Triple

T4680366
Position Surface form Disambiguated ID Type / Status
Subject Millau Viaduct E103783 entity
Predicate connects P390 FINISHED
Object Béziers E167514 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: Béziers | Statement: [Millau Viaduct, connects, Béziers]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Béziers
Context triple: [Millau Viaduct, connects, Béziers]
  • A. Béziers chosen
    Béziers is a historic city in southern France known for its wine production, ancient Roman heritage, and the famous Feria de Béziers festival.
  • B. Perpignan
    Perpignan is a historic city in southern France near the Spanish border, known for its Catalan culture and Mediterranean climate.
  • C. Montpellier
    Montpellier is a major city in southern France known for its medieval old town, vibrant university life, and proximity to the Mediterranean coast.
  • D. Toulouse
    Toulouse is a major city in southwestern France known for its aerospace industry, historic pink-brick architecture, and vibrant university and cultural life.
  • E. Albi
    Albi is a historic city in southern France renowned for its red-brick medieval architecture and the UNESCO-listed Episcopal City centered around Sainte-Cécile Cathedral.
  • 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_69bd43debbf08190b4bc372e286ec234 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd636c105081908655ab384f539f38 completed March 20, 2026, 3:10 p.m.
NED1 Entity disambiguation (via context triple) batch_69c1132b29ac819086654ae935e819ef completed March 23, 2026, 10:17 a.m.
Created at: March 20, 2026, 1:16 p.m.