Triple
T3166572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kari Lehtonen |
E66224
|
entity |
| Predicate | hasSportNumber |
P15865
|
FINISHED |
| Object | 32 |
—
|
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: 32 | Statement: [Kari Lehtonen, hasSportNumber, 32]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSportNumber Context triple: [Kari Lehtonen, hasSportNumber, 32]
-
A.
jerseyNumber
Indicates the specific uniform number assigned to and worn by an individual, typically in a sports context.
-
B.
sportNumber
chosen
Indicates the specific jersey or uniform number associated with an athlete in a sporting context.
-
C.
hasSportsBody
Indicates that an entity possesses a body or physique that is characteristic of someone who regularly engages in sports or athletic activities.
-
D.
hasSportsStatus
Indicates that an entity holds a particular sports-related status, role, or classification (such as amateur, professional, active, or retired) within a sporting context.
-
E.
numberOfSports
Indicates the quantity of distinct sports associated with or involved in a given entity.
- 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_69ad8585d7988190af37365331093ccd |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada643e3e481908f4526d66e36e150 |
completed | March 8, 2026, 4:39 p.m. |
| PD | Predicate disambiguation | batch_69ad9dfe0a948190928f2201d671c654 |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:06 p.m.