Triple

T5887963
Position Surface form Disambiguated ID Type / Status
Subject Mitsubishi E130915 entity
Predicate hasMember P10 FINISHED
Object Mitsubishi Rayon E554267 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: Mitsubishi Rayon | Statement: [Mitsubishi, hasMember, Mitsubishi Rayon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mitsubishi Rayon
Context triple: [Mitsubishi, hasMember, Mitsubishi Rayon]
  • A. Mitsubishi Paper Mills
    Mitsubishi Paper Mills is a Japanese company in the Mitsubishi group that manufactures and sells a wide range of paper and paper-related products.
  • B. Yoshimoto Kogyo
    Yoshimoto Kogyo is a major Japanese entertainment conglomerate best known for managing comedians and producing comedy shows, theater, television, and other media.
  • C. Mitsubishi Plastics chosen
    Mitsubishi Plastics is a subsidiary of the Mitsubishi Group that specializes in the production and development of plastic and chemical materials for industrial and consumer applications.
  • D. Nippon Kobo
    Nippon Kobo was a Japanese design and architecture firm active in the mid-20th century, known for its collaborations with prominent modernist designers such as Charlotte Perriand.
  • E. Taisei Corporation
    Taisei Corporation is a major Japanese construction and civil engineering company known for leading large-scale infrastructure and landmark building projects in Japan and abroad.
  • 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_69c0085628dc8190b334c1b44c067efc completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c036af6330819081d9fa98a8c26633 completed March 22, 2026, 6:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0bff9ecdc819098823b003cec66a2 completed March 23, 2026, 4:22 a.m.
Created at: March 22, 2026, 3:57 p.m.