Triple
T9182856
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saga Prefecture |
E220376
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Tosu
Tosu is a city in Japan’s Kyushu region known as a transportation hub and home to the professional football club Sagan Tosu.
|
E868224
|
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: Tosu | Statement: [Saga Prefecture, contains, Tosu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tosu Context triple: [Saga Prefecture, contains, Tosu]
-
A.
Karatsu
Karatsu is a coastal city in Saga Prefecture, Japan, known for its historic castle, traditional Karatsu ware pottery, and the annual Karatsu Kunchi festival.
-
B.
Ōsaki
Ōsaki is a major commercial and business district in Tokyo known for its high-rise office complexes and convenient rail connections.
-
C.
Toyokawa
Toyokawa is a city in Aichi Prefecture, Japan, known for its historic Toyokawa Inari temple and manufacturing industries.
-
D.
Yame
Yame is a city in southwestern Japan renowned for its high-quality green tea production and traditional crafts.
-
E.
Natori City
Natori City is a coastal municipality in northeastern Japan known for its proximity to Sendai and its recovery efforts following the 2011 Tōhoku earthquake and tsunami.
- 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: Tosu Triple: [Saga Prefecture, contains, Tosu]
Generated description
Tosu is a city in Japan’s Kyushu region known as a transportation hub and home to the professional football club Sagan Tosu.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tosu Target entity description: Tosu is a city in Japan’s Kyushu region known as a transportation hub and home to the professional football club Sagan Tosu.
-
A.
Karatsu
Karatsu is a coastal city in Saga Prefecture, Japan, known for its historic castle, traditional Karatsu ware pottery, and the annual Karatsu Kunchi festival.
-
B.
Ōsaki
Ōsaki is a major commercial and business district in Tokyo known for its high-rise office complexes and convenient rail connections.
-
C.
Toyokawa
Toyokawa is a city in Aichi Prefecture, Japan, known for its historic Toyokawa Inari temple and manufacturing industries.
-
D.
Yame
Yame is a city in southwestern Japan renowned for its high-quality green tea production and traditional crafts.
-
E.
Natori City
Natori City is a coastal municipality in northeastern Japan known for its proximity to Sendai and its recovery efforts following the 2011 Tōhoku earthquake and tsunami.
- 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_69ca83e6d77c81909862b7afef56b1bf |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccc2553e548190898434aeda517407 |
completed | April 1, 2026, 6:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d90d3dfbac819087d07c35a1776064 |
completed | April 10, 2026, 2:46 p.m. |
| NEDg | Description generation | batch_69d9100604e08190b744b361d60188d5 |
completed | April 10, 2026, 2:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d910a1cdb88190988db41e97341ed9 |
completed | April 10, 2026, 3 p.m. |
Created at: March 30, 2026, 7:23 p.m.