Triple
T11309644
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 交野市 |
E267803
|
entity |
| Predicate | 隣接自治体 |
P33892
|
FINISHED |
| Object |
京都府京田辺市
京都府京田辺市は、京都府南部に位置し、同志社大学のキャンパスや茶畑が広がる山城地域の都市です。
|
E917315
|
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: 京都府京田辺市 | Statement: [交野市, 隣接自治体, 京都府京田辺市]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 京都府京田辺市 Context triple: [交野市, 隣接自治体, 京都府京田辺市]
-
A.
Nakagyō-ku, Kyoto
Nakagyō-ku, Kyoto is a central ward of Kyoto City known for its historic sites, traditional streetscapes, and role as a cultural and commercial hub.
-
B.
Higashiyama-ku, Kyoto
Higashiyama-ku, Kyoto is a historic ward on Kyoto’s eastern side known for its preserved traditional streets, temples, and cultural landmarks.
-
C.
Kofu
Kofu is the capital city of Yamanashi Prefecture in central Japan, known for its surrounding mountains, hot springs, and proximity to the Fuji Five Lakes region.
-
D.
Bunkyō City
Bunkyō City is a special ward in central Tokyo, Japan, known for its universities, historic temples, and quiet residential neighborhoods.
-
E.
Fuchu, Tokyo
Fuchu, Tokyo is a city in western Tokyo Metropolis known for its blend of residential suburbs, historical sites, and major facilities such as racetracks and large cemeteries.
- 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: 京都府京田辺市 Triple: [交野市, 隣接自治体, 京都府京田辺市]
Generated description
京都府京田辺市は、京都府南部に位置し、同志社大学のキャンパスや茶畑が広がる山城地域の都市です。
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 京都府京田辺市 Target entity description: 京都府京田辺市は、京都府南部に位置し、同志社大学のキャンパスや茶畑が広がる山城地域の都市です。
-
A.
Nakagyō-ku, Kyoto
Nakagyō-ku, Kyoto is a central ward of Kyoto City known for its historic sites, traditional streetscapes, and role as a cultural and commercial hub.
-
B.
Higashiyama-ku, Kyoto
Higashiyama-ku, Kyoto is a historic ward on Kyoto’s eastern side known for its preserved traditional streets, temples, and cultural landmarks.
-
C.
Kofu
Kofu is the capital city of Yamanashi Prefecture in central Japan, known for its surrounding mountains, hot springs, and proximity to the Fuji Five Lakes region.
-
D.
Bunkyō City
Bunkyō City is a special ward in central Tokyo, Japan, known for its universities, historic temples, and quiet residential neighborhoods.
-
E.
Fuchu, Tokyo
Fuchu, Tokyo is a city in western Tokyo Metropolis known for its blend of residential suburbs, historical sites, and major facilities such as racetracks and large cemeteries.
- 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_69d6aaca5c24819083db46a30d86cb34 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e9c0b3b88190ac0e3d6a5ad3b9bc |
completed | April 9, 2026, 6:02 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e50a70022081908bc74185003a3503 |
completed | April 19, 2026, 5:01 p.m. |
| NEDg | Description generation | batch_69e510fb1e288190a7a38fe896d7b91d |
completed | April 19, 2026, 5:29 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e516bec3e481909cbd0d9c683d2191 |
completed | April 19, 2026, 5:54 p.m. |
Created at: April 8, 2026, 9:32 p.m.