Triple
T21021318
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carolina Goldrusher |
E517813
|
entity |
| Predicate | hasTrainStyle |
P142501
|
FINISHED |
| Object | mine train–style trains |
—
|
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: mine train–style trains | Statement: [Carolina Goldrusher, hasTrainStyle, mine train–style trains]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTrainStyle Context triple: [Carolina Goldrusher, hasTrainStyle, mine train–style trains]
-
A.
hasRail
Indicates that something is equipped with, includes, or is connected to a rail or rail system.
-
B.
hasRailMode
Indicates that an entity is associated with or supports transportation via rail-based modes (such as trains, trams, or subways).
-
C.
hasStationStyle
Indicates that one entity (typically a station) possesses or is characterized by a particular architectural or design style.
-
D.
hasTailTrain
Indicates that one entity possesses or is characterized by a tail-like train extending from it.
-
E.
hasMTAStyle
Indicates that one entity possesses or is associated with a particular MTA (Mail Transfer Agent) configuration or style.
- 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_69e0b50262b081909bc488937145eb73 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fc5db0b88190ae61ea8b38e8ecf7 |
completed | April 21, 2026, 4:26 a.m. |
| PD | Predicate disambiguation | batch_69e5dbf274ac81909bbf245627dc8fdc |
completed | April 20, 2026, 7:55 a.m. |
| PDg | Predicate description generation | batch_69e5e2df1a888190b5b478e76bdf7fdf |
completed | April 20, 2026, 8:25 a.m. |
Created at: April 16, 2026, 1:54 p.m.