Triple

T1781050
Position Surface form Disambiguated ID Type / Status
Subject University of Maine at Fort Kent E39291 entity
Predicate abbreviation P43 FINISHED
Object UMFK
UMFK is a small public university in Fort Kent, Maine, known for its rural campus, professional programs, and service to the St. John Valley region.
 E199913 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: UMFK | Statement: [University of Maine at Fort Kent, abbreviation, UMFK]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: UMFK
Context triple: [University of Maine at Fort Kent, abbreviation, UMFK]
  • A. USM
    USM (User-based Security Model) is the SNMPv3 security framework that provides user-level authentication, privacy (encryption), and access control for Simple Network Management Protocol communications.
  • B. UM
    UM is the commonly used abbreviation for the University of Miami, a private research university located in Coral Gables, Florida.
  • C. UM
    UM is the regional vehicle registration code used for the district of Uckermark in the German state of Brandenburg.
  • D. UM
    UM is a public research university in Winnipeg, Canada, known as the University of Manitoba.
  • E. UME
    UME is Spain’s specialized military emergency unit responsible for rapid response to natural disasters, major accidents, and other civil emergencies.
  • 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: UMFK
Triple: [University of Maine at Fort Kent, abbreviation, UMFK]
Generated description
UMFK is a small public university in Fort Kent, Maine, known for its rural campus, professional programs, and service to the St. John Valley region.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: UMFK
Target entity description: UMFK is a small public university in Fort Kent, Maine, known for its rural campus, professional programs, and service to the St. John Valley region.
  • A. USM
    USM (User-based Security Model) is the SNMPv3 security framework that provides user-level authentication, privacy (encryption), and access control for Simple Network Management Protocol communications.
  • B. UM
    UM is the commonly used abbreviation for the University of Miami, a private research university located in Coral Gables, Florida.
  • C. UM
    UM is the regional vehicle registration code used for the district of Uckermark in the German state of Brandenburg.
  • D. UM
    UM is a public research university in Winnipeg, Canada, known as the University of Manitoba.
  • E. UME
    UME is Spain’s specialized military emergency unit responsible for rapid response to natural disasters, major accidents, and other civil emergencies.
  • 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_69a88630519c8190a17addd83c4a3ef4 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa64e22d6881909ba6ec120b320918 completed March 6, 2026, 5:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69ada99f52a08190854109d152c22be0 completed March 8, 2026, 4:53 p.m.
NEDg Description generation batch_69adab04b5688190afb3418e9b9da845 completed March 8, 2026, 4:59 p.m.
NED2 Entity disambiguation (via description) batch_69adaeaf81e881908f99f5d948e3557b completed March 8, 2026, 5:15 p.m.
Created at: March 4, 2026, 7:31 p.m.