Triple

T6445053
Position Surface form Disambiguated ID Type / Status
Subject Universidad de Zaragoza E138320 entity
Predicate hasCityCampus P5417 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: [Universidad de Zaragoza, hasCityCampus, Teruel]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Teruel
Context triple: [Universidad de Zaragoza, hasCityCampus, 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. 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.
  • E. Baeza
    Baeza is a historic Andalusian town in southern Spain renowned for its well-preserved Renaissance architecture and status as a UNESCO World Heritage Site.
  • 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_69c008aa61ac8190bc96715ed79fe2d8 completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c0698d866c81909ef3e0a53833ff7d completed March 22, 2026, 10:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6857a8f188190ab3eaac5d3f87473 completed March 27, 2026, 1:26 p.m.
Created at: March 22, 2026, 4:46 p.m.