Triple
T1666098
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shanghai International Circuit |
E36014
|
entity |
| Predicate | F1LapRecordCategory |
P30557
|
FINISHED |
| Object | race lap record |
—
|
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: race lap record | Statement: [Shanghai International Circuit, F1LapRecordCategory, race lap record]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: F1LapRecordCategory Context triple: [Shanghai International Circuit, F1LapRecordCategory, race lap record]
-
A.
raceCategory
Indicates the classification of an entity into a specific race or racial group within a defined categorization system.
-
B.
speedClass
Indicates the categorical speed level or range assigned to an entity based on how fast it moves or operates.
-
C.
hasFrequencyCategory
Indicates that something is associated with a particular classification of how often it occurs or is used.
-
D.
safetyCarFrequency
Indicates how often a safety car is deployed or appears within a given context or time frame.
-
E.
lapsAtAlbertPark
Indicates that an entity completes or records laps at the Albert Park circuit.
- 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_69a8861286808190939afff3ce8ee31e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69aa994f92b0819084ee2f6a672334b9 |
completed | March 6, 2026, 9:07 a.m. |
| PD | Predicate disambiguation | batch_69a907d2475c8190b7ec7dccd3335eb1 |
completed | March 5, 2026, 4:34 a.m. |
| PDg | Predicate description generation | batch_69a94192abc0819092fc00fef9d53bcb |
completed | March 5, 2026, 8:40 a.m. |
Created at: March 4, 2026, 7:29 p.m.