Triple

T21968299
Position Surface form Disambiguated ID Type / Status
Subject Universidade do Estado do Rio Grande do Norte E542514 entity
Predicate shortName P43 FINISHED
Object UERN
UERN is a public state university in Rio Grande do Norte, Brazil, offering higher education and research across multiple campuses and disciplines.
 E1510948 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: UERN | Statement: [Universidade do Estado do Rio Grande do Norte, shortName, UERN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: UERN
Context triple: [Universidade do Estado do Rio Grande do Norte, shortName, UERN]
  • A. UER
    UER is the vehicle registration code used on license plates for the Vorpommern-Greifswald district in the German state of Mecklenburg-Vorpommern.
  • B. UER
    UER is the French abbreviation for the European Broadcasting Union, an alliance of public service media organizations across Europe and beyond.
  • C. ERU
    ERU is the standard unit used to quantify emissions reductions achieved through Joint Implementation projects under the Kyoto Protocol.
  • D. UNE
    UNE is a university commonly known by its acronym for Universidad del Este, a higher education institution in Puerto Rico.
  • E. URS
    URS was the FIFA country code used to represent the Soviet Union national football team in international competitions.
  • 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: UERN
Triple: [Universidade do Estado do Rio Grande do Norte, shortName, UERN]
Generated description
UERN is a public state university in Rio Grande do Norte, Brazil, offering higher education and research across multiple campuses and disciplines.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: UERN
Target entity description: UERN is a public state university in Rio Grande do Norte, Brazil, offering higher education and research across multiple campuses and disciplines.
  • A. UER
    UER is the vehicle registration code used on license plates for the Vorpommern-Greifswald district in the German state of Mecklenburg-Vorpommern.
  • B. UER
    UER is the French abbreviation for the European Broadcasting Union, an alliance of public service media organizations across Europe and beyond.
  • C. ERU
    ERU is the standard unit used to quantify emissions reductions achieved through Joint Implementation projects under the Kyoto Protocol.
  • D. UNE
    UNE is a university commonly known by its acronym for Universidad del Este, a higher education institution in Puerto Rico.
  • E. URS
    URS was the FIFA country code used to represent the Soviet Union national football team in international competitions.
  • 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_69e0c47fab1081908dc74a6545dbb051 completed April 16, 2026, 11:14 a.m.
NER Named-entity recognition batch_69f1245c5d148190af2a06190ba32feb completed April 28, 2026, 9:19 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0a6716e6f48190a758929b4cf94707 completed May 18, 2026, 1:10 a.m.
NEDg Description generation batch_6a0a67c29848819080860f13b4c9697b completed May 18, 2026, 1:13 a.m.
NED2 Entity disambiguation (via description) batch_6a0a686a0f208190977890119ffcff79 completed May 18, 2026, 1:16 a.m.
Created at: April 16, 2026, 8:02 p.m.