Triple

T30640393
Position Surface form Disambiguated ID Type / Status
Subject Tenjimbashisuji Rokuchōme Station E779957 entity
Predicate hasNearbyAttraction P2064 FINISHED
Object Tenjimbashisuji Shotengai
Tenjimbashisuji Shotengai is one of Japan’s longest covered shopping streets in Osaka, lined with numerous shops, restaurants, and entertainment spots that attract both locals and tourists.
E1924529 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: Tenjimbashisuji Shotengai | Statement: [Tenjimbashisuji Rokuchōme Station, hasNearbyAttraction, Tenjimbashisuji Shotengai]
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: Tenjimbashisuji Shotengai
Triple: [Tenjimbashisuji Rokuchōme Station, hasNearbyAttraction, Tenjimbashisuji Shotengai]
Generated description
Tenjimbashisuji Shotengai is one of Japan’s longest covered shopping streets in Osaka, lined with numerous shops, restaurants, and entertainment spots that attract both locals and tourists.

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_69f224a50ebc81909b961a94c7f66b12 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f68a557e308190a55aa6958b6d012e completed May 2, 2026, 11:35 p.m.
NED1 Entity disambiguation (via context triple) batch_6a2863f45fe08190b9444199303a07b9 completed June 9, 2026, 7:05 p.m.
NEDg Description generation batch_6a28683a08c48190992c65e4ebd02e56 completed June 9, 2026, 7:23 p.m.
NED2 Entity disambiguation (via description) batch_6a2868fa5d1c81909ec9422f7d152a75 completed June 9, 2026, 7:26 p.m.
Created at: April 29, 2026, 8:29 p.m.