Triple

T8836177
Position Surface form Disambiguated ID Type / Status
Subject Human towers (castells) E210270 entity
Predicate hasNotableCity P2813 FINISHED
Object Vilafranca del Penedès E305686 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: Vilafranca del Penedès | Statement: [Human towers (castells), hasNotableCity, Vilafranca del Penedès]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Vilafranca del Penedès
Context triple: [Human towers (castells), hasNotableCity, Vilafranca del Penedès]
  • A. Vilafranca del Penedès chosen
    Vilafranca del Penedès is a historic town in Catalonia, Spain, known as a traditional wine-producing center in the Penedès region.
  • B. Banyoles
    Banyoles is a town in Catalonia, Spain, best known for its large natural lake and scenic surroundings.
  • C. 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.
  • D. Palamós
    Palamós is a coastal town and popular tourist destination on Spain’s Costa Brava, known for its fishing port, beaches, and seafood cuisine.
  • E. Vilanova i la Geltrú
    Vilanova i la Geltrú is a coastal city in Catalonia, Spain, known for its Mediterranean beaches, cultural festivals, and role as a regional educational and industrial hub.
  • 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_69ca8388549c819095fd94eadefbb007 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc6069ad7881909e31010e73e26f91 completed April 1, 2026, 12:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69cf898022c88190b7274350ce065f00 completed April 3, 2026, 9:33 a.m.
Created at: March 30, 2026, 6:47 p.m.