Triple
T214631
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2009 Red Line collision |
E4791
|
entity |
| Predicate | involvedRollingStockModel |
P1305
|
FINISHED |
| Object | 1000-series railcars |
—
|
LITERAL 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: 1000-series railcars | Statement: [2009 Red Line collision, involvedRollingStockModel, 1000-series railcars]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: involvedRollingStockModel Context triple: [2009 Red Line collision, involvedRollingStockModel, 1000-series railcars]
-
A.
rollingStockType
chosen
Indicates the specific category or type of railway rolling stock associated with an entity (e.g., locomotive, passenger car, freight wagon).
-
B.
usesRollingStock
Indicates that one entity employs or operates specific rolling stock (such as rail vehicles) in its activities or services.
-
C.
rollingStockOperator
Indicates that an entity operates or manages rolling stock, such as trains or rail vehicles, in a railway system.
-
D.
railroadClass
Indicates the classification or category of a railroad according to an established system (e.g., by size, revenue, or regulatory status).
-
E.
trains
Indicates that one entity teaches, instructs, or coaches another entity to develop skills, knowledge, or abilities.
- 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_69a2575cb1dc8190a01ad332426dc339 |
completed | Feb. 28, 2026, 2:47 a.m. |
| NER | Named-entity recognition | batch_69a25dcd2b208190855d5d8d70a3acfc |
completed | Feb. 28, 2026, 3:15 a.m. |
| PD | Predicate disambiguation | batch_69a25b52190481908f299d26122bafd2 |
completed | Feb. 28, 2026, 3:04 a.m. |
Created at: Feb. 28, 2026, 2:52 a.m.