Triple

T3608866
Position Surface form Disambiguated ID Type / Status
Subject University of Coimbra E76435 entity
Predicate hasCampus P116 FINISHED
Object Alta and Sofia
Alta and Sofia is a historic campus area of the University of Coimbra in Portugal, known for its centuries-old academic buildings and UNESCO World Heritage status.
 E374186 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: Alta and Sofia | Statement: [University of Coimbra, hasCampus, Alta and Sofia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Alta and Sofia
Context triple: [University of Coimbra, hasCampus, Alta and Sofia]
  • A. Alva
    Alva is a small city in northwestern Oklahoma known as the county seat of Woods County and home to Northwestern Oklahoma State University.
  • B. Alva
    Alva is a small town in central Scotland situated at the foot of the Ochil Hills in Clackmannanshire.
  • C. Alva
    Alva is the middle name of the famed American inventor Thomas Edison, often used as part of his full name, Thomas Alva Edison.
  • D. Silvana
    Silvana is a feminine given name used in various cultures, often associated with meanings related to forests or woods.
  • E. Amalia
    Amalia is the Dutch crown princess, heir apparent to the throne of the Kingdom of the Netherlands.
  • 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: Alta and Sofia
Triple: [University of Coimbra, hasCampus, Alta and Sofia]
Generated description
Alta and Sofia is a historic campus area of the University of Coimbra in Portugal, known for its centuries-old academic buildings and UNESCO World Heritage status.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Alta and Sofia
Target entity description: Alta and Sofia is a historic campus area of the University of Coimbra in Portugal, known for its centuries-old academic buildings and UNESCO World Heritage status.
  • A. Alva
    Alva is a small town in central Scotland situated at the foot of the Ochil Hills in Clackmannanshire.
  • B. Alva
    Alva is a small city in northwestern Oklahoma known as the county seat of Woods County and home to Northwestern Oklahoma State University.
  • C. Alva
    Alva is the middle name of the famed American inventor Thomas Edison, often used as part of his full name, Thomas Alva Edison.
  • D. Silvana
    Silvana is a feminine given name used in various cultures, often associated with meanings related to forests or woods.
  • E. Amalia
    Amalia is the Dutch crown princess, heir apparent to the throne of the Kingdom of the Netherlands.
  • 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_69b4330de7a08190933aa7e9dc0a65be completed March 13, 2026, 3:53 p.m.
NEDg Description generation batch_69b437cf839881909b1d505328285123 completed March 13, 2026, 4:14 p.m.
NED2 Entity disambiguation (via description) batch_69b43835994c81909230bbb21b12b8ef completed March 13, 2026, 4:15 p.m.
Created at: March 8, 2026, 3:22 p.m.