Triple

T12986628
Position Surface form Disambiguated ID Type / Status
Subject Cheong E321784 entity
Predicate usedInDialect P23892 FINISHED
Object Hokkien E410805 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: Hokkien | Statement: [Cheong, usedInDialect, Hokkien]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Hokkien
Context triple: [Cheong, usedInDialect, Hokkien]
  • A. Hokkien chosen
    Hokkien is a Southern Min Chinese language variety widely spoken in Taiwan, Southeast Asia, and parts of southern China, known for its rich tonal system and distinct vocabulary from Mandarin.
  • B. Hongū
    Hongū is the principal sanctuary building of Tsurugaoka Hachimangū Shrine in Kamakura, serving as its main place of worship and ritual.
  • C. Nanbu
    Nanbu is a town in Shizuoka Prefecture, Japan, known for its rural landscape and location near the border with Yamanashi Prefecture.
  • D. Kahama
    Kahama is a town and district-level administrative center in northwestern Tanzania known for its mining activities and role as a commercial hub in the Shinyanga area.
  • E. Nanyō
    Nanyō is a city in Yamagata Prefecture, Japan, known for its hot springs, cherry orchards, 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_69d8076479b8819090afce3591939cdf completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97e5f47ec8190b39107bc016f9824 completed April 10, 2026, 10:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6c0f95c548190a6fc2c1ea98246c3 completed May 3, 2026, 3:28 a.m.
Created at: April 9, 2026, 8:40 p.m.