Triple

T30011615
Position Surface form Disambiguated ID Type / Status
Subject Wolvertem E762476 entity
Predicate partOf P40 FINISHED
Object municipality of Meise
The municipality of Meise is a local government area in the Belgian province of Flemish Brabant, known for its villages including Wolvertem and the National Botanic Garden of Belgium.
E1894631 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: municipality of Meise | Statement: [Wolvertem, partOf, municipality of Meise]
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: municipality of Meise
Triple: [Wolvertem, partOf, municipality of Meise]
Generated description
The municipality of Meise is a local government area in the Belgian province of Flemish Brabant, known for its villages including Wolvertem and the National Botanic Garden of Belgium.

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_69f2246b0c84819094f1250b6a02d277 completed April 29, 2026, 3:31 p.m.
NER Named-entity recognition batch_69f679808ed0819084770b0ac96494b1 completed May 2, 2026, 10:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a272210f9a08190905215acf0cd8e6f completed June 8, 2026, 8:12 p.m.
NEDg Description generation batch_6a2723349d708190aabfc1578a8e6e3f completed June 8, 2026, 8:16 p.m.
NED2 Entity disambiguation (via description) batch_6a2723e6c6648190801fec9c9fd7f557 completed June 8, 2026, 8:19 p.m.
Created at: April 29, 2026, 6:44 p.m.