Triple
T3370157
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Critérium du Dauphiné Libéré 1965 |
E70933
|
entity |
| Predicate | pointsClassificationJerseyColor |
P47644
|
FINISHED |
| Object | green |
—
|
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: green | Statement: [Critérium du Dauphiné Libéré 1965, pointsClassificationJerseyColor, green]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: pointsClassificationJerseyColor Context triple: [Critérium du Dauphiné Libéré 1965, pointsClassificationJerseyColor, green]
-
A.
leaderJerseyColor
Indicates the color of the jersey worn by the current leader in a competition or ranking.
-
B.
jerseyNumber
Indicates the specific uniform number assigned to and worn by an individual, typically in a sports context.
-
C.
wearsJerseyFor
Indicates that one entity wears a jersey representing, belonging to, or in support of another entity (such as a team, organization, or individual).
-
D.
jerseyColorMountainsClassification
Indicates a classification relationship that assigns or associates a jersey color with a specific mountains-related category or ranking.
-
E.
jerseyColorPrimary
Indicates the main or dominant color used on an entity’s jersey or uniform.
- 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_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. |
| PDg | Predicate description generation | batch_69ada698eeb48190a1f5762fdd3b7b63 |
completed | March 8, 2026, 4:40 p.m. |
Created at: March 8, 2026, 3:13 p.m.