Triple

T11607861
Position Surface form Disambiguated ID Type / Status
Subject Mariano Comense E275307 entity
Predicate locatedNear P294 FINISHED
Object 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.
 E936471 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: [Mariano Comense, locatedNear, Meda]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Meda
Context triple: [Mariano Comense, locatedNear, Meda]
  • A. Mora
    Mora is a canton in Costa Rica’s San José Province known for its rural landscapes, agricultural activities, and small-town communities.
  • B. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • C. 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.
  • D. Mora
    Mora is a municipality in Portugal known for its rural Alentejo landscapes, traditional villages, and proximity to the Montargil reservoir.
  • E. Mette
    Mette is a given name most notably associated with American dancer and actress Mette Towley, known for her work in music videos and film.
  • 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: [Mariano Comense, locatedNear, Meda]
Generated description
Meda is a town in the Brianza area of Lombardy, northern Italy, known for its furniture industry and proximity to other small industrial centers.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Meda
Target entity description: Meda is a town in the Brianza area of Lombardy, northern Italy, known for its furniture industry and proximity to other small industrial centers.
  • A. Mora
    Mora is a surname of Hungarian origin most notably borne by the German-Hungarian writer Terézia Mora.
  • 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 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.
  • D. Mora
    Mora is a municipality in Portugal known for its rural Alentejo landscapes, traditional villages, and proximity to the Montargil reservoir.
  • E. Mette
    Mette is a given name most notably associated with American dancer and actress Mette Towley, known for her work in music videos and film.
  • 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_69d6aaf84b548190ac072e4fb89ae18f completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d89551649c81908096ff392677442d completed April 10, 2026, 6:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69e8a82381708190aa0e674603d5778a completed April 22, 2026, 10:51 a.m.
NEDg Description generation batch_69e8af9665648190b7732076aa129671 completed April 22, 2026, 11:23 a.m.
NED2 Entity disambiguation (via description) batch_69ee5b3a3720819095a4a87176e052cb completed April 26, 2026, 6:36 p.m.
Created at: April 8, 2026, 9:38 p.m.