Triple

T32341621
Position Surface form Disambiguated ID Type / Status
Subject Neder-Over-Heembeek E826331 entity
Predicate hasFacility P105 FINISHED
Object firefighting training center of Brussels
The firefighting training center of Brussels is the main facility where Brussels’ professional and volunteer firefighters receive practical and theoretical instruction, simulations, and certification for emergency response.
 E2001265 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: firefighting training center of Brussels | Statement: [Neder-Over-Heembeek, hasFacility, firefighting training center of Brussels]
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: firefighting training center of Brussels
Triple: [Neder-Over-Heembeek, hasFacility, firefighting training center of Brussels]
Generated description
The firefighting training center of Brussels is the main facility where Brussels’ professional and volunteer firefighters receive practical and theoretical instruction, simulations, and certification for emergency response.

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_69f34914dfc48190a390cd0720d9e86f completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6be25253881908d83f1028bfc90a9 completed May 3, 2026, 3:16 a.m.
NED1 Entity disambiguation (via context triple) batch_6a30572ca9e081908939473a0b8510c8 completed June 15, 2026, 7:49 p.m.
NEDg Description generation batch_6a30581f4878819083d6e510e2ca386e completed June 15, 2026, 7:53 p.m.
NED2 Entity disambiguation (via description) batch_6a3058d36d9c8190bec0ef32cf26c84f completed June 15, 2026, 7:56 p.m.
Created at: May 1, 2026, 12:48 a.m.