Triple

T27161950
Position Surface form Disambiguated ID Type / Status
Subject NUAA E682682 entity
Predicate hasFaculty P141 FINISHED
Object College of Automation Engineering
The College of Automation Engineering is an academic unit of Nanjing University of Aeronautics and Astronautics specializing in automation, control engineering, and related technological disciplines.
E1764133 NE FINISHED

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: College of Automation Engineering | Statement: [NUAA, hasFaculty, College of Automation Engineering]
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: College of Automation Engineering
Triple: [NUAA, hasFaculty, College of Automation Engineering]
Generated description
The College of Automation Engineering is an academic unit of Nanjing University of Aeronautics and Astronautics specializing in automation, control engineering, and related technological disciplines.

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_69eefacf6e788190a75a64399d9e3109 completed April 27, 2026, 5:57 a.m.
NER Named-entity recognition batch_69f6250b1b248190950a94cd6fe1d479 completed May 2, 2026, 4:23 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12626025048190a3b4bd70d2a9fd9a completed May 24, 2026, 2:28 a.m.
NEDg Description generation batch_6a126b4b40248190a88fc737f79eb819 completed May 24, 2026, 3:06 a.m.
NED2 Entity disambiguation (via description) batch_6a126bbf63c48190b7d8588f7e9d1c9c completed May 24, 2026, 3:08 a.m.
Created at: April 27, 2026, 9:19 a.m.