Triple

T35651958
Position Surface form Disambiguated ID Type / Status
Subject Lyon urban road network E1030174 entity
Predicate includesRoad P85887 FINISHED
Object Boulevard Périphérique Nord de Lyon
Boulevard Périphérique Nord de Lyon is a major ring-road section in Lyon that helps divert and streamline traffic around the northern part of the city.
E2149493 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: Boulevard Périphérique Nord de Lyon | Statement: [Lyon urban road network, includesRoad, Boulevard Périphérique Nord de Lyon]
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: Boulevard Périphérique Nord de Lyon
Triple: [Lyon urban road network, includesRoad, Boulevard Périphérique Nord de Lyon]
Generated description
Boulevard Périphérique Nord de Lyon is a major ring-road section in Lyon that helps divert and streamline traffic around the northern part of the city.

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_69f76e0938088190a8f199631e97dec3 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f79f751e9c81909b8d9b6a7d6604be completed May 3, 2026, 7:18 p.m.
NED1 Entity disambiguation (via context triple) batch_6a38685d42508190b1a6ddd1abbb4d38 completed June 21, 2026, 10:40 p.m.
NEDg Description generation batch_6a38697d0f888190b2bb83e2d68f6e23 completed June 21, 2026, 10:45 p.m.
NED2 Entity disambiguation (via description) batch_6a386a0f11248190b8a832d728e47513 completed June 21, 2026, 10:47 p.m.
Created at: May 3, 2026, 4:05 p.m.