Triple
T2550302
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Grange, Kentucky |
E56608
|
entity |
| Predicate | hasRailroadTracksRunningThroughDowntown |
P40778
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [La Grange, Kentucky, hasRailroadTracksRunningThroughDowntown, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRailroadTracksRunningThroughDowntown Context triple: [La Grange, Kentucky, hasRailroadTracksRunningThroughDowntown, true]
-
A.
hasMajorRailCorridor
Indicates that a location or region is traversed by a primary, high-capacity railway route used for significant passenger or freight transport.
-
B.
hasRailSystem
Indicates that an entity possesses or is served by a rail-based transportation system.
-
C.
connectsDowntownTo
Indicates a relationship where one location, route, or service provides a direct connection or access to a downtown area.
-
D.
hasMajorRailLinksTo
Indicates that there are significant railway connections or routes between two locations.
-
E.
hasRackRailway
Indicates that one entity possesses or includes a rack railway system connecting locations or operating within its area.
- F. None of above. chosen
Provenance (4 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_69ab4a4bfec081908039988ec4c86e28 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abd5a33234819082ad49fa6594b6be |
completed | March 7, 2026, 7:37 a.m. |
| PD | Predicate disambiguation | batch_69abd0c8b6f08190a68645db3e8b779a |
completed | March 7, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69abd5a1cd508190a660b9a3c6b7cbcb |
completed | March 7, 2026, 7:37 a.m. |
Created at: March 6, 2026, 9:48 p.m.