Triple

T26913540
Position Surface form Disambiguated ID Type / Status
Subject Chinese Culture University E677455 entity
Predicate hasFaculty P141 FINISHED
Object College of Education
The College of Education is an academic division of Chinese Culture University dedicated to training teachers and conducting research in educational theory and practice.
 E1746863 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 Education | Statement: [Chinese Culture University, hasFaculty, College of Education]
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 Education
Triple: [Chinese Culture University, hasFaculty, College of Education]
Generated description
The College of Education is an academic division of Chinese Culture University dedicated to training teachers and conducting research in educational theory and practice.

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_69eee9bcef1c8190be88586bb902bb9b completed April 27, 2026, 4:44 a.m.
NER Named-entity recognition batch_69f61fdc239c81908b7bebeb2d8a8a20 completed May 2, 2026, 4:01 p.m.
NED1 Entity disambiguation (via context triple) batch_6a121eb338ec8190b4fc02f7714ffee7 completed May 23, 2026, 9:40 p.m.
NEDg Description generation batch_6a121f90eec08190bd18be556349e464 completed May 23, 2026, 9:43 p.m.
NED2 Entity disambiguation (via description) batch_6a12205e89f4819098a8901d520e9c7d completed May 23, 2026, 9:47 p.m.
Created at: April 27, 2026, 6:03 a.m.