Triple

T7985011
Position Surface form Disambiguated ID Type / Status
Subject Azure Machine Learning E185664 entity
Predicate supports P516 FINISHED
Object R E98913 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: R | Statement: [Azure Machine Learning, supports, R]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: R
Context triple: [Azure Machine Learning, supports, R]
  • A. R
    R is a New York City Subway service that runs along the Broadway Line in Manhattan and Queens, providing local transit through key commercial and residential areas.
  • B. R chosen
    R is a widely used open-source programming language and environment focused on statistical computing, data analysis, and graphical visualization.
  • C. R
    R is a post-nominal letter used to denote a specific rank or class within the Danish Order of the Dannebrog.
  • D. Re
    Re is the ancient Egyptian sun god, a major deity associated with creation, kingship, and the daily journey of the sun across the sky.
  • E. RA
    RA is the commonly used abbreviation for Rugby Australia, the governing body for rugby union in Australia.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ca829a2cfc819083d591d58ec04075 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3c4a55b881909a96133e56c0dffa completed March 31, 2026, 3:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69cbe0e0b2748190930c22c6157d1b07 completed March 31, 2026, 2:57 p.m.
Created at: March 30, 2026, 5:15 p.m.