Triple

T16361460
Position Surface form Disambiguated ID Type / Status
Subject Penedono E397320 entity
Predicate borderedBy P224 FINISHED
Object Meda
Meda is a municipality in Portugal’s Guarda District, known for its historic villages, wine production, and location in the Douro region.
 E1208110 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: Meda | Statement: [Penedono, borderedBy, Meda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Meda
Context triple: [Penedono, borderedBy, Meda]
  • A. Meda
    Meda is a town in the Brianza area of Lombardy, northern Italy, known for its furniture industry and proximity to other small industrial centers.
  • B. Mora
    Mora is a canton in Costa Rica’s San José Province known for its rural landscapes, agricultural activities, and small-town communities.
  • C. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • D. Mora
    Mora is a town in central Sweden’s Dalarna region, known for its traditional Swedish culture, proximity to Lake Siljan, and as the finish line of the Vasaloppet cross-country ski race.
  • E. Mora
    Mora is a municipality in Portugal known for its rural Alentejo landscapes, traditional villages, and proximity to the Montargil reservoir.
  • 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: Meda
Triple: [Penedono, borderedBy, Meda]
Generated description
Meda is a municipality in Portugal’s Guarda District, known for its historic villages, wine production, and location in the Douro region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Meda
Target entity description: Meda is a municipality in Portugal’s Guarda District, known for its historic villages, wine production, and location in the Douro region.
  • A. Meda
    Meda is a town in the Brianza area of Lombardy, northern Italy, known for its furniture industry and proximity to other small industrial centers.
  • B. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • C. Mora
    Mora is a canton in Costa Rica’s San José Province known for its rural landscapes, agricultural activities, and small-town communities.
  • D. Mora
    Mora is a municipality in Portugal known for its rural Alentejo landscapes, traditional villages, and proximity to the Montargil reservoir.
  • E. Mora
    Mora is a town in central Sweden’s Dalarna region, known for its traditional Swedish culture, proximity to Lake Siljan, and as the finish line of the Vasaloppet cross-country ski race.
  • 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_69d87f2778dc8190aa95c7572db127e6 completed April 10, 2026, 4:40 a.m.
NER Named-entity recognition batch_69e2fad304448190b3f6f0350a1e151d completed April 18, 2026, 3:30 a.m.
NED1 Entity disambiguation (via context triple) batch_6a002dbeabe081909e3d02676293e8b2 completed May 10, 2026, 7:03 a.m.
NEDg Description generation batch_6a002ed2ddf0819083be2dd80810ac78 completed May 10, 2026, 7:08 a.m.
NED2 Entity disambiguation (via description) batch_6a002f8fdefc8190bf893c7dc22c9f0b completed May 10, 2026, 7:11 a.m.
Created at: April 10, 2026, 5:08 a.m.