Triple
T8078601
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shanghai Guoji Saichechang |
E188557
|
entity |
| Predicate | F1Status |
P80370
|
FINISHED |
| Object | Formula One World Championship circuit |
—
|
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: Formula One World Championship circuit | Statement: [Shanghai Guoji Saichechang, F1Status, Formula One World Championship circuit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: F1Status Context triple: [Shanghai Guoji Saichechang, F1Status, Formula One World Championship circuit]
-
A.
raceStatus
Indicates the current state or progress of an entity’s participation in a race or competitive event.
-
B.
footballStatus
Indicates the current state or condition of an entity in the context of football, such as its role, phase, or situation within the game or season.
-
C.
F1LapRecordCar
Indicates the car that holds the lap record in a Formula 1 session or at a specific F1 circuit.
-
D.
hasGrandPrixStatus
Indicates that an event or competition holds official Grand Prix classification or status.
-
E.
safetyCarPeriods
Indicates periods during an event when a safety car is deployed, affecting normal progression or conditions.
- 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_69ca82b50c708190863f661d438e68df |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb40a2b64c8190ae2b3414b4f840e4 |
completed | March 31, 2026, 3:33 a.m. |
| PD | Predicate disambiguation | batch_69cb049f1614819087360d1a4c6f0faa |
completed | March 30, 2026, 11:17 p.m. |
| PDg | Predicate description generation | batch_69cb14be17208190bb51c3dfcb613f20 |
completed | March 31, 2026, 12:26 a.m. |
Created at: March 30, 2026, 5:28 p.m.