Triple

T30894302
Position Surface form Disambiguated ID Type / Status
Subject Bethlehem University E786981 entity
Predicate hasUnit P35 FINISHED
Object Career Services Office
The Career Services Office at Bethlehem University is a campus unit that supports students and graduates with career planning, job search assistance, and connections to employment opportunities.
 E1935629 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: Career Services Office | Statement: [Bethlehem University, hasUnit, Career Services Office]
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: Career Services Office
Triple: [Bethlehem University, hasUnit, Career Services Office]
Generated description
The Career Services Office at Bethlehem University is a campus unit that supports students and graduates with career planning, job search assistance, and connections to employment opportunities.

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_69f224bbfa7c81908448e0c261c523e3 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f692391694819096143e91732b126b completed May 3, 2026, 12:09 a.m.
NED1 Entity disambiguation (via context triple) batch_6a28c7ebcfbc81909f2ba04b578d7a6c completed June 10, 2026, 2:11 a.m.
NEDg Description generation batch_6a28caa7fa2081908b1235b7dccb090c completed June 10, 2026, 2:23 a.m.
NED2 Entity disambiguation (via description) batch_6a28cb1dbc2c8190ae22fff781e418d4 completed June 10, 2026, 2:25 a.m.
Created at: April 29, 2026, 8:49 p.m.