Triple
T1620178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Union Station (St. Louis) |
E35011
|
entity |
| Predicate | servedRailroads |
P782
|
FINISHED |
| Object | multiple railroad companies |
—
|
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: multiple railroad companies | Statement: [Union Station (St. Louis), servedRailroads, multiple railroad companies]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: servedRailroads Context triple: [Union Station (St. Louis), servedRailroads, multiple railroad companies]
-
A.
servedByRailroad
chosen
Indicates that a location or facility is provided with transportation or service by a railroad line or company.
-
B.
followsFormerRailroad
Indicates that something is aligned with or traces the route of a former railroad line.
-
C.
hasRailroadHistoryWith
Indicates a historical relationship or connection between entities involving railroads, such as shared development, operation, or significant events in railway history.
-
D.
hasRailroadHistory
Indicates that an entity is associated with, involved in, or notable for historical events, operations, or developments related to railroads.
-
E.
railroadNetwork
Indicates a relationship where locations are connected as part of the same system of railway lines and infrastructure used for train transport.
- 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_69a886023194819080a3fccd6e325d0e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aaf4a0ef748190ae52b9656474c0ef |
completed | March 6, 2026, 3:37 p.m. |
| PD | Predicate disambiguation | batch_69a907c731808190a1d998155041b3c1 |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:28 p.m.