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.