Triple

T26703425
Position Surface form Disambiguated ID Type / Status
Subject Nippori Fabric Town E673221 entity
Predicate locatedIn P40 FINISHED
Object Nippori
Nippori is a neighborhood in Tokyo, Japan, known for its traditional atmosphere, historic temples, and convenient rail connections including the Nippori-Toneri Liner and access to Narita Airport.
E2042644 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: Nippori | Statement: [Nippori Fabric Town, locatedIn, Nippori]
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: Nippori
Triple: [Nippori Fabric Town, locatedIn, Nippori]
Generated description
Nippori is a neighborhood in Tokyo, Japan, known for its traditional atmosphere, historic temples, and convenient rail connections including the Nippori-Toneri Liner and access to Narita Airport.

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_69eecda2b49c8190a6c481cfc4c07954 completed April 27, 2026, 2:44 a.m.
NER Named-entity recognition batch_69f6178140788190b8492b75a2eb7cc4 completed May 2, 2026, 3:25 p.m.
NED1 Entity disambiguation (via context triple) batch_6a352f92c63c819098035a4d045b0a0a completed June 19, 2026, 12:01 p.m.
NEDg Description generation batch_6a3533aaf1d881909e9f0981fae87c6d completed June 19, 2026, 12:18 p.m.
NED2 Entity disambiguation (via description) batch_6a35341981848190941fffd6dd22b4c1 completed June 19, 2026, 12:20 p.m.
Created at: April 27, 2026, 3:32 a.m.