Triple

T5686168
Position Surface form Disambiguated ID Type / Status
Subject Rokkomichi Station E125317 entity
Predicate near P350 FINISHED
Object Nada Ward Office
Nada Ward Office is the main administrative government office serving Kobe’s Nada Ward in Hyōgo Prefecture, Japan.
 E541476 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: Nada Ward Office | Statement: [Rokkomichi Station, near, Nada Ward Office]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Nada Ward Office
Context triple: [Rokkomichi Station, near, Nada Ward Office]
  • A. Granberry Ward
    Granberry Ward is a sibling of American actress and producer Sela Ward.
  • B. Hạ Đình Ward
    Hạ Đình Ward is an urban administrative subdivision located within Thanh Xuân District of Hanoi, Vietnam.
  • C. Nadine Tolliver
    Nadine Tolliver is a key fictional character in the political drama series "Madam Secretary," serving as the capable and loyal chief of staff to Secretary of State Elizabeth McCord.
  • D. Nancy Shevell
    Nancy Shevell is an American businesswoman and heiress best known for her long-term relationship and marriage to musician Paul McCartney.
  • E. Laura Harris
    Laura Harris is a Canadian actress known for her roles in films like "The Faculty" and TV series such as "24" and "Dead Like Me."
  • 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: Nada Ward Office
Triple: [Rokkomichi Station, near, Nada Ward Office]
Generated description
Nada Ward Office is the main administrative government office serving Kobe’s Nada Ward in Hyōgo Prefecture, Japan.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Nada Ward Office
Target entity description: Nada Ward Office is the main administrative government office serving Kobe’s Nada Ward in Hyōgo Prefecture, Japan.
  • A. Granberry Ward
    Granberry Ward is a sibling of American actress and producer Sela Ward.
  • B. Hạ Đình Ward
    Hạ Đình Ward is an urban administrative subdivision located within Thanh Xuân District of Hanoi, Vietnam.
  • C. Nadine Tolliver
    Nadine Tolliver is a key fictional character in the political drama series "Madam Secretary," serving as the capable and loyal chief of staff to Secretary of State Elizabeth McCord.
  • D. Nancy Shevell
    Nancy Shevell is an American businesswoman and heiress best known for her long-term relationship and marriage to musician Paul McCartney.
  • E. Laura Harris
    Laura Harris is a Canadian actress known for her roles in films like "The Faculty" and TV series such as "24" and "Dead Like Me."
  • 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_69c0082a884c8190a79001bae658941f completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c023bbfb988190bb61c7d183660d5d completed March 22, 2026, 5:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69c05a40b3808190bc57fde5990ac04e completed March 22, 2026, 9:08 p.m.
NEDg Description generation batch_69c05cd2dea88190bc79ca0a7709e7ca completed March 22, 2026, 9:19 p.m.
NED2 Entity disambiguation (via description) batch_69c05d8c85f88190a1a962794eeecd8d completed March 22, 2026, 9:22 p.m.
Created at: March 22, 2026, 3:44 p.m.