Triple
T7606905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toyooka |
E180128
|
entity |
| Predicate | mergedWith |
P77
|
FINISHED |
| Object |
Kinosaki
Kinosaki is a famous hot spring resort area in Hyōgo Prefecture, Japan, renowned for its historic onsen baths and traditional ryokan inns.
|
E810378
|
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: Kinosaki | Statement: [Toyooka, mergedWith, Kinosaki]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kinosaki Context triple: [Toyooka, mergedWith, Kinosaki]
-
A.
Ōgaki
Ōgaki is a former municipality in Hiroshima Prefecture, Japan, that was incorporated into the city of Etajima.
-
B.
Kameyama
Kameyama is a city in Mie Prefecture, Japan, known historically as a post town on the Tōkaidō and for its preserved castle ruins and traditional streetscapes.
-
C.
Akiruno
Akiruno is a city in western Tokyo, Japan, known for its natural scenery, including rivers, forests, and hiking areas.
-
D.
Kasukabe
Kasukabe is a city in Japan known for its suburban character within the Greater Tokyo area and as the setting of the popular manga and anime series "Crayon Shin-chan."
-
E.
Kyotanabe
Kyotanabe is a city in Kyoto Prefecture, Japan, known for its residential suburbs, educational institutions, and location within the Kansai region.
- 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: Kinosaki Triple: [Toyooka, mergedWith, Kinosaki]
Generated description
Kinosaki is a famous hot spring resort area in Hyōgo Prefecture, Japan, renowned for its historic onsen baths and traditional ryokan inns.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kinosaki Target entity description: Kinosaki is a famous hot spring resort area in Hyōgo Prefecture, Japan, renowned for its historic onsen baths and traditional ryokan inns.
-
A.
Ōgaki
Ōgaki is a former municipality in Hiroshima Prefecture, Japan, that was incorporated into the city of Etajima.
-
B.
Kameyama
Kameyama is a city in Mie Prefecture, Japan, known historically as a post town on the Tōkaidō and for its preserved castle ruins and traditional streetscapes.
-
C.
Akiruno
Akiruno is a city in western Tokyo, Japan, known for its natural scenery, including rivers, forests, and hiking areas.
-
D.
Kasukabe
Kasukabe is a city in Japan known for its suburban character within the Greater Tokyo area and as the setting of the popular manga and anime series "Crayon Shin-chan."
-
E.
Kyotanabe
Kyotanabe is a city in Kyoto Prefecture, Japan, known for its residential suburbs, educational institutions, and location within the Kansai region.
- 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_69c69f3567008190ab01d2ca7b53584a |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c6f9fe10408190b1c12bb8f911cea8 |
completed | March 27, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d178a6467c8190aab201fb12d2a64e |
completed | April 4, 2026, 8:46 p.m. |
| NEDg | Description generation | batch_69d17b5514d881909cd5357ce21649a8 |
completed | April 4, 2026, 8:57 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d17bb23b68819083a54ff8a19f741c |
completed | April 4, 2026, 8:59 p.m. |
Created at: March 27, 2026, 3:54 p.m.