Triple

T16782127
Position Surface form Disambiguated ID Type / Status
Subject Matarranya E407881 entity
Predicate capital P234 FINISHED
Object Valderrobres E580830 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: Valderrobres | Statement: [Matarranya, capital, Valderrobres]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Valderrobres
Context triple: [Matarranya, capital, Valderrobres]
  • A. Valderrobres chosen
    Valderrobres is a historic town in eastern Spain known for its well-preserved medieval architecture, including a hilltop castle and Gothic bridge over the Matarraña River.
  • B. Vilalba
    Vilalba is a town in the province of Lugo in Galicia, northwestern Spain, known as the birthplace of several notable Galician political and cultural figures.
  • C. Cabrils
    Cabrils is a small municipality in the Maresme comarca of Catalonia, Spain, known for its residential character and proximity to the Mediterranean coast.
  • D. Brihuega
    Brihuega is a historic town in central Spain’s Castilla-La Mancha region, renowned for its medieval architecture and extensive lavender fields.
  • E. Valverde de la Vera
    Valverde de la Vera is a historic village in the province of Cáceres, Extremadura, Spain, known for its traditional architecture and scenic setting in the La Vera region.
  • 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_69d8839270588190886720d9519bbf8f completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3b217b2108190bbba262a3b324509 completed April 18, 2026, 4:32 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00b287a96c8190a16d7c76d05be106 completed May 10, 2026, 4:29 p.m.
Created at: April 10, 2026, 5:22 a.m.