Triple

T12785445
Position Surface form Disambiguated ID Type / Status
Subject Shiley-Marcos School of Engineering E305612 entity
Predicate acronym P43 FINISHED
Object SMSE
SMSE is the Shiley-Marcos School of Engineering, an academic division focused on engineering education and research at the University of San Diego.
 E1003489 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: SMSE | Statement: [Shiley-Marcos School of Engineering, acronym, SMSE]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SMSE
Context triple: [Shiley-Marcos School of Engineering, acronym, SMSE]
  • A. MES
    MES is the former IATA airport code that was used for Polonia International Airport in Medan, Indonesia.
  • B. MMS
    MMS (Multimedia Messaging Service) is a mobile messaging standard that allows users to send multimedia content such as images, audio, and video between mobile devices.
  • C. SMDS
    SMDS is the abbreviated name for the U.S. Army Space and Missile Defense School, a training institution focused on space and missile defense operations.
  • D. MSG
    MSG is a famous multi-purpose indoor arena in New York City known for hosting major sports events, concerts, and entertainment spectacles.
  • E. MSG
    MSG is a subregional political and economic organization that promotes cooperation and solidarity among Melanesian countries and territories in the Pacific.
  • 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: SMSE
Triple: [Shiley-Marcos School of Engineering, acronym, SMSE]
Generated description
SMSE is the Shiley-Marcos School of Engineering, an academic division focused on engineering education and research at the University of San Diego.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SMSE
Target entity description: SMSE is the Shiley-Marcos School of Engineering, an academic division focused on engineering education and research at the University of San Diego.
  • A. MES
    MES is the former IATA airport code that was used for Polonia International Airport in Medan, Indonesia.
  • B. MMS
    MMS (Multimedia Messaging Service) is a mobile messaging standard that allows users to send multimedia content such as images, audio, and video between mobile devices.
  • C. SMDS
    SMDS is the abbreviated name for the U.S. Army Space and Missile Defense School, a training institution focused on space and missile defense operations.
  • D. MSG
    MSG is a famous multi-purpose indoor arena in New York City known for hosting major sports events, concerts, and entertainment spectacles.
  • E. MSG
    MSG is a subregional political and economic organization that promotes cooperation and solidarity among Melanesian countries and territories in the Pacific.
  • 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_69d7bdf2b43c819098ae5aa68e61ea58 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d96e5cb3c08190b8e1e22de8b96e17 completed April 10, 2026, 9:40 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6850703948190984acf9e434cd2a7 completed May 2, 2026, 11:13 p.m.
NEDg Description generation batch_69f685dc63f48190bb68f9859e99e3b4 completed May 2, 2026, 11:16 p.m.
NED2 Entity disambiguation (via description) batch_69f68a92226881909600555332c2c370 completed May 2, 2026, 11:36 p.m.
Created at: April 9, 2026, 5:29 p.m.