Triple
T5755215
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | NASCAR Mexico Series |
E126948
|
entity |
| Predicate | typicalRaceDistanceUnit |
P66191
|
FINISHED |
| Object | kilometres |
—
|
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: kilometres | Statement: [NASCAR Mexico Series, typicalRaceDistanceUnit, kilometres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalRaceDistanceUnit Context triple: [NASCAR Mexico Series, typicalRaceDistanceUnit, kilometres]
-
A.
raceDistanceType
Indicates the specific type or category of distance over which a race is conducted.
-
B.
majorRaceDistance
Indicates the standard or primary distance over which a major race or competition is contested.
-
C.
typicalRaceDistanceLaps
Indicates the usual number of laps that constitute the standard race distance for a given racing event or category.
-
D.
range_km
Indicates the maximum distance, measured in kilometers, over which something can operate, travel, or be effective.
-
E.
hourRecordDistanceKm
Indicates the distance in kilometers that was covered or recorded within a one-hour period.
- 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_69c00832aedc81909899801b141fa3b4 |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c02904dcf481909e4340a64ee1034e |
completed | March 22, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69c021cc68648190bb86d049ebe80f12 |
completed | March 22, 2026, 5:07 p.m. |
| PDg | Predicate description generation | batch_69c028fec2bc819083f5dca6a8d9d435 |
completed | March 22, 2026, 5:38 p.m. |
Created at: March 22, 2026, 3:49 p.m.