Triple
T3370154
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Critérium du Dauphiné Libéré 1965 |
E70933
|
entity |
| Predicate | mountainsClassificationJerseyColor |
P16775
|
FINISHED |
| Object | white dots on red |
—
|
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: white dots on red | Statement: [Critérium du Dauphiné Libéré 1965, mountainsClassificationJerseyColor, white dots on red]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: mountainsClassificationJerseyColor Context triple: [Critérium du Dauphiné Libéré 1965, mountainsClassificationJerseyColor, white dots on red]
-
A.
mountainsJerseyColor
chosen
Indicates the color of the jersey worn by the leader of the mountains classification (best climber) in a cycling race.
-
B.
jerseyColorMountainsClassification
Indicates a classification relationship that assigns or associates a jersey color with a specific mountains-related category or ranking.
-
C.
jerseyColorGeneralClassification
Indicates the color of the jersey worn by the leader of the general classification in a race or competition.
-
D.
hillColor
Indicates the color attribute associated with a hill.
-
E.
mountainType
Indicates the specific classification or category of a mountain based on its geological or physical characteristics.
- 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_69ad85a729d48190afd789cd8417f289 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb2b9b1fc8190a2cbf040ea808baf |
completed | March 8, 2026, 5:32 p.m. |
| PD | Predicate disambiguation | batch_69ada4317e288190ab7d0f66e9dba65f |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:13 p.m.