  • A. Halle
    Halle is a surname most notably borne by Morris Halle, a prominent linguist and phonologist.
  • B. Hof
    Hof is a town in northeastern Bavaria, Germany, known for its location near the Czech border and its regional cultural and economic significance.
  • C. Trêveszaal
    Trêveszaal is a historic meeting room in The Hague’s Binnenhof complex, traditionally used for Dutch government council meetings and important political deliberations.
  • D. Haller
    Haller is a surname most notably associated with Ernest Haller, an American cinematographer renowned for his work in classic Hollywood films.
  • E. Saalhof
    Saalhof is a historic medieval building complex in Frankfurt am Main that forms part of the city’s museum landscape and reflects its architectural and urban history.
  • 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: Hallen
Triple: [South Gloucestershire, contains, Hallen]
Generated description
Hallen is a small village in South Gloucestershire, England, situated near Bristol and known for its rural character and proximity to major transport routes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hallen
Target entity description: Hallen is a small village in South Gloucestershire, England, situated near Bristol and known for its rural character and proximity to major transport routes.
  • A. Halle
    Halle is a surname most notably borne by Morris Halle, a prominent linguist and phonologist.
  • B. Hof
    Hof is a town in northeastern Bavaria, Germany, known for its location near the Czech border and its regional cultural and economic significance.
  • C. Trêveszaal
    Trêveszaal is a historic meeting room in The Hague’s Binnenhof complex, traditionally used for Dutch government council meetings and important political deliberations.
  • D. Haller
    Haller is a surname most notably associated with Ernest Haller, an American cinematographer renowned for his work in classic Hollywood films.
  • E. Saalhof
    Saalhof is a historic medieval building complex in Frankfurt am Main that forms part of the city’s museum landscape and reflects its architectural and urban history.
  • 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_69ad85a0bb048190a5458d2738012d61 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb1151f3c8190911af4edac701116 completed March 8, 2026, 5:25 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2f40055a48190afe401a488d3ae34 completed March 12, 2026, 5:12 p.m.
NEDg Description generation batch_69b2fa0f9c348190a1d48003b96761a9 completed March 12, 2026, 5:38 p.m.
NED2 Entity disambiguation (via description) batch_69b3094548788190aac61165a982e5e9 completed March 12, 2026, 6:43 p.m.
Created at: March 8, 2026, 3:11 p.m.