Triple
T10529974
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yamaguchi Prefecture |
E248411
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Shunan
Shunan is an industrial city in western Japan known for its chemical and heavy manufacturing industries along the Seto Inland Sea.
|
E869657
|
NE FINISHED |
How this triple was built (4 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: Shunan | Statement: [Yamaguchi Prefecture, hasCity, Shunan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shunan Context triple: [Yamaguchi Prefecture, hasCity, Shunan]
-
A.
Bouyon
Bouyon is a small rural commune in southeastern France, situated in the Alpes-Maritimes department of the Provence-Alpes-Côte d’Azur region.
-
B.
Liye
Liye is an archaeological site in Hunan, China, renowned for yielding a large cache of Qin dynasty bamboo slips that significantly expanded knowledge of early Chinese legal and administrative systems.
-
C.
Yangsan
Yangsan is a city in South Gyeongsang Province, South Korea, known as a growing residential and educational hub near Busan.
-
D.
Yueyang
Yueyang is a historic port city in northeastern Hunan, China, best known for its location on the shores of Dongting Lake and its famous Yueyang Tower.
-
E.
Yangsansi
Yangsansi is a city in South Korea located within Gyeonggi Province, forming part of the greater Seoul metropolitan area.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Shunan Triple: [Yamaguchi Prefecture, hasCity, Shunan]
Generated description
Shunan is an industrial city in western Japan known for its chemical and heavy manufacturing industries along the Seto Inland Sea.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Shunan Target entity description: Shunan is an industrial city in western Japan known for its chemical and heavy manufacturing industries along the Seto Inland Sea.
-
A.
Bouyon
Bouyon is a small rural commune in southeastern France, situated in the Alpes-Maritimes department of the Provence-Alpes-Côte d’Azur region.
-
B.
Liye
Liye is an archaeological site in Hunan, China, renowned for yielding a large cache of Qin dynasty bamboo slips that significantly expanded knowledge of early Chinese legal and administrative systems.
-
C.
Yangsan
Yangsan is a city in South Gyeongsang Province, South Korea, known as a growing residential and educational hub near Busan.
-
D.
Yueyang
Yueyang is a historic port city in northeastern Hunan, China, best known for its location on the shores of Dongting Lake and its famous Yueyang Tower.
-
E.
Yangsansi
Yangsansi is a city in South Korea located within Gyeonggi Province, forming part of the greater Seoul metropolitan area.
- F. None of above. chosen
Provenance (5 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_69d381c5c7448190bec34bee7ec72bac |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d509f7d8ac8190b90c1a7f77b23545 |
completed | April 7, 2026, 1:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d90e3caf4c8190b19199f1a68a00de |
completed | April 10, 2026, 2:50 p.m. |
| NEDg | Description generation | batch_69d9107f488481908845aef0fdf6d60d |
completed | April 10, 2026, 3 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d911790010819093fc50952502fd59 |
completed | April 10, 2026, 3:04 p.m. |
Created at: April 6, 2026, 12:30 p.m.