Triple

T24173567
Position Surface form Disambiguated ID Type / Status
Subject Sendai Port E599211 entity
Predicate regionServed P82 FINISHED
Object Tohoku industrial belt
The Tohoku industrial belt is a major Japanese coastal manufacturing and logistics corridor in the Tohoku region, characterized by heavy industry, ports, and related infrastructure.
E1627276 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: Tohoku industrial belt | Statement: [Sendai Port, regionServed, Tohoku industrial belt]
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: Tohoku industrial belt
Triple: [Sendai Port, regionServed, Tohoku industrial belt]
Generated description
The Tohoku industrial belt is a major Japanese coastal manufacturing and logistics corridor in the Tohoku region, characterized by heavy industry, ports, and related infrastructure.

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_69e288cbd62881909de32ca64a70c17b completed April 17, 2026, 7:23 p.m.
NER Named-entity recognition batch_69f1e17cd1f0819094d049ed2ee0766e completed April 29, 2026, 10:46 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0fc9a8b2f48190917a73835bd0d5f0 completed May 22, 2026, 3:12 a.m.
NEDg Description generation batch_6a0fca4823b48190a73f8d6f6d6de353 completed May 22, 2026, 3:15 a.m.
NED2 Entity disambiguation (via description) batch_6a0fcac9856c819082dba8010016ce83 completed May 22, 2026, 3:17 a.m.
Created at: April 17, 2026, 11:33 p.m.