Triple

T37647430
Position Surface form Disambiguated ID Type / Status
Subject Urayasu E937077 entity
Predicate servedBy P82 FINISHED
Object Tokyo Bay City Kotsu bus network
The Tokyo Bay City Kotsu bus network is a local public transportation system operating buses in and around Urayasu in Chiba Prefecture, Japan.
 E2237714 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: Tokyo Bay City Kotsu bus network | Statement: [Urayasu, servedBy, Tokyo Bay City Kotsu bus network]
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: Tokyo Bay City Kotsu bus network
Triple: [Urayasu, servedBy, Tokyo Bay City Kotsu bus network]
Generated description
The Tokyo Bay City Kotsu bus network is a local public transportation system operating buses in and around Urayasu in Chiba Prefecture, Japan.

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_69f76ed4fe908190b8061c5c135e0971 completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fba987acf0819098d44ba33e0fff60 completed May 6, 2026, 8:50 p.m.
NED1 Entity disambiguation (via context triple) batch_6a40ba4e373c81908b9dc8f558c6b0b1 completed June 28, 2026, 6:08 a.m.
NEDg Description generation batch_6a40bbeaa9c88190ad99a22f4dff4abb completed June 28, 2026, 6:15 a.m.
NED2 Entity disambiguation (via description) batch_6a40bc8341cc8190bbabc1edad255068 completed June 28, 2026, 6:17 a.m.
Created at: May 3, 2026, 4:18 p.m.