Triple
T17775006
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Huli District |
E443745
|
entity |
| Predicate | languageUsed |
P238
|
FINISHED |
| Object | Hokkien |
—
|
NE NERFINISHED |
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: [Huli District, languageUsed, Hokkien]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hokkien Context triple: [Huli District, languageUsed, 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 (2 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_69d8b9ef17708190bdf7e2adbf14ddc2 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4871c23408190aae7f1a2c77a10cb |
completed | April 19, 2026, 7:41 a.m. |
Created at: April 10, 2026, 10:12 a.m.