Triple
T17734434
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sanin Main Line |
E442675
|
entity |
| Predicate | connectsCity |
P4245
|
FINISHED |
| Object |
Hamada
Hamada is a coastal city in Shimane Prefecture, Japan, known for its fishing industry, beaches, and role as a regional transport hub.
|
E1286285
|
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: Hamada | Statement: [Sanin Main Line, connectsCity, Hamada]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hamada Context triple: [Sanin Main Line, connectsCity, Hamada]
-
A.
Hamada
Hamada is the surname of Hiro Hamada, the young robotics prodigy and main protagonist of Disney's animated film "Big Hero 6."
-
B.
Hadano
Hadano is a city in Kanagawa Prefecture, Japan, known for its natural scenery, hiking trails, and proximity to the Tanzawa Mountains.
-
C.
Harada
Harada is a fictional character from the X-Men film universe, depicted as a skilled Japanese warrior and bodyguard in "The Wolverine."
-
D.
Kominato
Kominato is a coastal area in present-day Chiba Prefecture, Japan, historically known as the birthplace of the Buddhist monk Nichiren.
-
E.
Kōta
Kōta is a town in central Japan known for its manufacturing industries and location within Aichi Prefecture.
- 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: Hamada Triple: [Sanin Main Line, connectsCity, Hamada]
Generated description
Hamada is a coastal city in Shimane Prefecture, Japan, known for its fishing industry, beaches, and role as a regional transport hub.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hamada Target entity description: Hamada is a coastal city in Shimane Prefecture, Japan, known for its fishing industry, beaches, and role as a regional transport hub.
-
A.
Hamada
Hamada is the surname of Hiro Hamada, the young robotics prodigy and main protagonist of Disney's animated film "Big Hero 6."
-
B.
Hadano
Hadano is a city in Kanagawa Prefecture, Japan, known for its natural scenery, hiking trails, and proximity to the Tanzawa Mountains.
-
C.
Harada
Harada is a fictional character from the X-Men film universe, depicted as a skilled Japanese warrior and bodyguard in "The Wolverine."
-
D.
Kominato
Kominato is a coastal area in present-day Chiba Prefecture, Japan, historically known as the birthplace of the Buddhist monk Nichiren.
-
E.
Kōta
Kōta is a town in central Japan known for its manufacturing industries and location within Aichi Prefecture.
- 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_69d8b9ed3a2081909b2ec0d4dd2f4c37 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e478e98a00819089490be2aa36873d |
completed | April 19, 2026, 6:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a02efb0cb1881909065885ae936d82f |
completed | May 12, 2026, 9:15 a.m. |
| NEDg | Description generation | batch_6a02f033da3081908124079eedc3a5c7 |
completed | May 12, 2026, 9:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a02f103b3c8819097dda16ec3dfa84e |
completed | May 12, 2026, 9:21 a.m. |
Created at: April 10, 2026, 10:08 a.m.