Triple

T11756761
Position Surface form Disambiguated ID Type / Status
Subject House of Sabran E279544 entity
Predicate associatedTerritory P1103 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: [House of Sabran, associatedTerritory, Provence]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Provence
Context triple: [House of Sabran, associatedTerritory, 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_69d6ab01038c819080714901502c84fc completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8a50c8d708190ad79ecb57b715d04 completed April 10, 2026, 7:21 a.m.
NED1 Entity disambiguation (via context triple) batch_69f48a1d244c81908628b01afe467023 completed May 1, 2026, 11:10 a.m.
Created at: April 8, 2026, 9:41 p.m.