Triple

T3608867
Position Surface form Disambiguated ID Type / Status
Subject University of Coimbra E76435 entity
Predicate hasCampus P116 FINISHED
Object Pólo II
Pólo II is the University of Coimbra’s science and technology campus, housing many of its engineering and scientific departments and research facilities.
 E374187 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: Pólo II | Statement: [University of Coimbra, hasCampus, Pólo II]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Pólo II
Context triple: [University of Coimbra, hasCampus, Pólo II]
  • A. Polaria
    Polaria is an Arctic-themed experience center and aquarium in Tromsø, Norway, focusing on polar research, climate, and marine life.
  • B. Lomonosovo
    Lomonosovo is a rural locality in Russia’s Arkhangelsk Oblast, best known as the birthplace of the polymath Mikhail Lomonosov.
  • C. Pevek
    Pevek is a small Arctic port town in Russia, known as one of the northernmost settlements in the country and a key hub in the Chukotka region.
  • D. Nespolo
    Nespolo is a small Italian municipality located in the Lazio region, known for its rural character and scenic Apennine surroundings.
  • E. Bór
    Bór is the wartime nickname of Tadeusz Bór-Komorowski, the Polish general who commanded the Home Army and led the Warsaw Uprising during World War II.
  • 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: Pólo II
Triple: [University of Coimbra, hasCampus, Pólo II]
Generated description
Pólo II is the University of Coimbra’s science and technology campus, housing many of its engineering and scientific departments and research facilities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Pólo II
Target entity description: Pólo II is the University of Coimbra’s science and technology campus, housing many of its engineering and scientific departments and research facilities.
  • A. Polaria
    Polaria is an Arctic-themed experience center and aquarium in Tromsø, Norway, focusing on polar research, climate, and marine life.
  • B. Lomonosovo
    Lomonosovo is a rural locality in Russia’s Arkhangelsk Oblast, best known as the birthplace of the polymath Mikhail Lomonosov.
  • C. Pevek
    Pevek is a small Arctic port town in Russia, known as one of the northernmost settlements in the country and a key hub in the Chukotka region.
  • D. Nespolo
    Nespolo is a small Italian municipality located in the Lazio region, known for its rural character and scenic Apennine surroundings.
  • E. Bór
    Bór is the wartime nickname of Tadeusz Bór-Komorowski, the Polish general who commanded the Home Army and led the Warsaw Uprising during World War II.
  • 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.