Triple

T5813016
Position Surface form Disambiguated ID Type / Status
Subject Maashees E128915 entity
Predicate roadAccessVia P9041 FINISHED
Object A73 motorway E574999 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: A73 motorway | Statement: [Maashees, roadAccessVia, A73 motorway]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: A73 motorway
Context triple: [Maashees, roadAccessVia, A73 motorway]
  • A. A73 motorway chosen
    The A73 motorway is a major Dutch highway in the southeastern Netherlands that connects several cities and regions, including the area around Heumen.
  • B. A73 motorway
    The A73 motorway is a major German autobahn that runs through Bavaria and Thuringia, linking cities such as Nuremberg, Erlangen, Bamberg, and Suhl.
  • C. A75 motorway
    The A75 motorway is a major French highway running through central and southern France, known for connecting Clermont-Ferrand to Béziers and featuring the iconic Millau Viaduct.
  • D. A7 motorway
    The A7 motorway is a major French highway that runs through the Rhône Valley, linking Lyon to Marseille and serving as a key north–south route in southeastern France.
  • E. A7 motorway
    The A7 motorway is one of Germany's major north–south autobahns, running the length of the country and passing near cities such as Göttingen.
  • 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_69c0084788848190bcf71f6bc5d71597 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c02b56044c8190847478a342d441c2 completed March 22, 2026, 5:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69ca621ce1b881908ed8145e0088ea3f completed March 30, 2026, 11:44 a.m.
Created at: March 22, 2026, 3:52 p.m.