Triple

T10362156
Position Surface form Disambiguated ID Type / Status
Subject Catalan municipalities E244162 entity
Predicate includeExample P1259 FINISHED
Object Reus E388590 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: Reus | Statement: [Catalan municipalities, includeExample, Reus]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Reus
Context triple: [Catalan municipalities, includeExample, Reus]
  • A. Reus chosen
    Reus is a city in Catalonia, Spain, known as the birthplace of architect Antoni Gaudí and for its historic center and vermouth production.
  • B. Benicàssim
    Benicàssim is a coastal town in eastern Spain best known for its Mediterranean beaches and the annual Festival Internacional de Benicàssim (FIB) music festival.
  • C. Lleida
    Lleida is a historic city in western Catalonia, Spain, known for its medieval Seu Vella cathedral and role as a regional agricultural and commercial center.
  • D. Esplugues de Llobregat
    Esplugues de Llobregat is a municipality in the metropolitan area of Barcelona, Catalonia, known for its residential character and proximity to the Catalan capital.
  • E. Berga
    Berga is a historic town in Catalonia, Spain, known for its mountainous surroundings and the traditional Patum de Berga festival.
  • 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_69d381b22b8c8190aaed476be5f872a9 completed April 6, 2026, 9:49 a.m.
NER Named-entity recognition batch_69d4e962f08c8190a7ac489dc524510d completed April 7, 2026, 11:24 a.m.
NED1 Entity disambiguation (via context triple) batch_69d979ecfef48190a6014601bcddf761 completed April 10, 2026, 10:30 p.m.
Created at: April 6, 2026, 11:59 a.m.