Triple

T14930835
Position Surface form Disambiguated ID Type / Status
Subject Temple 87: Nagaoji E372258 entity
Predicate dedicatedTo P500 FINISHED
Object Kannon E234489 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: Kannon | Statement: [Temple 87: Nagaoji, dedicatedTo, Kannon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kannon
Context triple: [Temple 87: Nagaoji, dedicatedTo, Kannon]
  • A. Kannon chosen
    Kannon is the Japanese name for the bodhisattva of compassion, derived from the Buddhist deity Avalokiteshvara and widely venerated in Japan.
  • B. Ōsu Kannon
    Ōsu Kannon is a famous Buddhist temple in Nagoya, Japan, known for its large wooden statue of Kannon and its surrounding shopping arcade.
  • C. Takkaze
    Takkaze is a river in northern Ethiopia that flows through deep gorges before joining the Atbarah River, ultimately contributing to the Nile basin.
  • D. Gono
    Gono is a Zimbabwean surname most notably borne by Gideon Gono, the former governor of the Reserve Bank of Zimbabwe.
  • E. Kankinara
    Kankinara is a suburban locality in West Bengal, India, known for its railway station on the Kolkata suburban network and its surrounding residential and industrial areas.
  • 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_69d85cc9da0c81908d583ca3f63a3908 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded64550dc8190ba44120df00ba498 completed April 15, 2026, 12:05 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe7e8ac6d08190809045a6d00a3d47 completed May 9, 2026, 12:23 a.m.
Created at: April 10, 2026, 2:36 a.m.