Triple

T20238764
Position Surface form Disambiguated ID Type / Status
Subject China Medical University (Shenyang) E498223 entity
Predicate alternativeName P39 FINISHED
Object CMU NE NERFINISHED

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: CMU | Statement: [China Medical University (Shenyang), alternativeName, CMU]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: CMU
Context triple: [China Medical University (Shenyang), alternativeName, CMU]
  • A. CMU
    CMU is a private research university in Pittsburgh, Pennsylvania, renowned for its leading programs in computer science, engineering, and the arts.
  • B. CMU
    CMU is a public university in Grand Junction, Colorado, known for its diverse undergraduate programs and strong regional presence on the Western Slope.
  • C. CMU chosen
    CMU is a major medical university located in Shenyang, China, known for its education and research in clinical medicine and related health sciences.
  • D. CMU
    CMU is a major public research university in Chiang Mai, Thailand, known for its comprehensive academic programs and role as a leading educational institution in northern Thailand.
  • E. CMU College of Engineering
    CMU College of Engineering is the engineering school of Carnegie Mellon University, renowned for its cutting-edge research and education in areas such as robotics, computer engineering, and materials science.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69da6274c58c81909c646eabed6f4f30 completed April 11, 2026, 3:02 p.m.
NER Named-entity recognition batch_69e6716c8de88190916bfa1d6b7f79cb completed April 20, 2026, 6:33 p.m.
Created at: April 11, 2026, 11:40 p.m.