Triple

T799604
Position Surface form Disambiguated ID Type / Status
Subject Macedonia (Greece) E17099 entity
Predicate containsCity P294 FINISHED
Object Serres
Serres is a historic city in northern Greece known for its Byzantine heritage and role as a regional economic and cultural center.
 E104307 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: Serres | Statement: [Macedonia (Greece), containsCity, Serres]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Serres
Context triple: [Macedonia (Greece), containsCity, Serres]
  • A. San Fernando
    San Fernando is a major industrial and commercial city located in the southern part of Trinidad, known for its energy sector and bustling urban center.
  • B. San Fernando
    San Fernando is a principal urban center and agricultural hub in central Chile’s O’Higgins Region.
  • C. San Borja
    San Borja is a primarily residential and commercial district in Lima, Peru, known for its middle- to upper-class neighborhoods, green areas, and cultural institutions.
  • D. Rivas
    Rivas is a city in southwestern Nicaragua known as a regional commercial center and gateway between Lake Nicaragua and the Pacific coast.
  • E. Ciudad Serdán
    Ciudad Serdán is a town in the Mexican state of Puebla, known as a gateway community to the nearby Pico de Orizaba volcano and surrounding highland 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: Serres
Triple: [Macedonia (Greece), containsCity, Serres]
Generated description
Serres is a historic city in northern Greece known for its Byzantine heritage and role as a regional economic and cultural center.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Serres
Target entity description: Serres is a historic city in northern Greece known for its Byzantine heritage and role as a regional economic and cultural center.
  • A. San Fernando
    San Fernando is a major industrial and commercial city located in the southern part of Trinidad, known for its energy sector and bustling urban center.
  • B. San Fernando
    San Fernando is a principal urban center and agricultural hub in central Chile’s O’Higgins Region.
  • C. San Borja
    San Borja is a primarily residential and commercial district in Lima, Peru, known for its middle- to upper-class neighborhoods, green areas, and cultural institutions.
  • D. Rivas
    Rivas is a city in southwestern Nicaragua known as a regional commercial center and gateway between Lake Nicaragua and the Pacific coast.
  • E. Ciudad Serdán
    Ciudad Serdán is a town in the Mexican state of Puebla, known as a gateway community to the nearby Pico de Orizaba volcano and surrounding highland 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_69a49378b9c48190adbf5f62e5b7aca1 completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a7cb26dc8190bdd3a278b8695873 completed March 1, 2026, 8:55 p.m.
NED1 Entity disambiguation (via context triple) batch_69a7b83f0fb4819097f29c9ab90cf1a8 completed March 4, 2026, 4:42 a.m.
NEDg Description generation batch_69a7bc1acf708190aa86cd5eca101966 completed March 4, 2026, 4:59 a.m.
NED2 Entity disambiguation (via description) batch_69a7bc96dd2881909310147292b99023 completed March 4, 2026, 5:01 a.m.
Created at: March 1, 2026, 7:38 p.m.