Triple

T32532077
Position Surface form Disambiguated ID Type / Status
Subject Sombreffe E831481 entity
Predicate containsAdministrativeTerritorialEntity P747 FINISHED
Object Sombreffe (village)
Sombreffe (village) is a small settlement in the municipality of Sombreffe in Wallonia, Belgium, known for its rural character and historical surroundings.
E831484 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: Sombreffe (village) | Statement: [Sombreffe, containsAdministrativeTerritorialEntity, Sombreffe (village)]
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: Sombreffe (village)
Triple: [Sombreffe, containsAdministrativeTerritorialEntity, Sombreffe (village)]
Generated description
Sombreffe (village) is a small settlement in the municipality of Sombreffe in Wallonia, Belgium, known for its rural character and historical surroundings.

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_69f34924b1cc8190ad3aca0c0f012a7e completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6c568ae488190aa7c3bb8d65a655a completed May 3, 2026, 3:47 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3470723c5c8190a296ec97c209368f completed June 18, 2026, 10:25 p.m.
NEDg Description generation batch_6a3473850f448190b590c2b4e74b5e38 completed June 18, 2026, 10:39 p.m.
NED2 Entity disambiguation (via description) batch_6a3473eca0e88190b223ea9ae6d94e5c completed June 18, 2026, 10:40 p.m.
Created at: May 1, 2026, 1:01 a.m.