Triple
T29815332
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | UCI velodrome standards |
E757087
|
entity |
| Predicate | minimumTrackLength |
P171063
|
FINISHED |
| Object | 133.333 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: 133.333 metres | Statement: [UCI velodrome standards, minimumTrackLength, 133.333 metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: minimumTrackLength Context triple: [UCI velodrome standards, minimumTrackLength, 133.333 metres]
-
A.
minimumTrackCount
Indicates the smallest number of tracks that must be present or satisfied in a given context or configuration.
-
B.
typicalTrackLengthRange
Indicates the usual minimum and maximum lengths that a track associated with something tends to fall between.
-
C.
longestTrack
Indicates that the related track is the one with the greatest duration or length among a given set of tracks.
-
D.
notableTrackLength
Indicates that there is a track whose duration is considered significant or noteworthy in the context of the subject.
-
E.
lapLengthOfTrack
Indicates the distance or length of a single lap around a track.
- F. None of above. chosen
Provenance (4 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_69f698ad83a08190a6834056ccc3e3a4 |
completed | May 3, 2026, 12:37 a.m. |
| PD | Predicate disambiguation | batch_69f69664142c8190bc695501056b0236 |
completed | May 3, 2026, 12:27 a.m. |
| PDg | Predicate description generation | batch_69f697e92e2c8190bed50d5ba0981b64 |
completed | May 3, 2026, 12:33 a.m. |
Created at: April 29, 2026, 5:26 p.m.