Triple

T33480084
Position Surface form Disambiguated ID Type / Status
Subject Chiba Urban Monorail E857445 entity
Predicate railwayRollingStock P128290 FINISHED
Object 1000 series trainsets
The 1000 series trainsets are electric multiple-unit monorail trains operated by the Chiba Urban Monorail system in Chiba, Japan.
 E2054262 NE FINISHED

How this triple was built (3 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: 1000 series trainsets | Statement: [Chiba Urban Monorail, railwayRollingStock, 1000 series trainsets]
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: 1000 series trainsets
Triple: [Chiba Urban Monorail, railwayRollingStock, 1000 series trainsets]
Generated description
The 1000 series trainsets are electric multiple-unit monorail trains operated by the Chiba Urban Monorail system in Chiba, Japan.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: railwayRollingStock
Context triple: [Chiba Urban Monorail, railwayRollingStock, 1000 series trainsets]
  • A. passengerRollingStock chosen
    Indicates that the rolling stock is designed or used for carrying passengers rather than freight or other purposes.
  • B. formerRollingStock
    Indicates that an entity was previously used as rolling stock (e.g., railway vehicles) but no longer serves in that capacity.
  • C. notableRollingStock
    Indicates that there is a notable or historically significant piece of rolling stock (such as a train car or locomotive) associated with the subject.
  • D. railcode
    Indicates that an entity is associated with a specific railway code used for identification or classification within a rail system.
  • E. servicesRollingStock
    Indicates that an entity provides operational services to or performs work on rolling stock (such as trains or rail vehicles).
  • F. None of above.

Provenance (6 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_69f3497472508190b300ebd3fd402367 completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69f78c61ed4c8190ad84c918fa9af55a completed May 3, 2026, 5:56 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3595b1a3e881908d8c304db59a75e9 completed June 19, 2026, 7:17 p.m.
NEDg Description generation batch_6a3596c81644819097f9237ed4ec071d completed June 19, 2026, 7:21 p.m.
NED2 Entity disambiguation (via description) batch_6a35978115108190894086f7f04c81cf completed June 19, 2026, 7:24 p.m.
PD Predicate disambiguation batch_69f78b8cb3a881909ebaac1b503988c2 completed May 3, 2026, 5:53 p.m.
Created at: May 1, 2026, 1:38 a.m.