Triple

T6779864
Position Surface form Disambiguated ID Type / Status
Subject University of Cádiz E155651 entity
Predicate abbreviation P43 FINISHED
Object UCA
UCA is a Spanish public university based in Cádiz, known for its programs in marine sciences, engineering, and humanities.
 E617637 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: UCA | Statement: [University of Cádiz, abbreviation, UCA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: UCA
Context triple: [University of Cádiz, abbreviation, UCA]
  • A. UCA
    UCA is the Unicode Collation Algorithm, a Unicode standard that defines a language-independent method for ordering and comparing Unicode text.
  • B. UCA
    UCA is a Jesuit-run Central American University in Managua, Nicaragua, known for its strong emphasis on social justice, human rights, and critical scholarship.
  • C. UCA
    UCA is a French public university located in Clermont-Ferrand, known for its research and education across disciplines such as science, health, law, and humanities.
  • D. UAK
    UAK was the temporary currency code used for the Ukrainian karbovanets, a transitional currency of Ukraine in the early 1990s before the introduction of the hryvnia.
  • E. UNA
    UNA is the stock ticker symbol for Unilever, a major multinational consumer goods company known for its wide range of food, personal care, and household products.
  • 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: UCA
Triple: [University of Cádiz, abbreviation, UCA]
Generated description
UCA is a Spanish public university based in Cádiz, known for its programs in marine sciences, engineering, and humanities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: UCA
Target entity description: UCA is a Spanish public university based in Cádiz, known for its programs in marine sciences, engineering, and humanities.
  • A. UCA
    UCA is the Unicode Collation Algorithm, a Unicode standard that defines a language-independent method for ordering and comparing Unicode text.
  • B. UCA
    UCA is a Jesuit-run Central American University in Managua, Nicaragua, known for its strong emphasis on social justice, human rights, and critical scholarship.
  • C. UCA
    UCA is a French public university located in Clermont-Ferrand, known for its research and education across disciplines such as science, health, law, and humanities.
  • D. UAK
    UAK was the temporary currency code used for the Ukrainian karbovanets, a transitional currency of Ukraine in the early 1990s before the introduction of the hryvnia.
  • E. UNA
    UNA is the stock ticker symbol for Unilever, a major multinational consumer goods company known for its wide range of food, personal care, and household products.
  • 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_69c688162bf8819088b664b5c3b5be7a completed March 27, 2026, 1:37 p.m.
NER Named-entity recognition batch_69c6d26a1634819099b8a3b3196a306a completed March 27, 2026, 6:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69c712cf86e08190bd07c98f01ac5e19 completed March 27, 2026, 11:29 p.m.
NEDg Description generation batch_69c713c89a748190bd7bf280b82317f0 completed March 27, 2026, 11:33 p.m.
NED2 Entity disambiguation (via description) batch_69c714697330819082be2c9a871703f5 completed March 27, 2026, 11:36 p.m.
Created at: March 27, 2026, 2:14 p.m.