Triple

T10058977
Position Surface form Disambiguated ID Type / Status
Subject Enryaku-ji E208933 entity
Predicate trainedFigure P91883 FINISHED
Object Eisai E395854 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: Eisai | Statement: [Enryaku-ji, trainedFigure, Eisai]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Eisai
Context triple: [Enryaku-ji, trainedFigure, Eisai]
  • A. Eisai
    Eisai is a Japanese pharmaceutical company known for developing treatments in neurology and oncology, including Alzheimer’s disease therapies.
  • B. Eisai chosen
    Eisai was a Japanese Buddhist monk of the Kamakura period best known for introducing Rinzai Zen and promoting tea culture in Japan.
  • C. Chugai Pharmaceutical
    Chugai Pharmaceutical is a major Japanese research-based pharmaceutical company known for its innovative biopharmaceuticals and strategic alliance with Roche.
  • D. Takeda Pharmaceutical Company
    Takeda Pharmaceutical Company is a leading global biopharmaceutical firm headquartered in Japan, focused on developing innovative medicines in areas such as oncology, gastroenterology, neuroscience, and rare diseases.
  • E. Mitsubishi Tanabe Pharma
    Mitsubishi Tanabe Pharma is a Japanese pharmaceutical company known for developing prescription drugs and biopharmaceuticals, including treatments for neurological and autoimmune diseases.
  • 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_69ca836094408190a36a1ea7e9a86fcd completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cdcfb0f17c8190a8c0cfb02863537d completed April 2, 2026, 2:08 a.m.
NED1 Entity disambiguation (via context triple) batch_69d29a5d4b308190b5b1ece1ca99be86 completed April 5, 2026, 5:22 p.m.
Created at: March 30, 2026, 8:57 p.m.