Triple
T38429445
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | D400–D449 |
E903757
|
entity |
| Predicate | appliedToNumberOfLocomotives |
P201176
|
FINISHED |
| Object | 50 |
—
|
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: 50 | Statement: [D400–D449, appliedToNumberOfLocomotives, 50]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliedToNumberOfLocomotives Context triple: [D400–D449, appliedToNumberOfLocomotives, 50]
-
A.
typeOfLocomotivesProduced
Indicates the specific kinds or categories of locomotives that are manufactured or produced by an entity.
-
B.
usedLocomotives
Indicates that an entity employed locomotives as tools, resources, or means to perform an action or fulfill a function.
-
C.
hasLocomotive
Indicates that one entity possesses or is equipped with a locomotive as part of its composition or operation.
-
D.
associatedWithLocomotive
Indicates a relationship where something is connected or related to a locomotive, such as by use, function, origin, or context.
-
E.
locomotiveNumber
Indicates the identifying number assigned to a locomotive in the relationship.
- 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_69f76e6a2024819081aa04f4932f89d2 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69ffcf46fd688190907fd1ceb499a8d1 |
completed | May 10, 2026, 12:20 a.m. |
| PD | Predicate disambiguation | batch_69ffccde2a8c81908e055e74077dbd19 |
completed | May 10, 2026, 12:10 a.m. |
| PDg | Predicate description generation | batch_69ffcf4631cc8190aefa4b8f0b940b89 |
completed | May 10, 2026, 12:20 a.m. |
Created at: May 3, 2026, 4:31 p.m.