Triple
T14656753
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Red, Black & Green |
E344129
|
entity |
| Predicate | typicalTrackLength |
P77352
|
FINISHED |
| Object | medium length tracks |
—
|
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: medium length tracks | Statement: [Red, Black & Green, typicalTrackLength, medium length tracks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalTrackLength Context triple: [Red, Black & Green, typicalTrackLength, medium length tracks]
-
A.
typicalTrackLengthRange
Indicates the usual minimum and maximum lengths that a track associated with something tends to fall between.
-
B.
typicalLength
Indicates the usual or characteristic length associated with an entity or phenomenon.
-
C.
trackLengthApproxKm
Indicates that one entity has an approximate track length, measured in kilometers, associated with it.
-
D.
trailLengthApprox
Indicates an approximate measurement of the total length of a trail.
-
E.
trailLengthCategory
chosen
Indicates the classification of a trail based on its total length (e.g., short, medium, long).
- 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_69d822e1a2cc81908e5bb93cf61ce3cc |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb51a562c819098971447db4b29f7 |
completed | April 14, 2026, 9:43 p.m. |
| PD | Predicate disambiguation | batch_69de6576f0208190aa94d995e797ac38 |
completed | April 14, 2026, 4:04 p.m. |
Created at: April 10, 2026, 1:27 a.m.