Triple
T29953595
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LNER Class V2 |
E760834
|
entity |
| Predicate | brNumberOfGreenArrow |
P3339
|
FINISHED |
| Object | 60800 |
—
|
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: 60800 | Statement: [LNER Class V2, brNumberOfGreenArrow, 60800]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: brNumberOfGreenArrow Context triple: [LNER Class V2, brNumberOfGreenArrow, 60800]
-
A.
arrowCount
chosen
Indicates the number of arrows associated with or involved in a given entity or interaction.
-
B.
hasGreenRuns
Indicates that an entity possesses or includes ski runs that are classified as green (i.e., beginner-level).
-
C.
numberOfDoubleGreens
Indicates the count of instances where two green-related elements or conditions occur together within a given context.
-
D.
green500Rank
Indicates the position or ranking of an entity on the Green500 list, which orders systems by energy-efficient performance.
-
E.
hasNumberOfDirections
Indicates that an entity is associated with a specific count of possible directions or orientations.
- 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_69f2246562b881909d57622f4086d43d |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f6783880608190906379178b865dc0 |
completed | May 2, 2026, 10:18 p.m. |
| PD | Predicate disambiguation | batch_69f673c4abec8190bc2379e66f4af0a9 |
completed | May 2, 2026, 9:59 p.m. |
Created at: April 29, 2026, 6:26 p.m.