Triple

T35241619
Position Surface form Disambiguated ID Type / Status
Subject canton of Les Coteaux de Moselle E1017536 entity
Predicate contains P35 FINISHED
Object commune of Vernéville
The commune of Vernéville is a small municipality in northeastern France’s Moselle department, characterized by its rural setting and local agricultural and residential activities.
E2130820 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: commune of Vernéville | Statement: [canton of Les Coteaux de Moselle, contains, commune of Vernéville]
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: commune of Vernéville
Triple: [canton of Les Coteaux de Moselle, contains, commune of Vernéville]
Generated description
The commune of Vernéville is a small municipality in northeastern France’s Moselle department, characterized by its rural setting and local agricultural and residential activities.

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_69f76de235048190b990070c23c51b6b completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f78ef451dc8190bdd41217a28475e2 completed May 3, 2026, 6:07 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38042388b881909cc79807154aa556 completed June 21, 2026, 3:32 p.m.
NEDg Description generation batch_6a3804c6a8788190ac07c698d78a290d completed June 21, 2026, 3:35 p.m.
NED2 Entity disambiguation (via description) batch_6a38057811848190a12d3e760db65b2d completed June 21, 2026, 3:38 p.m.
Created at: May 3, 2026, 4:02 p.m.