Triple

T16967881
Position Surface form Disambiguated ID Type / Status
Subject 津山市 E411588 entity
Predicate hasLandmark P105 FINISHED
Object 津山城
津山城は、岡山県津山市に位置する江戸時代初期築城の平山城で、桜の名所としても知られる日本の歴史的城跡です。
 E1243257 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: 津山城 | Statement: [津山市, hasLandmark, 津山城]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: 津山城
Context triple: [津山市, hasLandmark, 津山城]
  • A. 津山市
    津山市 is a historic castle town and regional city in Okayama Prefecture, Japan, known for Tsuyama Castle ruins and its preserved traditional streetscapes.
  • B. 福知山市
    福知山市 is a city in northern Kyoto Prefecture, Japan, known as a regional commercial and transportation hub with a mix of historical sites and rural landscapes.
  • C. 木津川市
    木津川市は、京都府南部に位置し、奈良県に隣接する住宅都市・歴史観光地として発展している市です。
  • D. 橋本市
    橋本市は、和歌山県北東部に位置し、高野山への玄関口として知られる都市です。
  • E. 米原市
    米原市は、滋賀県北東部に位置し、東海道新幹線や在来線が交差する交通の要衝として知られる市です。
  • 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: 津山城
Triple: [津山市, hasLandmark, 津山城]
Generated description
津山城は、岡山県津山市に位置する江戸時代初期築城の平山城で、桜の名所としても知られる日本の歴史的城跡です。
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: 津山城
Target entity description: 津山城は、岡山県津山市に位置する江戸時代初期築城の平山城で、桜の名所としても知られる日本の歴史的城跡です。
  • A. 津山市
    津山市 is a historic castle town and regional city in Okayama Prefecture, Japan, known for Tsuyama Castle ruins and its preserved traditional streetscapes.
  • B. 福知山市
    福知山市 is a city in northern Kyoto Prefecture, Japan, known as a regional commercial and transportation hub with a mix of historical sites and rural landscapes.
  • C. 木津川市
    木津川市は、京都府南部に位置し、奈良県に隣接する住宅都市・歴史観光地として発展している市です。
  • D. 橋本市
    橋本市は、和歌山県北東部に位置し、高野山への玄関口として知られる都市です。
  • E. 米原市
    米原市は、滋賀県北東部に位置し、東海道新幹線や在来線が交差する交通の要衝として知られる市です。
  • 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_69d886c9c9d481909afe222093641cae completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d0a6f628819080db47285954729a completed April 18, 2026, 6:42 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00d46f1d608190befe4dcbda086c03 completed May 10, 2026, 6:54 p.m.
NEDg Description generation batch_6a00d619d0f88190904a8afdd02c6f54 completed May 10, 2026, 7:01 p.m.
NED2 Entity disambiguation (via description) batch_6a00d67eb2a48190b57e394925181f70 completed May 10, 2026, 7:03 p.m.
Created at: April 10, 2026, 5:31 a.m.