Triple

T8242556
Position Surface form Disambiguated ID Type / Status
Subject Narita E192571 entity
Predicate hasRomanization P2508 FINISHED
Object Narita-shi E727731 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: Narita-shi | Statement: [Narita, hasRomanization, Narita-shi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Narita-shi
Context triple: [Narita, hasRomanization, Narita-shi]
  • A. Narita, Chiba chosen
    Narita, Chiba is a city in Japan’s Chiba Prefecture best known internationally as the location of Narita International Airport, one of the Tokyo area’s major air gateways.
  • B. Hachiōji
    Hachiōji is a city in western Tokyo, Japan, known as a regional commercial and educational hub with rich historical sites and access to nearby mountains and nature.
  • C. Akishima
    Akishima is a city in western Tokyo, Japan, known as part of the Tama area and characterized by its residential neighborhoods and light industry.
  • D. Ibaraki City
    Ibaraki City is a suburban city in northern Osaka Prefecture, Japan, known as a residential and commercial hub between Osaka and Kyoto.
  • E. Bunkyō City
    Bunkyō City is a special ward in central Tokyo, Japan, known for its universities, historic temples, and quiet residential neighborhoods.
  • 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_69ca82dc8f148190a2c75a98501a7b91 completed March 30, 2026, 2:04 p.m.
NER Named-entity recognition batch_69cb783f67708190a4e1c4078c3a6fb0 completed March 31, 2026, 7:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69cf5120159881908361bf9cb81e3932 completed April 3, 2026, 5:33 a.m.
Created at: March 30, 2026, 5:47 p.m.