Triple

T32297638
Position Surface form Disambiguated ID Type / Status
Subject Gould School of Law E825145 entity
Predicate offersProgram P178 FINISHED
Object LLM in Business Law
LLM in Business Law is a specialized Master of Laws program focused on advanced study of corporate, commercial, and financial legal issues for law graduates and practitioners.
 E2001356 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: LLM in Business Law | Statement: [Gould School of Law, offersProgram, LLM in Business Law]
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: LLM in Business Law
Triple: [Gould School of Law, offersProgram, LLM in Business Law]
Generated description
LLM in Business Law is a specialized Master of Laws program focused on advanced study of corporate, commercial, and financial legal issues for law graduates and practitioners.

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_69f349115304819084ee91d345b6c8aa completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6bd3d783881909bc4b3335c0bfe1c completed May 3, 2026, 3:13 a.m.
NED1 Entity disambiguation (via context triple) batch_6a305706cb78819088a3cd05e5b2588c completed June 15, 2026, 7:48 p.m.
NEDg Description generation batch_6a3057e44e8481909b08f108fd22c6f4 completed June 15, 2026, 7:52 p.m.
NED2 Entity disambiguation (via description) batch_6a305879c8bc8190945a4ea71cf27ba8 completed June 15, 2026, 7:54 p.m.
Created at: May 1, 2026, 12:44 a.m.