Triple
T15395172
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Richie Porte |
E368158
|
entity |
| Predicate | woreClassificationJersey |
P118622
|
FINISHED |
| Object | maglia rosa at Giro d'Italia 2010 |
—
|
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: maglia rosa at Giro d'Italia 2010 | Statement: [Richie Porte, woreClassificationJersey, maglia rosa at Giro d'Italia 2010]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: woreClassificationJersey Context triple: [Richie Porte, woreClassificationJersey, maglia rosa at Giro d'Italia 2010]
-
A.
pointsClassificationJerseyColor
Indicates the color of the jersey awarded to the leader of the points classification in a race or competition.
-
B.
wearsJerseyFor
Indicates that one entity wears a jersey representing, belonging to, or in support of another entity (such as a team, organization, or individual).
-
C.
jerseyNumber
Indicates the specific uniform number assigned to and worn by an individual, typically in a sports context.
-
D.
leaderJerseyColor
Indicates the color of the jersey worn by the current leader in a competition or ranking.
-
E.
hasJerseyColor
Indicates that an entity’s jersey possesses or is characterized by a specific color.
- 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_69d85a16c68c819099c1b547fbc87b32 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e8ac79081908ac79c0b3e7587ff |
completed | April 16, 2026, 1:42 a.m. |
| PD | Predicate disambiguation | batch_69ded27b8cac8190bfa77698d53c5d1c |
completed | April 14, 2026, 11:49 p.m. |
| PDg | Predicate description generation | batch_69ded57005608190886cd01f640dfedb |
completed | April 15, 2026, 12:01 a.m. |
Created at: April 10, 2026, 3:19 a.m.