Triple

T2256118
Position Surface form Disambiguated ID Type / Status
Subject Kyoto University E49727 entity
Predicate memberOf P10 FINISHED
Object ASEA-UNINET E45219 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: ASEA-UNINET | Statement: [Kyoto University, memberOf, ASEA-UNINET]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ASEA-UNINET
Context triple: [Kyoto University, memberOf, ASEA-UNINET]
  • A. ASEA-UNINET chosen
    ASEA-UNINET is an international academic network that promotes cooperation in higher education and research between universities in Europe and Southeast Asia.
  • B. ASEC
    ASEC is the commonly used abbreviation for the ASEAN Secretariat, the administrative body that supports and coordinates the activities of the Association of Southeast Asian Nations.
  • C. ECASA
    ECASA is a Cuban state-owned company responsible for managing and operating the country’s civil airports and air terminals.
  • D. UCEI
    UCEI is the acronym for the Union of Italian Jewish Communities, the central organization representing and coordinating Jewish communities throughout Italy.
  • E. SEACEN Centre
    The SEACEN Centre is a regional training and research institution that supports central banks and monetary authorities in the Asia-Pacific region through capacity building and policy collaboration.
  • 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_69a88aaa9250819095e127d0d77e8a32 completed March 4, 2026, 7:40 p.m.
NER Named-entity recognition batch_69abc1559ff481908efe3f214b2570dc completed March 7, 2026, 6:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae71c69f088190a38254a8a3670124 completed March 9, 2026, 7:07 a.m.
Created at: March 4, 2026, 7:47 p.m.