Triple

T29661029
Position Surface form Disambiguated ID Type / Status
Subject Angelo State University E750403 entity
Predicate hasAcademicDivision P50 FINISHED
Object College of Education
The College of Education is an academic division of Angelo State University that prepares students for careers in teaching, educational leadership, and related professional roles in education.
E1881534 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: [Angelo State University, hasAcademicDivision, 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: [Angelo State University, hasAcademicDivision, College of Education]
Generated description
The College of Education is an academic division of Angelo State University that prepares students for careers in teaching, educational leadership, and related professional roles in education.

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_69f0d6226fe881908819197c9ef9ee04 completed April 28, 2026, 3:45 p.m.
NER Named-entity recognition batch_69f671c062c08190bfe6b51c4edc80a5 completed May 2, 2026, 9:50 p.m.
NED1 Entity disambiguation (via context triple) batch_6a26aa67d5808190b32844ae6e796182 completed June 8, 2026, 11:41 a.m.
NEDg Description generation batch_6a26b01a27148190aa0135f779819255 completed June 8, 2026, 12:05 p.m.
NED2 Entity disambiguation (via description) batch_6a26b4faf2c881909f77e6c4a8dc665b completed June 8, 2026, 12:26 p.m.
Created at: April 28, 2026, 6:58 p.m.