Triple
T1622076
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Rail Enquiries |
E35053
|
entity |
| Predicate | dataCovers |
P13946
|
FINISHED |
| Object | train operating companies in Great Britain |
—
|
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: train operating companies in Great Britain | Statement: [National Rail Enquiries, dataCovers, train operating companies in Great Britain]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dataCovers Context triple: [National Rail Enquiries, dataCovers, train operating companies in Great Britain]
-
A.
mapCoverage
Indicates the extent or area that is represented, covered, or included by a particular map.
-
B.
eraCovered
Indicates that one entity temporally encompasses, includes, or spans the historical period or era associated with another entity.
-
C.
alsoCovers
chosen
Indicates that something extends its scope or applicability to include an additional subject, area, or case beyond what was originally covered.
-
D.
regionCoverage
Indicates that one entity geographically spans, includes, or serves the area defined by another entity.
-
E.
notableCover
Indicates that one entity is a particularly well-known or significant cover version or adaptation of another 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_69a886023194819080a3fccd6e325d0e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aaf4a0ef748190ae52b9656474c0ef |
completed | March 6, 2026, 3:37 p.m. |
| PD | Predicate disambiguation | batch_69a907c731808190a1d998155041b3c1 |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:28 p.m.