Triple
T9580698
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2009 Fort Totten crash |
E231160
|
entity |
| Predicate | involvedRollingStockSeries |
P45556
|
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 Fort Totten crash, involvedRollingStockSeries, 1000-series railcars]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: involvedRollingStockSeries Context triple: [2009 Fort Totten crash, involvedRollingStockSeries, 1000-series railcars]
-
A.
introducedRollingStock
Indicates that an entity caused new rolling stock (such as trains or rail vehicles) to be put into service or use.
-
B.
ownedRollingStock
Indicates that one entity possesses or has ownership rights over specific rolling stock (such as trains, railcars, or locomotives).
-
C.
rollingStockFamily
chosen
Indicates a relationship where a piece of rolling stock belongs to, or is classified under, a particular family or series of related rolling stock designs.
-
D.
formerRollingStock
Indicates that an entity was previously used as rolling stock (e.g., railway vehicles) but no longer serves in that capacity.
-
E.
rollingStockType
Indicates the specific category or type of railway rolling stock associated with an entity (e.g., locomotive, passenger car, freight wagon).
- 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_69ca848091c48190bc313d6620d09555 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd99cbe79081909947b4d1389eb015 |
completed | April 1, 2026, 10:18 p.m. |
| PD | Predicate disambiguation | batch_69ccd59fd7408190b36831902e3f37f7 |
completed | April 1, 2026, 8:21 a.m. |
Created at: March 30, 2026, 8:05 p.m.