Triple
T4732448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Stanier |
E105041
|
entity |
| Predicate | appliedConcept |
P531
|
FINISHED |
| Object | standardisation of locomotive components on LMS |
—
|
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: standardisation of locomotive components on LMS | Statement: [William Stanier, appliedConcept, standardisation of locomotive components on LMS]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: appliedConcept Context triple: [William Stanier, appliedConcept, standardisation of locomotive components on LMS]
-
A.
appliedAs
Indicates that one entity submitted itself or was put forward for consideration in a particular role, position, or context relative to another entity.
-
B.
appliesTheory
Indicates that an entity uses or implements a particular theory in analyzing, explaining, or addressing something.
-
C.
hasConcept
chosen
Indicates that an entity includes, embodies, or is associated with a particular concept.
-
D.
introducedConcept
Indicates that one entity is responsible for presenting, defining, or bringing a new concept into use or awareness for another entity or context.
-
E.
appliedBy
Indicates that an action, process, or treatment is carried out or executed by a particular agent or entity.
- F. None of above.
Provenance (3 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_69bd43ee52048190b81a4f066534ffb3 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd67c9c3c08190a6c4944cdd1362a8 |
completed | March 20, 2026, 3:29 p.m. |
| PD | Predicate disambiguation | batch_69bd6220071881909670c89d072ffb6d |
completed | March 20, 2026, 3:05 p.m. |
Created at: March 20, 2026, 1:19 p.m.