Triple
T28107310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hockenheimring modern layout |
E710397
|
entity |
| Predicate | F1LapRecord_driver |
P30554
|
FINISHED |
| Object | Kimi Räikkönen |
—
|
NE NERFINISHED |
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: Kimi Räikkönen | Statement: [Hockenheimring modern layout, F1LapRecord_driver, Kimi Räikkönen]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: F1LapRecord_driver Context triple: [Hockenheimring modern layout, F1LapRecord_driver, Kimi Räikkönen]
-
A.
F1LapRecordCar
Indicates the car that holds the lap record in a Formula 1 session or at a specific F1 circuit.
-
B.
F1LapRecordHolder
chosen
Indicates that the subject holds the fastest lap record in a Formula 1 race or at a specific Formula 1 circuit.
-
C.
fastestLapDriver
Indicates the driver who recorded the quickest lap time in a given race or session.
-
D.
fastestLapDriverCountry
Indicates the country associated with the driver who recorded the fastest lap in a given race or session.
-
E.
totalFormulaOneFastestLaps
Indicates the total number of fastest laps a driver (or team) has recorded in Formula One races.
- 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_69ef9b71fdb081908b4a61cd7ff147c1 |
completed | April 27, 2026, 5:22 p.m. |
| NER | Named-entity recognition | batch_69f640c50dc88190a93952b9b87eb588 |
completed | May 2, 2026, 6:21 p.m. |
| PD | Predicate disambiguation | batch_69f63c6a8474819091b8c6fe98e3862d |
completed | May 2, 2026, 6:03 p.m. |
Created at: April 27, 2026, 9:09 p.m.