Triple

T7480591
Position Surface form Disambiguated ID Type / Status
Subject Kashiba E176745 entity
Predicate neighboringMunicipality P17964 FINISHED
Object Habikino E73678 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: Habikino | Statement: [Kashiba, neighboringMunicipality, Habikino]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Habikino
Context triple: [Kashiba, neighboringMunicipality, Habikino]
  • A. Habikino chosen
    Habikino is a city in Osaka Prefecture, Japan, known for its historic kofun burial mounds and role within the Osaka metropolitan area.
  • B. Hobokan
    Hobokan is an alternative name or spelling for the place or entity known as Hobuck.
  • C. Nakawa
    Nakawa is one of the energetic human hosts in Disney’s “Festival of the Lion King” stage show at Disney’s Animal Kingdom.
  • D. Kujūkuri
    Kujūkuri is a long, sandy coastal town and beach area on the eastern shore of Chiba Prefecture, Japan, known for its surfing spots and scenic Pacific shoreline.
  • E. Onikan
    Onikan is a historic neighborhood on Lagos Island in Lagos, Nigeria, known for its cultural landmarks, sports and event venues, and proximity to the city’s central business and administrative districts.
  • 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_69c69f236ce08190a04d7679f03b29b2 completed March 27, 2026, 3:15 p.m.
NER Named-entity recognition batch_69c6f532f4488190b6edaa96099c3b8f completed March 27, 2026, 9:22 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8682f438c8190bca55f7773fe6838 completed March 28, 2026, 11:45 p.m.
Created at: March 27, 2026, 3:42 p.m.