Triple
T16745161
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fiorenzo Magni |
E406932
|
entity |
| Predicate | knownForRidingWithInjury |
P3816
|
FINISHED |
| Object | broken collarbone in the 1956 Giro d'Italia |
—
|
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: broken collarbone in the 1956 Giro d'Italia | Statement: [Fiorenzo Magni, knownForRidingWithInjury, broken collarbone in the 1956 Giro d'Italia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: knownForRidingWithInjury Context triple: [Fiorenzo Magni, knownForRidingWithInjury, broken collarbone in the 1956 Giro d'Italia]
-
A.
notableRiderType
Indicates that an entity is notably associated with a particular type or category of rider (e.g., cyclist, jockey, driver).
-
B.
hasInjuries
chosen
Indicates that an entity has sustained one or more physical or bodily injuries.
-
C.
injuredIn
Indicates that an entity sustained an injury as a result of a specified event, situation, or action.
-
D.
ridingSpecialty
Indicates that one entity has a particular area of expertise or focus related to riding (e.g., a specific riding style, discipline, or type).
-
E.
hasNotableRide
Indicates that an entity is associated with a particularly remarkable or well-known ride or attraction.
- 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_69d8838ffb088190a0b11149929006bf |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3aa223aa88190a3c1805ece7317e2 |
completed | April 18, 2026, 3:58 p.m. |
| PD | Predicate disambiguation | batch_69e319c807788190901250ab6e0ca55f |
completed | April 18, 2026, 5:42 a.m. |
Created at: April 10, 2026, 5:21 a.m.