Triple
T17321908
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Circuit of The Americas |
E420580
|
entity |
| Predicate | hasF1LapLengthKm |
P69216
|
FINISHED |
| Object | 5.513 |
—
|
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: 5.513 | Statement: [Circuit of The Americas, hasF1LapLengthKm, 5.513]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasF1LapLengthKm Context triple: [Circuit of The Americas, hasF1LapLengthKm, 5.513]
-
A.
F1LapRecordHolder
Indicates that the subject holds the fastest lap record in a Formula 1 race or at a specific Formula 1 circuit.
-
B.
trackLengthApproxKm
chosen
Indicates that one entity has an approximate track length, measured in kilometers, associated with it.
-
C.
fastestLapTime
Indicates the shortest recorded time an entity achieved to complete a single lap in a given context or event.
-
D.
F1LapRecordCar
Indicates the car that holds the lap record in a Formula 1 session or at a specific F1 circuit.
-
E.
lengthInKm
Indicates that one entity specifies the length or distance of another entity measured in kilometers.
- 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_69d889d22b848190a4663d0b8f8f76e7 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e439d01e2c8190a358dace420d4575 |
completed | April 19, 2026, 2:11 a.m. |
| PD | Predicate disambiguation | batch_69e3b01b9d1c8190a406dd941c9b11a1 |
completed | April 18, 2026, 4:23 p.m. |
Created at: April 10, 2026, 5:43 a.m.