Triple

T27430947
Position Surface form Disambiguated ID Type / Status
Subject Gosselies E690633 entity
Predicate partOfUrbanArea P294 FINISHED
Object Charleroi metropolitan area
The Charleroi metropolitan area is a major urban and industrial agglomeration in Wallonia, Belgium, centered on the city of Charleroi and its surrounding suburbs.
E95883 NE FINISHED

How this triple was built (2 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: Charleroi metropolitan area | Statement: [Gosselies, partOfUrbanArea, Charleroi metropolitan area]
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: Charleroi metropolitan area
Triple: [Gosselies, partOfUrbanArea, Charleroi metropolitan area]
Generated description
The Charleroi metropolitan area is a major urban and industrial agglomeration in Wallonia, Belgium, centered on the city of Charleroi and its surrounding suburbs.

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_69ef52003fb48190b0f1295246182a86 completed April 27, 2026, 12:09 p.m.
NER Named-entity recognition batch_69f62d59a6e88190a9042398d754c304 completed May 2, 2026, 4:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12c5995ffc8190bac3a5e1c6b5e9d1 completed May 24, 2026, 9:32 a.m.
NEDg Description generation batch_6a12c6431ff8819092864b074cc494b8 completed May 24, 2026, 9:34 a.m.
NED2 Entity disambiguation (via description) batch_6a12c6c3a8fc819083942c89ff00352b completed May 24, 2026, 9:37 a.m.
Created at: April 27, 2026, 12:42 p.m.