Triple
T10982493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kelly McCormick |
E259542
|
entity |
| Predicate | roleInBulletTrain |
P96448
|
FINISHED |
| Object | producer |
—
|
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: producer | Statement: [Kelly McCormick, roleInBulletTrain, producer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInBulletTrain Context triple: [Kelly McCormick, roleInBulletTrain, producer]
-
A.
roleInTrain
Indicates the specific function or position an entity holds within the context of a train (e.g., passenger, conductor, locomotive, or car type).
-
B.
roleInWhiteBuses
Indicates that an entity participated in or held a specific role within the White Buses operation.
-
C.
usesTrainNumber
Indicates that one entity operates, identifies, or references another entity by a specific train number.
-
D.
transportationRole
Indicates a role or function that an entity has specifically in the context of providing, operating, or supporting transportation.
-
E.
comfortLevelComparedToConventionalTrains
Indicates how the comfort level of something compares relative to that of conventional trains.
- 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_69d6aa895f4c8190887a15460ef622f4 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d772eb518c8190a885a417815f2ff6 |
completed | April 9, 2026, 9:35 a.m. |
| PD | Predicate disambiguation | batch_69d72e9055908190b438f039574aaaaf |
completed | April 9, 2026, 4:44 a.m. |
| PDg | Predicate description generation | batch_69d732242fdc8190be77d1f730a42935 |
completed | April 9, 2026, 4:59 a.m. |
Created at: April 8, 2026, 9:24 p.m.