Triple

T3333441
Position Surface form Disambiguated ID Type / Status
Subject Luxembourg City E70084 entity
Predicate hasDistrict P459 FINISHED
Object Grund
Grund is a historic, picturesque quarter of Luxembourg City known for its riverside setting, old architecture, and vibrant nightlife.
 E349450 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: Grund | Statement: [Luxembourg City, hasDistrict, Grund]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Grund
Context triple: [Luxembourg City, hasDistrict, Grund]
  • A. Boden
    Boden is a northern Swedish town known for its strategic military significance and large army garrison.
  • B. Jordbro
    Jordbro is a suburban district in the southern Stockholm region of Sweden, known for its residential areas, industrial zone, and commuter rail connections within Haninge Municipality.
  • C. Zemin
    Zemin is a Chinese given name notably borne by Mao Zemin, a revolutionary and younger brother of Mao Zedong.
  • D. Bedrock
    Bedrock is the prehistoric, stone-age town that serves as the primary setting of the animated television series "The Flintstones."
  • E. Gard
    Gard is a department in southern France known for its Mediterranean landscapes, historic towns, and the famous Pont du Gard Roman aqueduct.
  • 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: Grund
Triple: [Luxembourg City, hasDistrict, Grund]
Generated description
Grund is a historic, picturesque quarter of Luxembourg City known for its riverside setting, old architecture, and vibrant nightlife.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Grund
Target entity description: Grund is a historic, picturesque quarter of Luxembourg City known for its riverside setting, old architecture, and vibrant nightlife.
  • A. Boden
    Boden is a northern Swedish town known for its strategic military significance and large army garrison.
  • B. Jordbro
    Jordbro is a suburban district in the southern Stockholm region of Sweden, known for its residential areas, industrial zone, and commuter rail connections within Haninge Municipality.
  • C. Zemin
    Zemin is a Chinese given name notably borne by Mao Zemin, a revolutionary and younger brother of Mao Zedong.
  • D. Bedrock
    Bedrock is the prehistoric, stone-age town that serves as the primary setting of the animated television series "The Flintstones."
  • E. Gard
    Gard is a department in southern France known for its Mediterranean landscapes, historic towns, and the famous Pont du Gard Roman aqueduct.
  • 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_69ad85a24f208190bcf83131bfed3521 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb194960081909333c855f06d8b03 completed March 8, 2026, 5:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69b31a867cac81909ddde955c1752ab8 completed March 12, 2026, 7:56 p.m.
NEDg Description generation batch_69b31c393f20819098d5761372d6a980 completed March 12, 2026, 8:04 p.m.
NED2 Entity disambiguation (via description) batch_69b3206be2748190874560701dc1ed18 completed March 12, 2026, 8:22 p.m.
Created at: March 8, 2026, 3:12 p.m.