Triple

T2873648
Position Surface form Disambiguated ID Type / Status
Subject Shibuya City E56823 entity
Predicate contains P35 FINISHED
Object Ebisu E29485 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: Ebisu | Statement: [Shibuya City, contains, Ebisu]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ebisu
Context triple: [Shibuya City, contains, Ebisu]
  • A. Ebisu chosen
    Ebisu is a fashionable Tokyo neighborhood known for its upscale dining, craft beer scene, and convenient access via Ebisu Station near Shibuya.
  • B. Shiba
    Shiba is a central district in Minato, Tokyo, known for its mix of historic temples, business centers, and residential areas.
  • C. Kibushi
    Kibushi is a Bantu language spoken primarily in Mayotte, where it serves as one of the island’s main regional languages.
  • D. Boso
    Boso is the fictional dialogue partner and student of Anselm of Canterbury in the theological treatise "Cur Deus Homo," representing the questioning layperson in discussions about the Incarnation and Atonement.
  • E. Akita
    Akita is a city in Japan’s Tōhoku region, serving as the capital of Akita Prefecture and known for its port, rice production, and traditional festivals.
  • 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_69ab4a4ced288190ab6d3e062d10f7f6 completed March 6, 2026, 9:42 p.m.
NER Named-entity recognition batch_69abe0032ddc8190bb4d15ec7e3c63e8 completed March 7, 2026, 8:21 a.m.
NED1 Entity disambiguation (via context triple) batch_69b01db40e388190a208fe58e2ed6029 completed March 10, 2026, 1:33 p.m.
Created at: March 6, 2026, 10:03 p.m.