Triple
T1290454
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Class I freight railroads in the United States |
E27533
|
entity |
| Predicate | typicalTraffic |
P621
|
FINISHED |
| Object | intermodal containers |
—
|
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: intermodal containers | Statement: [Class I freight railroads in the United States, typicalTraffic, intermodal containers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalTraffic Context triple: [Class I freight railroads in the United States, typicalTraffic, intermodal containers]
-
A.
trafficType
chosen
Indicates the category or nature of traffic involved in a given interaction, flow, or connection (e.g., type of network, data, or transport traffic).
-
B.
trafficDirection
Indicates the direction in which traffic is intended or allowed to move relative to a given reference point or segment.
-
C.
trafficLevel
Indicates the degree of congestion or flow intensity present in a transportation network or route at a given time.
-
D.
roadTraffic
Indicates the presence, flow, or conditions of vehicles and movement along roads or streets.
-
E.
annualTraffic
Indicates the typical amount or volume of traffic associated with something over the course of a year.
- 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_69a496d4ec448190ad653b2590c46711 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c0d6af908190ade9c481cc6f0ad8 |
completed | March 1, 2026, 10:42 p.m. |
| PD | Predicate disambiguation | batch_69a4bee41ca08190b0ad6f7ea40c0b62 |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:51 p.m.