Triple
T398909
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pittsburgh, Cincinnati, Chicago and St. Louis Railroad |
E9234
|
entity |
| Predicate | carriedCommodity |
P3553
|
FINISHED |
| Object | coal |
—
|
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: coal | Statement: [Pittsburgh, Cincinnati, Chicago and St. Louis Railroad, carriedCommodity, coal]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: carriedCommodity Context triple: [Pittsburgh, Cincinnati, Chicago and St. Louis Railroad, carriedCommodity, coal]
-
A.
mainCargo
Indicates that one entity serves as the primary or principal cargo carried or transported by another entity.
-
B.
commodity
chosen
Indicates that one entity is a tradable good or resource that is bought, sold, or exchanged in relation to another entity.
-
C.
carriedBy
Indicates that one entity is physically supported and transported by another entity.
-
D.
usesRollingStock
Indicates that one entity employs or operates specific rolling stock (such as rail vehicles) in its activities or services.
-
E.
shipUsed
Indicates that a particular ship was employed or utilized in carrying out an event, activity, or operation.
- 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_69a2e8004cb88190b92ed1add6abf41a |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2ec8d0ca881909d786e8eed9b6748 |
completed | Feb. 28, 2026, 1:24 p.m. |
| PD | Predicate disambiguation | batch_69a2e96d17d08190878d3a68b17d51ca |
completed | Feb. 28, 2026, 1:11 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.