Triple
T29815333
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UCI velodrome standards |
E757087
|
entity |
| Predicate | maximumTrackLength |
P23293
|
FINISHED |
| Object | 500 metres |
—
|
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: 500 metres | Statement: [UCI velodrome standards, maximumTrackLength, 500 metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maximumTrackLength Context triple: [UCI velodrome standards, maximumTrackLength, 500 metres]
-
A.
longestTrack
Indicates that the related track is the one with the greatest duration or length among a given set of tracks.
-
B.
minimumTrackLength
Indicates the smallest allowable or observed length that a track must have within a given context or system.
-
C.
maximumRecordedLength
chosen
Indicates the greatest length value that has been observed and recorded for the entity in question.
-
D.
lapLengthOfTrack
Indicates the distance or length of a single lap around a track.
-
E.
minimumTrackCount
Indicates the smallest number of tracks that must be present or satisfied in a given context or configuration.
- 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_69f2245701c88190ad42415a0956c4ed |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f74c70fd248190a9d5543afcb08211 |
completed | May 3, 2026, 1:24 p.m. |
| PD | Predicate disambiguation | batch_69f7478e3b548190a51d5d436e2bb036 |
completed | May 3, 2026, 1:03 p.m. |
Created at: April 29, 2026, 5:26 p.m.