Triple

T14284799
Position Surface form Disambiguated ID Type / Status
Subject 港区 E354141 entity
Predicate contains P35 FINISHED
Object 汐留
汐留 is a modern waterfront district in Tokyo known for its high-rise office towers, media headquarters, shopping complexes, and proximity to Shiodome Shiosite and Hamarikyu Gardens.
 E1090671 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: 汐留 | Statement: [港区, contains, 汐留]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: 汐留
Context triple: [港区, contains, 汐留]
  • A. 二子玉川
    二子玉川 is a riverside commercial and residential district in Tokyo known for its large shopping complexes, stylish cafes, and family-friendly urban development along the Tama River.
  • B. 高輪
    高輪 is a district in Minato, Tokyo, known for its mix of historic temples, residential areas, and proximity to major transport hubs like Shinagawa.
  • C. 神保町
    神保町 is a Tokyo neighborhood famed as Japan’s largest used-book district, lined with countless bookstores, publishers, and cozy cafés.
  • D. 根津
    根津は東京都文京区に位置し、古い町並みと根津神社で知られる歴史ある下町エリアです。
  • E. 赤坂
    赤坂は、東京都港区に位置し、官公庁や高級ホテル、飲食店が集まるビジネス・商業エリアとして知られる街です。
  • 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: 汐留
Triple: [港区, contains, 汐留]
Generated description
汐留 is a modern waterfront district in Tokyo known for its high-rise office towers, media headquarters, shopping complexes, and proximity to Shiodome Shiosite and Hamarikyu Gardens.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: 汐留
Target entity description: 汐留 is a modern waterfront district in Tokyo known for its high-rise office towers, media headquarters, shopping complexes, and proximity to Shiodome Shiosite and Hamarikyu Gardens.
  • A. 二子玉川
    二子玉川 is a riverside commercial and residential district in Tokyo known for its large shopping complexes, stylish cafes, and family-friendly urban development along the Tama River.
  • B. 高輪
    高輪 is a district in Minato, Tokyo, known for its mix of historic temples, residential areas, and proximity to major transport hubs like Shinagawa.
  • C. 神保町
    神保町 is a Tokyo neighborhood famed as Japan’s largest used-book district, lined with countless bookstores, publishers, and cozy cafés.
  • D. 根津
    根津は東京都文京区に位置し、古い町並みと根津神社で知られる歴史ある下町エリアです。
  • E. 赤坂
    赤坂は、東京都港区に位置し、官公庁や高級ホテル、飲食店が集まるビジネス・商業エリアとして知られる街です。
  • 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_69d8278d25148190abf1a8c8f5f533ad completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de697ef40c8190bea37724b28c2e99 completed April 14, 2026, 4:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd3d1a6d8081908e857143c0c809c0 completed May 8, 2026, 1:32 a.m.
NEDg Description generation batch_69fd3da6f0648190876dd86dd51e72cc completed May 8, 2026, 1:34 a.m.
NED2 Entity disambiguation (via description) batch_69fd3e30868481908b55b368ab45c7fb completed May 8, 2026, 1:36 a.m.
Created at: April 10, 2026, 1:10 a.m.