Triple
T1298703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monroe, North Carolina |
E27711
|
entity |
| Predicate | hasRailroadHistory |
P28401
|
FINISHED |
| Object | Yes |
—
|
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: Yes | Statement: [Monroe, North Carolina, hasRailroadHistory, Yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRailroadHistory Context triple: [Monroe, North Carolina, hasRailroadHistory, Yes]
-
A.
hasRailroadHistoryWith
Indicates a historical relationship or connection between entities involving railroads, such as shared development, operation, or significant events in railway history.
-
B.
hasRailSystem
Indicates that an entity possesses or is served by a rail-based transportation system.
-
C.
followsFormerRailroad
Indicates that something is aligned with or traces the route of a former railroad line.
-
D.
servedByRailroad
Indicates that a location or facility is provided with transportation or service by a railroad line or company.
-
E.
railroad
Indicates that one entity constructs, operates, or provides railroad or train transportation services for another entity or 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_69a496d6682881909ba658f1c1e0e2b0 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c3bb3a9c81909db2ad91defd87b6 |
completed | March 1, 2026, 10:54 p.m. |
| PD | Predicate disambiguation | batch_69a4bee64d908190b6a9bb479959d523 |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4c3b9ebdc819098de4d3288201bc1 |
completed | March 1, 2026, 10:54 p.m. |
Created at: March 1, 2026, 7:51 p.m.