Triple

T12928878
Position Surface form Disambiguated ID Type / Status
Subject A29 autoroute E309317 entity
Predicate connectsTo P845 FINISHED
Object A1 autoroute E48845 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: A1 autoroute | Statement: [A29 autoroute, connectsTo, A1 autoroute]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: A1 autoroute
Context triple: [A29 autoroute, connectsTo, A1 autoroute]
  • A. A1 autoroute chosen
    The A1 autoroute is a major French motorway connecting Paris to Lille and serving key locations such as Charles de Gaulle Airport and the northern suburbs.
  • B. A10 autoroute
    The A10 autoroute is a major French motorway that connects Paris to the southwest of France, including cities such as Orléans, Tours, and Bordeaux.
  • C. A13 autoroute
    The A13 autoroute is a major French motorway linking Paris to Normandy, serving as a key route toward cities such as Rouen and Caen.
  • D. A12 autoroute
    The A12 autoroute is a short French motorway in the Île-de-France region that connects the A13 to the N10, helping link Paris to the southwestern suburbs and beyond.
  • E. A103 autoroute
    The A103 autoroute is a short French motorway in the eastern suburbs of Paris that links local traffic to the larger national autoroute network.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d7bdfa933c8190b5a27aa4a08a19b7 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d971ec72a48190aceef10630603d2c completed April 10, 2026, 9:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7460999c081908c8d84caf6c04985 completed May 3, 2026, 12:56 p.m.
Created at: April 9, 2026, 5:42 p.m.