Triple

T26808075
Position Surface form Disambiguated ID Type / Status
Subject Itabashi E671898 entity
Predicate hasTransportationInfrastructure P385 FINISHED
Object Metropolitan Expressway
The Metropolitan Expressway is a network of urban toll expressways serving the Greater Tokyo Area, facilitating high-speed vehicular traffic through Tokyo and surrounding prefectures.
E1762009 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: Metropolitan Expressway | Statement: [Itabashi, hasTransportationInfrastructure, Metropolitan Expressway]
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: Metropolitan Expressway
Triple: [Itabashi, hasTransportationInfrastructure, Metropolitan Expressway]
Generated description
The Metropolitan Expressway is a network of urban toll expressways serving the Greater Tokyo Area, facilitating high-speed vehicular traffic through Tokyo and surrounding prefectures.

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_69eeb3225a3c8190aaf6746efeded2f3 completed April 27, 2026, 12:51 a.m.
NER Named-entity recognition batch_69f61a1f1f8481908c75c51a505c505a completed May 2, 2026, 3:37 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12535f42d481908b3d3632f63ddf12 completed May 24, 2026, 1:24 a.m.
NEDg Description generation batch_6a12545544f881909f0afd8459986559 completed May 24, 2026, 1:28 a.m.
NED2 Entity disambiguation (via description) batch_6a125879112c8190959380eaef8ccf19 completed May 24, 2026, 1:46 a.m.
Created at: April 27, 2026, 4:27 a.m.