Triple

T11840645
Position Surface form Disambiguated ID Type / Status
Subject Gallesia E281641 entity
Predicate namedAfter P63 FINISHED
Object Gallesio
Gallesio was an Italian botanist and naturalist best known for his pioneering studies on fruit trees and plant classification in the early 19th century.
 E950520 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: Gallesio | Statement: [Gallesia, namedAfter, Gallesio]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Gallesio
Context triple: [Gallesia, namedAfter, Gallesio]
  • A. Wethau
    Wethau is a small river in central Germany that flows through Saxony-Anhalt and Thuringia before joining the Saale.
  • B. Golian
    Golian is a Slovak surname most notably associated with Ján Golian, a key military leader of the Slovak National Uprising during World War II.
  • C. Glarentza
    Glarentza was a medieval port city in the Peloponnese that served as a major commercial and administrative center during the Frankish rule of Greece.
  • D. Gallega
    Gallega was one of the ships in Christopher Columbus’s final transatlantic expedition, the fourth voyage undertaken to explore parts of Central and South America.
  • E. Galaosiyo
    Galaosiyo is a town in Uzbekistan that serves as a local urban center within the historic Bukhara Region.
  • 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: Gallesio
Triple: [Gallesia, namedAfter, Gallesio]
Generated description
Gallesio was an Italian botanist and naturalist best known for his pioneering studies on fruit trees and plant classification in the early 19th century.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Gallesio
Target entity description: Gallesio was an Italian botanist and naturalist best known for his pioneering studies on fruit trees and plant classification in the early 19th century.
  • A. Wethau
    Wethau is a small river in central Germany that flows through Saxony-Anhalt and Thuringia before joining the Saale.
  • B. Golian
    Golian is a Slovak surname most notably associated with Ján Golian, a key military leader of the Slovak National Uprising during World War II.
  • C. Glarentza
    Glarentza was a medieval port city in the Peloponnese that served as a major commercial and administrative center during the Frankish rule of Greece.
  • D. Gallega
    Gallega was one of the ships in Christopher Columbus’s final transatlantic expedition, the fourth voyage undertaken to explore parts of Central and South America.
  • E. Galaosiyo
    Galaosiyo is a town in Uzbekistan that serves as a local urban center within the historic Bukhara Region.
  • 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_69d6ab276f8c8190b1966a0ef11349ac completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d8a658f918819092c2db05fe2ab0ce completed April 10, 2026, 7:27 a.m.
NED1 Entity disambiguation (via context triple) batch_69f1678668ac81909bddf67e8c176757 completed April 29, 2026, 2:05 a.m.
NEDg Description generation batch_69f17004fb908190a486c6718c5252cb completed April 29, 2026, 2:42 a.m.
NED2 Entity disambiguation (via description) batch_69f1db11f0f48190832ca4f552f21751 completed April 29, 2026, 10:18 a.m.
Created at: April 8, 2026, 9:43 p.m.