Triple
T33480084
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chiba Urban Monorail |
E857445
|
entity |
| Predicate | railwayRollingStock |
P128290
|
FINISHED |
| Object | 1000 series trainsets |
—
|
NE NERFINISHED |
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: 1000 series trainsets | Statement: [Chiba Urban Monorail, railwayRollingStock, 1000 series trainsets]
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 (3 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. |
| PD | Predicate disambiguation | batch_69f78b8cb3a881909ebaac1b503988c2 |
completed | May 3, 2026, 5:53 p.m. |
Created at: May 1, 2026, 1:38 a.m.