Triple

T14032094
Position Surface form Disambiguated ID Type / Status
Subject Mudéjar architecture E337614 entity
Predicate associatedWithRegion P285 FINISHED
Object Teruel E574615 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: Teruel | Statement: [Mudéjar architecture, associatedWithRegion, Teruel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Teruel
Context triple: [Mudéjar architecture, associatedWithRegion, Teruel]
  • A. Jaén
    Jaén is a province in southern Spain’s Andalusia region, renowned for its vast olive groves and historic Renaissance towns.
  • B. Jaén
    Jaén is a significant commercial and agricultural city in northern Peru, known as a regional hub within the Cajamarca Region.
  • C. Almería
    Almería is a coastal city and province in southeastern Spain known for its arid climate, historic Alcazaba fortress, and extensive greenhouse agriculture.
  • D. Zaragosa
    Zaragosa is a barangay (village-level administrative division) within the municipality of Badian in the province of Cebu, Philippines.
  • E. city of Teruel chosen
    The city of Teruel is the capital of Spain’s Teruel province, known for its Mudéjar architecture and historic medieval heritage.
  • 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_69d81c6543a48190bd5ba93d7419e797 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de2fab17008190981f1808726fa11c completed April 14, 2026, 12:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69fe9db6187c8190969b035bc2813413 completed May 9, 2026, 2:36 a.m.
Created at: April 9, 2026, 10:20 p.m.