Triple

T8938073
Position Surface form Disambiguated ID Type / Status
Subject HUST E212827 entity
Predicate shortName P43 FINISHED
Object HUST E7178 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: HUST | Statement: [HUST, shortName, HUST]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: HUST
Context triple: [HUST, shortName, HUST]
  • A. HUST
    HUST is a leading Vietnamese technical university renowned for its engineering, science, and technology education and research.
  • B. Hefei University of Technology
    Hefei University of Technology is a major national key university in China known for its strong engineering, science, and technology programs.
  • C. Hangzhou Dianzi University
    Hangzhou Dianzi University is a Chinese public university in Hangzhou known for its strong programs in electronics, information technology, and engineering.
  • D. Huazhong University of Science and Technology chosen
    Huazhong University of Science and Technology is a major comprehensive research university in China, renowned for its strengths in engineering, science, and medical disciplines.
  • E. Hubei University of Technology
    Hubei University of Technology is a provincial public university in Wuhan, China, known for its engineering, technology, and applied sciences programs.
  • 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_69ca839694c88190b324ffeb43d23b08 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc66b57a348190979effe4f9998eb7 completed April 1, 2026, 12:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfc1e692548190b631c4926927d12f completed April 3, 2026, 1:34 p.m.
Created at: March 30, 2026, 6:58 p.m.