Triple

T3608762
Position Surface form Disambiguated ID Type / Status
Subject Centro Region E76433 entity
Predicate containsCity P294 FINISHED
Object Alcanena
Alcanena is a Portuguese municipality known for its traditional leather and tanning industry, located in the Centro Region of Portugal.
 E378288 NE FINISHED

How this triple was built (4 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: Alcanena | Statement: [Centro Region, containsCity, Alcanena]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Alcanena
Context triple: [Centro Region, containsCity, Alcanena]
  • A. Brihuega
    Brihuega is a historic town in central Spain’s Castilla-La Mancha region, renowned for its medieval architecture and extensive lavender fields.
  • B. Higuillar
    Higuillar is a coastal barrio (district) of the municipality of Dorado in Puerto Rico, known for its beaches and residential communities.
  • C. Caleruega
    Caleruega is a small town in the province of Burgos, Spain, best known as the birthplace of Saint Dominic, founder of the Dominican Order.
  • D. Fuentealbilla
    Fuentealbilla is a small municipality in the province of Albacete, Spain, best known as the hometown of footballer Andrés Iniesta.
  • E. Nalón
    The Nalón is a major river in Asturias, northern Spain, known for flowing through mountainous landscapes and historically supporting regional industry and mining.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Alcanena
Triple: [Centro Region, containsCity, Alcanena]
Generated description
Alcanena is a Portuguese municipality known for its traditional leather and tanning industry, located in the Centro Region of Portugal.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Alcanena
Target entity description: Alcanena is a Portuguese municipality known for its traditional leather and tanning industry, located in the Centro Region of Portugal.
  • A. Brihuega
    Brihuega is a historic town in central Spain’s Castilla-La Mancha region, renowned for its medieval architecture and extensive lavender fields.
  • B. Higuillar
    Higuillar is a coastal barrio (district) of the municipality of Dorado in Puerto Rico, known for its beaches and residential communities.
  • C. Caleruega
    Caleruega is a small town in the province of Burgos, Spain, best known as the birthplace of Saint Dominic, founder of the Dominican Order.
  • D. Fuentealbilla
    Fuentealbilla is a small municipality in the province of Albacete, Spain, best known as the hometown of footballer Andrés Iniesta.
  • E. Nalón
    The Nalón is a major river in Asturias, northern Spain, known for flowing through mountainous landscapes and historically supporting regional industry and mining.
  • F. None of above. chosen

Provenance (5 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_69ad85da0ba481908b3b48c69efe2b98 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc22a3cf081908c20b6fb55be0db2 completed March 8, 2026, 6:38 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4882a556881909e6c20cce617e4b6 completed March 13, 2026, 9:56 p.m.
NEDg Description generation batch_69b48bd150088190a0ac0e2f9dafae85 completed March 13, 2026, 10:12 p.m.
NED2 Entity disambiguation (via description) batch_69b4b72f71bc8190a5cba6741db1e105 completed March 14, 2026, 1:17 a.m.
Created at: March 8, 2026, 3:22 p.m.