Triple

T36062374
Position Surface form Disambiguated ID Type / Status
Subject Lord of Rokujō estate E1043122 entity
Predicate associatedWith P37 FINISHED
Object Rokujō district
Rokujō district is a historical area in Kyoto, Japan, known from classical literature and courtly life as the setting for aristocratic residences and estates.
E2287864 NE FINISHED

How this triple was built (2 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: Rokujō district | Statement: [Lord of Rokujō estate, associatedWith, Rokujō district]
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: Rokujō district
Triple: [Lord of Rokujō estate, associatedWith, Rokujō district]
Generated description
Rokujō district is a historical area in Kyoto, Japan, known from classical literature and courtly life as the setting for aristocratic residences and estates.

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_69f76e2f09448190b0486d5ecad5e243 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f7b2137e508190ba2fadbce32a2376 completed May 3, 2026, 8:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a5a3468caa88190a4517ddb40e6b0d6 completed July 17, 2026, 1:55 p.m.
NEDg Description generation batch_6a5a34d1e9508190bd5e7d7ae087ebed completed July 17, 2026, 1:57 p.m.
NED2 Entity disambiguation (via description) batch_6a5a36dc67708190aeb72965c694ff36 completed July 17, 2026, 2:06 p.m.
Created at: May 3, 2026, 4:08 p.m.