Triple

T27407207
Position Surface form Disambiguated ID Type / Status
Subject Naniwa Ward E692035 entity
Predicate hasNeighbour P5707 FINISHED
Object 浪速区 (Japanese name of Naniwa Ward)
浪速区 is one of Osaka City's central wards, known for its dense urban neighborhoods, historic shopping districts, and proximity to major commercial and entertainment areas.
 E1770864 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: 浪速区 (Japanese name of Naniwa Ward) | Statement: [Naniwa Ward, hasNeighbour, 浪速区 (Japanese name of Naniwa Ward)]
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: 浪速区 (Japanese name of Naniwa Ward)
Triple: [Naniwa Ward, hasNeighbour, 浪速区 (Japanese name of Naniwa Ward)]
Generated description
浪速区 is one of Osaka City's central wards, known for its dense urban neighborhoods, historic shopping districts, and proximity to major commercial and entertainment areas.

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_69ef5205fc808190ad3efc5525b8e6d6 completed April 27, 2026, 12:09 p.m.
NER Named-entity recognition batch_69f62cd702e081909f5549c4aa6b837f completed May 2, 2026, 4:56 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12a7fddd5c81908a6c297025128ff6 completed May 24, 2026, 7:25 a.m.
NEDg Description generation batch_6a12a89d91708190a07d2e136d91f97a completed May 24, 2026, 7:28 a.m.
NED2 Entity disambiguation (via description) batch_6a12aa0e55a88190ae8b69a3063f47a7 completed May 24, 2026, 7:34 a.m.
Created at: April 27, 2026, 12:31 p.m.