Triple

T27110342
Position Surface form Disambiguated ID Type / Status
Subject A36 motorway E686692 entity
Predicate junctionWith P1018 FINISHED
Object A6 motorway
The A6 motorway is a major French autoroute that connects Paris to Lyon, forming a key segment of the primary north–south route through the country.
 E233238 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: A6 motorway | Statement: [A36 motorway, junctionWith, A6 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: A6 motorway
Triple: [A36 motorway, junctionWith, A6 motorway]
Generated description
The A6 motorway is a major French autoroute that connects Paris to Lyon, forming a key segment of the primary north–south route through the country.

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_69ef148accd48190b6ed6e13a15f2a4f completed April 27, 2026, 7:47 a.m.
NER Named-entity recognition batch_69f6240111c08190a863d9786d36af1d completed May 2, 2026, 4:19 p.m.
NED1 Entity disambiguation (via context triple) batch_6a7a6a61e0a4819099a1ceea987b67f5 completed Aug. 11, 2026, 12:18 a.m.
NEDg Description generation batch_6a7a6adfeb488190ae28278a47b67afb completed Aug. 11, 2026, 12:20 a.m.
NED2 Entity disambiguation (via description) batch_6a7a6b249800819093df0c10cf276740 completed Aug. 11, 2026, 12:21 a.m.
Created at: April 27, 2026, 8:53 a.m.