Triple

T17736216
Position Surface form Disambiguated ID Type / Status
Subject Kenchō-ji E442724 entity
Predicate hasPart P35 FINISHED
Object Hattō
Hattō is the main lecture and Dharma hall in a Zen Buddhist temple complex, used for sermons, ceremonies, and important monastic gatherings.
 E1582926 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: Hattō | Statement: [Kenchō-ji, hasPart, Hattō]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hattō
Context triple: [Kenchō-ji, hasPart, Hattō]
  • A. Higashiura
    Higashiura is a town in central Japan located within Aichi Prefecture, known as a residential community in the Chita Peninsula area.
  • B. Seishirō
    Seishirō is a Japanese given name commonly used for male individuals.
  • C. Ichigaya
    Ichigaya is a central Tokyo district known for its major railway station, government and educational institutions, and proximity to the Imperial Palace area.
  • D. Hiranaka
    Hiranaka is a Japanese surname borne by individuals such as former professional boxer Akinobu Hiranaka.
  • E. Marunouchi
    Marunouchi is a central Tokyo business district known for its concentration of corporate headquarters, upscale offices, and proximity to Tokyo Station and the Imperial Palace.
  • 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: Hattō
Triple: [Kenchō-ji, hasPart, Hattō]
Generated description
Hattō is the main lecture and Dharma hall in a Zen Buddhist temple complex, used for sermons, ceremonies, and important monastic gatherings.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hattō
Target entity description: Hattō is the main lecture and Dharma hall in a Zen Buddhist temple complex, used for sermons, ceremonies, and important monastic gatherings.
  • A. Higashiura
    Higashiura is a town in central Japan located within Aichi Prefecture, known as a residential community in the Chita Peninsula area.
  • B. Seishirō
    Seishirō is a Japanese given name commonly used for male individuals.
  • C. Ichigaya
    Ichigaya is a central Tokyo district known for its major railway station, government and educational institutions, and proximity to the Imperial Palace area.
  • D. Hiranaka
    Hiranaka is a Japanese surname borne by individuals such as former professional boxer Akinobu Hiranaka.
  • E. Marunouchi
    Marunouchi is a central Tokyo business district known for its concentration of corporate headquarters, upscale offices, and proximity to Tokyo Station and the Imperial Palace.
  • 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_69d8b9ed3a2081909b2ec0d4dd2f4c37 completed April 10, 2026, 8:50 a.m.
NER Named-entity recognition batch_69e478eaff6c81909c7bd438b8c6c987 completed April 19, 2026, 6:40 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0c5da47b648190a4ba7792f2b36999 completed May 19, 2026, 12:55 p.m.
NEDg Description generation batch_6a0c60283668819096076b1bf406eaa0 completed May 19, 2026, 1:05 p.m.
NED2 Entity disambiguation (via description) batch_6a0c60b52fe88190854803902df750c5 completed May 19, 2026, 1:08 p.m.
Created at: April 10, 2026, 10:08 a.m.