Triple

T31177274
Position Surface form Disambiguated ID Type / Status
Subject Liévin E794784 entity
Predicate hasUrbanUnit P156670 FINISHED
Object Lens–Liévin
Lens–Liévin is a major urban and former coal-mining conurbation in northern France centered around the cities of Lens and Liévin.
 E1949378 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: Lens–Liévin | Statement: [Liévin, hasUrbanUnit, Lens–Liévin]
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: Lens–Liévin
Triple: [Liévin, hasUrbanUnit, Lens–Liévin]
Generated description
Lens–Liévin is a major urban and former coal-mining conurbation in northern France centered around the cities of Lens and Liévin.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: hasUrbanUnit
Context triple: [Liévin, hasUrbanUnit, Lens–Liévin]
  • A. hasUrbanUnits chosen
    Indicates that an entity possesses or includes one or more urban units (such as cities, towns, or urbanized areas) within its scope or structure.
  • B. hasUrbanSectionsIn
    Indicates that an entity includes or contains sections that are classified as urban within a specified area or region.
  • C. hasUrbanFunction
    Indicates that an entity serves a specific role or purpose within an urban context, such as providing services, infrastructure, or activities typical of a city environment.
  • D. hasUrbanDistrictFunction
    Indicates that an entity serves the administrative or functional role of an urban district within a larger territorial or governance structure.
  • E. hasUrbanFeature
    Indicates that a place or area possesses a specific urban element or infrastructure feature (such as roads, parks, or buildings) as part of its built environment.
  • 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_69f224d5b9708190b6ca79ad2fd3a28a completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f6a916d2e08190bafc01cba73b6469 completed May 3, 2026, 1:47 a.m.
NED1 Entity disambiguation (via context triple) batch_6a29473669d8819097ebdea96de47361 completed June 10, 2026, 11:15 a.m.
NEDg Description generation batch_6a2947f4f9508190b93d7b28dd1cba01 completed June 10, 2026, 11:18 a.m.
NED2 Entity disambiguation (via description) batch_6a2948bb63e4819083a1e9d149cddac6 completed June 10, 2026, 11:21 a.m.
PD Predicate disambiguation batch_69f6a7548eb48190a69b60a3c6ad53b9 completed May 3, 2026, 1:39 a.m.
Created at: April 29, 2026, 9:08 p.m.