Triple

T17663192
Position Surface form Disambiguated ID Type / Status
Subject DURL E440302 entity
Predicate hasMember P10 FINISHED
Object Mitsubishi Corporation 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: Mitsubishi Corporation | Statement: [DURL, hasMember, Mitsubishi Corporation]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mitsubishi Corporation
Context triple: [DURL, hasMember, Mitsubishi Corporation]
  • A. Mitsubishi Corporation chosen
    Mitsubishi Corporation is a major Japanese multinational trading and investment company involved in a wide range of sectors including energy, machinery, chemicals, and infrastructure projects worldwide.
  • B. Mitsubishi Group
    Mitsubishi Group is a major Japanese conglomerate (keiretsu) comprising numerous companies across industries such as finance, heavy industry, automotive, and electronics.
  • C. Marubeni
    Marubeni is a major Japanese trading and investment conglomerate involved in a wide range of sectors including energy, infrastructure, chemicals, and food.
  • D. Sumitomo Corporation
    Sumitomo Corporation is a major Japanese multinational trading and investment company and one of the core members of the Sumitomo Group conglomerate.
  • E. Sumitomo Group
    Sumitomo Group is one of Japan’s largest and oldest keiretsu conglomerates, with diversified interests spanning industries such as finance, manufacturing, mining, and electronics.
  • 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_69d8b9e87e18819087104a44dc4dc5b1 completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e46ea73c90819087a23a7b6171f581 completed April 19, 2026, 5:56 a.m.
Created at: April 10, 2026, 9:51 a.m.