Triple

T15308499
Position Surface form Disambiguated ID Type / Status
Subject Musée Hyacinthe Rigaud E365966 entity
Predicate serves P98 FINISHED
Object Perpignan E164948 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: Perpignan | Statement: [Musée Hyacinthe Rigaud, serves, Perpignan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Perpignan
Context triple: [Musée Hyacinthe Rigaud, serves, Perpignan]
  • A. Perpignan chosen
    Perpignan is a historic city in southern France near the Spanish border, known for its Catalan culture and Mediterranean climate.
  • B. Béziers
    Béziers is a historic city in southern France known for its wine production, ancient Roman heritage, and the famous Feria de Béziers festival.
  • 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. Montpellier
    Montpellier is an affluent district of Cheltenham, England, known for its Regency architecture, boutique shops, and café culture.
  • E. Marseillan
    Marseillan is a coastal commune in southern France known for its historic port, oyster farming, and proximity to the Étang de Thau lagoon.
  • 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_69d85a113ee881908e297a1d38dd79fa completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e03cd001b48190bbdd69337efdb907 completed April 16, 2026, 1:35 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff8757325c8190ad97f50368862ca5 completed May 9, 2026, 7:13 p.m.
Created at: April 10, 2026, 3:16 a.m.