Triple

T6749594
Position Surface form Disambiguated ID Type / Status
Subject Avignon – Provence Airport E154308 entity
Predicate serves P98 FINISHED
Object Provence E269383 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: Provence | Statement: [Avignon – Provence Airport, serves, Provence]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Provence
Context triple: [Avignon – Provence Airport, serves, Provence]
  • A. Provence chosen
    Provence is a historic region in southeastern France known for its picturesque lavender fields, Mediterranean coastline, and rich cultural and culinary traditions.
  • B. Languedoc
    Languedoc is a historic region in southern France known for its Occitan culture, medieval towns, and long-standing wine-making tradition.
  • C. Provence-Alpes-Côte d’Azur
    Provence-Alpes-Côte d’Azur is a region in southeastern France known for its Mediterranean coastline, picturesque villages, and cultural hubs such as Marseille and Nice.
  • D. Southern France
    Southern France is a culturally rich and geographically diverse region known for its Mediterranean coastline, historic cities, and renowned cuisine and wine.
  • E. Occitanie
    Occitanie is a large administrative region in southern France known for its Mediterranean coastline, historic cities like Toulouse and Montpellier, and diverse landscapes ranging from coastal plains to the Pyrenees.
  • 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_69c6880ef37881909268a5a7299b9293 completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d1d8bfa48190a7fc48102258ae17 completed March 27, 2026, 6:52 p.m.
NED1 Entity disambiguation (via context triple) batch_69c712a793cc8190b838806151851711 completed March 27, 2026, 11:28 p.m.
Created at: March 27, 2026, 2:11 p.m.