Triple
T15335412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gyoda |
E366650
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object |
Konosu
Konosu is a city in Saitama Prefecture, Japan, known for its traditional doll-making industry and large-scale seasonal flower displays.
|
E1387408
|
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: Konosu | Statement: [Gyoda, locatedNear, Konosu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Konosu Context triple: [Gyoda, locatedNear, Konosu]
-
A.
Kasugo
Kasugo is a specific class or rank within the Philippine Order of Sikatuna, a national honor conferred for distinguished diplomatic service.
-
B.
Kōonji
Kōonji is a Buddhist temple in Japan, known as Temple 61 on the Shikoku Pilgrimage.
-
C.
Tokoro
Tokoro is a coastal district of Kitami City in Hokkaido, Japan, known historically for its fishing industry and drift ice along the Sea of Okhotsk.
-
D.
Kawasoe
Kawasoe is a locality in Saga Prefecture, Japan, situated near Saga Airport and serving as part of the surrounding regional community.
-
E.
Tomonoura
Tomonoura is a historic port town in Hiroshima Prefecture, Japan, known for its scenic seaside views, traditional streetscapes, and role as inspiration for various works of art and film.
- 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: Konosu Triple: [Gyoda, locatedNear, Konosu]
Generated description
Konosu is a city in Saitama Prefecture, Japan, known for its traditional doll-making industry and large-scale seasonal flower displays.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Konosu Target entity description: Konosu is a city in Saitama Prefecture, Japan, known for its traditional doll-making industry and large-scale seasonal flower displays.
-
A.
Kasugo
Kasugo is a specific class or rank within the Philippine Order of Sikatuna, a national honor conferred for distinguished diplomatic service.
-
B.
Kōonji
Kōonji is a Buddhist temple in Japan, known as Temple 61 on the Shikoku Pilgrimage.
-
C.
Tokoro
Tokoro is a coastal district of Kitami City in Hokkaido, Japan, known historically for its fishing industry and drift ice along the Sea of Okhotsk.
-
D.
Kawasoe
Kawasoe is a locality in Saga Prefecture, Japan, situated near Saga Airport and serving as part of the surrounding regional community.
-
E.
Tomonoura
Tomonoura is a historic port town in Hiroshima Prefecture, Japan, known for its scenic seaside views, traditional streetscapes, and role as inspiration for various works of art and film.
- 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_69d85a1355608190a6673ddb67231d54 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e03c5f081908e4d14dbdbc7f7a6 |
completed | April 16, 2026, 1:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a077ec78090819084a2de678e52cdec |
completed | May 15, 2026, 8:15 p.m. |
| NEDg | Description generation | batch_6a077fe7a3d48190bec5fea223209e0b |
completed | May 15, 2026, 8:19 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07804bcfa8819084953039cef5a5d8 |
completed | May 15, 2026, 8:21 p.m. |
Created at: April 10, 2026, 3:17 a.m.