Triple

T8457022
Position Surface form Disambiguated ID Type / Status
Subject London–Avignon E199943 entity
Predicate typicalDestinationRegion P6093 FINISHED
Object Provence E269383 NE FINISHED

How this triple was built (3 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: [London–Avignon, typicalDestinationRegion, Provence]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Provence
Context triple: [London–Avignon, typicalDestinationRegion, 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.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: typicalDestinationRegion
Context triple: [London–Avignon, typicalDestinationRegion, Provence]
  • A. typicalDestinationsRegion chosen
    Indicates that a region is a common or characteristic destination area associated with something (such as travelers, routes, or activities).
  • B. typicalHostRegion
    Indicates the geographic region where a host is most commonly or characteristically found or associated.
  • C. landingRegion
    Indicates the area or zone where an object or entity comes to rest or makes contact after moving or descending.
  • D. recipientRegion
    Indicates the geographic region that receives or is the destination of a transfer, delivery, or directed action.
  • E. geographicalRegionType
    Indicates the specific kind or category of geographical region that an entity belongs to (e.g., continent, country, province, or city).
  • F. None of above.

Provenance (4 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_69ca8318231881908fd1bc1c4d45d286 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe48f180c8190a71cf9d7248ade60 completed March 31, 2026, 3:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf27e1bda481909062b9259a168eb0 completed April 3, 2026, 2:37 a.m.
PD Predicate disambiguation batch_69cbd0fc634481909842c0a30077bfde completed March 31, 2026, 1:49 p.m.
Created at: March 30, 2026, 6:10 p.m.