Triple

T27946174
Position Surface form Disambiguated ID Type / Status
Subject Microsoft Learn E700882 entity
Predicate integratedWith P2830 FINISHED
Object Microsoft certification exams
Microsoft certification exams are standardized tests offered by Microsoft to validate individuals’ skills and knowledge in using and administering Microsoft technologies and solutions.
 E1798398 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: Microsoft certification exams | Statement: [Microsoft Learn, integratedWith, Microsoft certification exams]
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: Microsoft certification exams
Triple: [Microsoft Learn, integratedWith, Microsoft certification exams]
Generated description
Microsoft certification exams are standardized tests offered by Microsoft to validate individuals’ skills and knowledge in using and administering Microsoft technologies and solutions.

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_69ef6a5028108190a14696d9821dde49 completed April 27, 2026, 1:53 p.m.
NER Named-entity recognition batch_69f63ad18cb48190aafdf938b5978b37 completed May 2, 2026, 5:56 p.m.
NED1 Entity disambiguation (via context triple) batch_6a13116295d881909632f7fa9eee9254 completed May 24, 2026, 2:55 p.m.
NEDg Description generation batch_6a1311f106748190b256e38ceb2481f2 completed May 24, 2026, 2:57 p.m.
NED2 Entity disambiguation (via description) batch_6a159f7e09a88190a7e25e30dfd87d3d completed May 26, 2026, 1:26 p.m.
Created at: April 27, 2026, 7:22 p.m.