Triple

T20670125
Position Surface form Disambiguated ID Type / Status
Subject JSNN E507998 entity
Predicate abbreviation P43 FINISHED
Object JSNN NE NERFINISHED

How this triple was built (2 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: JSNN | Statement: [JSNN, abbreviation, JSNN]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: JSNN
Context triple: [JSNN, abbreviation, JSNN]
  • A. JSNN chosen
    JSNN is a graduate-level academic and research institution focused on nanoscience and nanoengineering, jointly operated by North Carolina A&T State University and the University of North Carolina at Greensboro.
  • B. JSN
    JSN is a collaborative association of Jesuit secondary and pre-secondary schools in North America that supports their shared mission of Catholic, Jesuit education.
  • C. JSO
    JSO is the abbreviation for Japan’s Joint Staff Office, the central command organization of the Japan Self-Defense Forces responsible for joint military operations and planning.
  • D. JJN
    JJN is the IATA airport code for Quanzhou Jinjiang International Airport, a regional airport serving Quanzhou and the surrounding area in Fujian, China.
  • E. JSB
    JSB is the commonly used abbreviation for John Seely Brown, an American researcher and former chief scientist at Xerox PARC known for his work on organizational learning, innovation, and the social aspects of technology.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4c059bc81908ea762cd73ea4424 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6b5c735048190a01cb7692928d66e completed April 20, 2026, 11:24 p.m.
Created at: April 16, 2026, 11:44 a.m.