Triple

T26527663
Position Surface form Disambiguated ID Type / Status
Subject Courtenay, Loiret E670735 entity
Predicate hasRoadConnection P385 FINISHED
Object A19 motorway
The A19 motorway is a French autoroute in north-central France that serves as an important east–west bypass of Paris, linking several major motorways and facilitating regional and long-distance traffic.
 E2293103 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: A19 motorway | Statement: [Courtenay, Loiret, hasRoadConnection, A19 motorway]
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: A19 motorway
Triple: [Courtenay, Loiret, hasRoadConnection, A19 motorway]
Generated description
The A19 motorway is a French autoroute in north-central France that serves as an important east–west bypass of Paris, linking several major motorways and facilitating regional and long-distance traffic.

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_69eeb31ea1e08190b9ff43cf9bc25bf8 completed April 27, 2026, 12:51 a.m.
NER Named-entity recognition batch_69f613f41ccc8190b8bcc027ba1d0b93 completed May 2, 2026, 3:10 p.m.
NED1 Entity disambiguation (via context triple) batch_6a7a689fe35081909c5c4500ef6deae9 completed Aug. 11, 2026, 12:11 a.m.
NEDg Description generation batch_6a7a693ff4688190a01ccb04e8d94185 completed Aug. 11, 2026, 12:13 a.m.
NED2 Entity disambiguation (via description) batch_6a7a696be3cc8190acd956c86c431a88 completed Aug. 11, 2026, 12:14 a.m.
Created at: April 27, 2026, 1:33 a.m.